WordNet-Similarity-2.05/0000755000175000017500000000000011025552366013273 5ustar sidsidWordNet-Similarity-2.05/t/0000755000175000017500000000000011025552366013536 5ustar sidsidWordNet-Similarity-2.05/t/vector_pairs.t0000755000175000017500000000422711020101703016407 0ustar sidsid#! /usr/bin/perl -w # Before `make install' is performed this script should be runnable with # `make test'. After `make install' it should work as `perl t/vector_pairs.t' ##################### We start with some black magic to print on failure. BEGIN { $| = 1; print "1..6\n"; } END {print "not ok 1\n" unless $loaded;} use WordNet::Similarity; use WordNet::QueryData; use WordNet::Similarity::vector_pairs; $loaded = 1; print "ok 1\n"; ######################### End of black magic. # Insert your test code below (better if it prints "ok 13" # (correspondingly "not ok 13") depending on the success of chunk 13 # of the test code): ############ Load QueryData $wn = WordNet::QueryData->new(); if($wn) { print "ok 2\n"; } else { print "not ok 2\n"; } ############ Load vector $vector = WordNet::Similarity::vector_pairs->new($wn); if($vector) { ($err, $errString) = $vector->getError(); if($err) { print "not ok 3\n"; } else { print "ok 3\n"; } } else { print "not ok 3\n"; } ############ Load vector with undef QueryData. $badVector = WordNet::Similarity::vector_pairs->new(undef); if($badVector) { ($err, $errString) = $badVector->getError(); if($err < 2) { print "not ok 4\n"; } elsif($err == 2) { if($errString =~ /A WordNet::QueryData object is required/) { print "ok 4\n"; } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } ############ getRelatedness of badly formed synset. ## (Tried getRelatedness of unknown synsets... "hjxlq#n#1", "pynbr#n#2"... ## QueryData complains... cannot trap that error myself.) if(defined $vector->getRelatedness("hjxlq#n", "pynbr#n")) { print "not ok 5\n"; } else { ($err, $errString) = $vector->getError(); if($err == 1) { print "ok 5\n"; } else { print "not ok 5\n"; } } ########### getRelatedness across parts of speech $value = $vector->getRelatedness("pay#v#1", "money#n#1"); if(defined $value && $value =~ /[\.0-9]+/) { if($value > 0) { print "ok 6\n"; } else { print "not ok 6\n"; } } else { print "not ok 6\n"; } WordNet-Similarity-2.05/t/pod-coverage.t0000755000175000017500000000051111020101543016254 0ustar sidsid#! /usr/bin/perl -w # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/pod-coverage.t' use Test::More; eval "use Test::Pod::Coverage 1.00"; plan skip_all => "Test::Pod::Coverage 1.00 required for testing POD coverage" if $@; all_pod_coverage_ok(); WordNet-Similarity-2.05/t/ICFinder.t0000755000175000017500000001223711020101153015331 0ustar sidsid#! /usr/bin/perl -w # ICFinder.t version 2.01 # (Updated 2/12/2004 -- Jason) # # Copyright (c) 2005 # # Jason Michelizzi, University of Minnesota Duluth # mich0212 at d.umn.edu # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/ICFinder.t' # A script to test the WordNet::Similarity::Infocontent module. # Specifically, for a given set of words, it checks that the probability # and infocontent values computed by Infocontent.pm are reasonably close # to pre-computed values. use strict; use warnings; use Test::More tests => 21; # Tests 1, 2 BEGIN {use_ok 'WordNet::QueryData'} BEGIN {use_ok 'WordNet::Similarity::ICFinder'} # Test 3 my $wn = WordNet::QueryData->new; ok ($wn) or diag "Failed to construct WordNet::QueryData object"; # get WordNet version (not the version of WordNet::QueryData) my $wnver = '0.0'; $wnver = $wn->version () if($wn->can('version')); my $module = WordNet::Similarity::ICFinder->new ($wn); # Tests 4, 5 ok ($module) or diag "Failed to construct WordNet::Similarity::ICFinder object"; my ($err, $errstr) = $module->getError (); is ($err, 0) or diag "$errstr"; # we are using the default infocontent file, ic-semtag.dat, here: i.e., # statistics extracted from WordNet (which used SemCor tagged text). # some variable we'll need repeatedly ' my $offset; my $prob; my $ic; SKIP: { skip "Skipping tests for WordNet v2.1, v2.0 and v1.7.1", 12 unless ($wnver eq '2.1' || $wnver eq '2.0' || $wnver eq '1.7.1'); # Tests 6, 7, 8, 9 # find probability and ic of entity#n#1 if ($wnver eq '2.1') { $offset = 0 + '00001740'; $prob = $module->probability ($offset, 'n'); cmp_ok($prob, "<=", 1.00001); cmp_ok($prob, ">=", 0.99999); $ic = $module->IC ($offset, 'n'); cmp_ok($ic, "<=", 0.00001); cmp_ok($ic, ">=", -0.00001); } elsif ($wnver eq '2.0') { $offset = 0 + '00001740'; $prob = $module->probability ($offset, 'n'); # should be around .425088 cmp_ok ($prob, '<=', .42509); cmp_ok ($prob, '>=', .42507); $ic = $module->IC ($offset, 'n'); # should be around .855459 cmp_ok ($ic, '<=', .85546); cmp_ok ($ic, '>=', .85544); } elsif ($wnver eq '1.7.1') { $offset = 0 + '00001742'; $prob = $module->probability ($offset, 'n'); # should be around .43663 cmp_ok ($prob, '<=', .43664); cmp_ok ($prob, '>=', .43663); $ic = $module->IC ($offset, 'n'); # should be around .82866 cmp_ok ($ic, '<=', .82867); cmp_ok ($ic, '>=', .82865); } # Tests 10, 11, 12, 13 # find probability and ic of breathe#v#1 if ($wnver eq '2.1') { $offset = 0 + '00001740'; $prob = $module->probability ($offset, 'v'); cmp_ok($prob, "<=", 0.00090766); cmp_ok($prob, ">=", 0.00090764); $ic = $module->IC ($offset, 'v'); cmp_ok($ic, "<=", 7.0047); cmp_ok($ic, ">=", 7.0045); } elsif ($wnver eq '2.0') { $offset = 0 + '00001740'; $prob = $module->probability ($offset, 'v'); # should be around .000907285 cmp_ok ($prob, '<=', .00090729); cmp_ok ($prob, '>=', .00090727); $ic = $module->IC ($offset, 'v'); # should be around 7.00505 cmp_ok ($ic, '<=', 7.0051); cmp_ok ($ic, '>=', 7.0050); } elsif ($wnver eq '1.7.1') { $offset = 0 + '00001742'; $prob = $module->probability ($offset, 'v'); # should be around .00090283 cmp_ok ($prob, '<=', .00090284); cmp_ok ($prob, '>=', .00090282); $ic = $module->IC ($offset, 'v'); # should be around 7.0100 cmp_ok ($ic, '<=', 7.0101); cmp_ok ($ic, '>=', 7.0099); } # Tests 14, 15, 16, 17 # find probability and ic of rest#v#7 if($wnver eq '2.1') { $offset = 0 + '02639123'; $prob = $module->probability ($offset, 'v'); cmp_ok($prob, "<=", 0.00000818); cmp_ok($prob, ">=", 0.00000816); $ic = $module->IC ($offset, 'v'); cmp_ok($ic, "<=", 11.72); cmp_ok($ic, ">=", 11.70); } elsif ($wnver eq '2.0') { $offset = 0 + '02584611'; $prob = $module->probability ($offset, 'v'); # should be around 0.00000817374 cmp_ok ($prob, '<=', 0.00000818); cmp_ok ($prob, '>=', 0.00000816); $ic = $module->IC ($offset, 'v'); # should be around 11.7146 cmp_ok ($ic, '<=', 11.72); cmp_ok ($ic, '>=', 11.70); } elsif ($wnver eq '1.7.1') { $offset = 0 + '02097157'; $prob = $module->probability ($offset, 'v'); # should be around .0000081336 cmp_ok ($prob, '<=', .0000081337); cmp_ok ($prob, '>=', .0000081335); $ic = $module->IC ($offset, 'v'); # should be around 11.720 cmp_ok ($ic, '<=', 11.721); cmp_ok ($ic, '>=', 11.719); } } # find probability and ic of root nodes (this should be the same regardless # of the WordNet version) $offset = 0; # the offset of the root is always 0 # Tests 18, 19 # check noun root node $prob = $module->probability ($offset, 'n'); cmp_ok ($prob, '==', 1); $ic = $module->IC ($offset, 'n'); cmp_ok ($ic, '==', 0); # Tests 20, 21 # check verb root node $prob = $module->probability ($offset, 'v'); cmp_ok ($prob, '==', 1); $ic = $module->IC ($offset, 'v'); cmp_ok ($ic, '==', 0); WordNet-Similarity-2.05/t/config/0000755000175000017500000000000011025552366015003 5ustar sidsidWordNet-Similarity-2.05/t/config/rootnode.t0000755000175000017500000000670111020102123017001 0ustar sidsid#! /usr/bin/perl -w # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/config/rootnode.t' # A script to run tests on the handling of root nodes by the # res, lin, jcn, path, lch, and wup measures. The following cases are # tested: # # 1) the relatedness of a noun and a verb # 2) the relatedness of the roots of two taxonomies of the same part of speech # 3) the relatedness words in the same taxonomy (and same part of speech) use strict; use warnings; my $numtests; my @measures; BEGIN { @measures = qw/res lin jcn path lch wup/; $numtests = 10 + 14 * scalar (@measures); } use Test::More tests => $numtests; BEGIN {use_ok ('WordNet::QueryData')} BEGIN {use_ok ('WordNet::Similarity::PathFinder')} BEGIN {use_ok ('WordNet::Similarity::res')} BEGIN {use_ok ('WordNet::Similarity::lin')} BEGIN {use_ok ('WordNet::Similarity::jcn')} BEGIN {use_ok ('WordNet::Similarity::path')} BEGIN {use_ok ('WordNet::Similarity::lch')} BEGIN {use_ok ('WordNet::Similarity::wup')} my $wn = new WordNet::QueryData; ok ($wn); my $wnver = '0.0'; $wnver = $wn->version() if($wn->can('version')); ok ($wnver); # array of temporary file names my @tempfiles; # make sure that the files get deleted even if the # script dies early END { unlink @tempfiles } foreach my $measure (@measures) { my $config = "root${measure}.txt"; push @tempfiles, $config; ok (open FH, ">$config") or diag "Cannot open $config for writing: $!"; print FH "WordNet::Similarity::$measure\n"; print FH "rootNode::0\n"; ok (close FH); my $module = "WordNet::Similarity::$measure"->new ($wn, $config); ok ($module); my ($err, $errString) = $module->getError(); is ($err, 0) or diag "$errString"; # now do some tests # dog#n#1 and cat#n#1 should always be related (relatedness > 0) my $score = $module->getRelatedness ('dog#n#1', 'cat#n#1'); is (($module->getError())[0], 0); ok ($score > 0) or diag "Bad relatedness using $measure"; # dog#n#1 and bark#v#4 should have undefined relatedness for these # measures $score = $module->getRelatedness ('dog#n#1', 'bark#v#4'); is (($module->getError())[0], 1); ok ($score < 0); # entity#n#1 and event#n#1 are the root nodes of different noun taxonomies # When the root node is "turned off", they will have undefined relatedness $score = $module->getRelatedness ('entity#n#1', 'event#n#1'); SKIP: { skip("Skipping tests for WordNet v2.1, v2.0 and v1.7.1", 4) unless($wnver eq '2.1' || $wnver eq '2.0' || $wnver eq '1.7.1'); if($wnver eq '2.0' || $wnver eq '1.7.1') { is (($module->getError())[0], 1); ok ($score < 0); } if($wnver eq '2.1') { is (($module->getError())[0], 0); ok ($score >= 0); } if($wnver eq '2.0' || $wnver eq '1.7.1') { # carry#v#30 is a troponym (hyponym) of grow#v#7 => they should always # have a non-negative relatedness $score = $module->getRelatedness ('grow#v#7', 'carry#v#30'); } if($wnver eq '2.1') { # carry#v#31 is a troponym (hyponym) of grow#v#7 => they should always # have a non-negative relatedness $score = $module->getRelatedness ('grow#v#7', 'carry#v#31'); } is (($module->getError())[0], 0); ok ($score >= 0); } # recognize#v#3 and cultivate#v#1 are roots of different verb taxonomies $score = $module->getRelatedness ('recognize#v#3', 'cultivate#v#1'); is (($module->getError())[0], 1); ok ($score < 0); } WordNet-Similarity-2.05/t/config/all.t0000755000175000017500000000726511020104752015740 0ustar sidsid#! /usr/bin/perl -w # all.t version 2.05 # ($Id: all.t,v 1.10 2008/05/30 23:12:42 sidz1979 Exp $) # # Copyright (C) 2004 # # Jason Michelizzi, University of Minnesota Duluth # mich0212 at d.umn.edu # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/config/all.t' # A script to run general tests of the ability of various WordNet::Similarity # modules to read config files. This script simply tries to contruct each # measure while specifying a configuration file. use strict; use warnings; use Test::More tests => 40; BEGIN {use_ok ('File::Spec')} BEGIN {use_ok ('WordNet::QueryData')} BEGIN {use_ok ('WordNet::Similarity::hso')} BEGIN {use_ok ('WordNet::Similarity::jcn')} BEGIN {use_ok ('WordNet::Similarity::lch')} BEGIN {use_ok ('WordNet::Similarity::lesk')} BEGIN {use_ok ('WordNet::Similarity::lin')} BEGIN {use_ok ('WordNet::Similarity::path')} BEGIN {use_ok ('WordNet::Similarity::random')} BEGIN {use_ok ('WordNet::Similarity::res')} #BEGIN {use_ok ('WordNet::Similarity::vector')} BEGIN {use_ok ('WordNet::Similarity::wup')} my $wn = WordNet::QueryData->new; ok ($wn); my $fs = bless [], "File::Spec"; # find the sample config files that should come with this distribution my $dir = $fs->catfile ('samples', 'config-files'); my $hsoconf = $fs->catfile ($dir, 'config-hso.conf'); my $jcnconf = $fs->catfile ($dir, 'config-jcn.conf'); my $lchconf = $fs->catfile ($dir, 'config-lch.conf'); my $leskconf = $fs->catfile ($dir, 'config-lesk.conf'); my $linconf = $fs->catfile ($dir, 'config-lin.conf'); my $pathconf = $fs->catfile ($dir, 'config-path.conf'); my $randomconf = $fs->catfile ($dir, 'config-random.conf'); my $resconf = $fs->catfile ($dir, 'config-res.conf'); #my $vectorconf = $fs->catfile ($dir, 'config-vector.conf'); my $wupconf = $fs->catfile ($dir, 'config-wup.conf'); # check to make sure the config files exist ok (-e $dir) or diag "Can't find config files: be sure to run this script from the root of the directory tree"; ok (-e $hsoconf); ok (-e $jcnconf); ok (-e $lchconf); ok (-e $leskconf); ok (-e $linconf); ok (-e $pathconf); ok (-e $randomconf); ok (-e $resconf); #ok (-e $vectorconf); ok (-e $wupconf); # just try to create the modules using the config files my $hso = WordNet::Similarity::hso->new ($wn, $hsoconf); ok ($hso); my ($err, $errstr) = $hso->getError (); is ($err, 0) or diag "$errstr\n"; my $jcn = WordNet::Similarity::jcn->new ($wn, $jcnconf); ok ($jcn); ($err, $errstr) = $jcn->getError (); is ($err, 0) or diag "$errstr\n"; my $lch = WordNet::Similarity::lch->new ($wn, $lchconf); ok ($lch); ($err, $errstr) = $lch->getError (); is ($err, 0) or diag "$errstr\n"; my $lesk = WordNet::Similarity::lesk->new ($wn, $leskconf); ok ($lesk); ($err, $errstr) = $lesk->getError (); is ($err, 0) or diag "$errstr\n"; my $lin = WordNet::Similarity::lin->new ($wn, $linconf); ok ($lin); ($err, $errstr) = $lin->getError (); is ($err, 0) or diag "$errstr\n"; my $path = WordNet::Similarity::path->new ($wn, $pathconf); ok ($path); ($err, $errstr) = $path->getError (); is ($err, 0) or diag "$errstr\n"; my $random = WordNet::Similarity::random->new ($wn, $randomconf); ok ($random); ($err, $errstr) = $random->getError (); is ($err, 0) or diag "$errstr\n"; my $res = WordNet::Similarity::res->new ($wn, $resconf); ok ($res); ($err, $errstr) = $res->getError (); is ($err, 0) or diag "$errstr\n"; #my $vector = WordNet::Similarity::vector->new ($wn, $vectorconf); #ok ($vector); #is (($vector->getError())[0], 0) my $wup = WordNet::Similarity::wup->new ($wn, $wupconf); ok ($wup); ($err, $errstr) = $res->getError (); is ($err, 0) or diag "$errstr\n"; WordNet-Similarity-2.05/t/config/invalid.t0000755000175000017500000000714411020102055016604 0ustar sidsid#! /usr/bin/perl -w # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/config/invalid.t' # A script that ensures that errors/warnings are raised when the modules # are given invalid config files. The following tests are run: # # 1) a config file contains an invalid value for an option # 2) a config file contains the same option twice # 3) a config file contains an unknown/nonsensical option use strict; use warnings; my @measures; my $num_tests; BEGIN { @measures = qw/res lin jcn path lch wup hso lesk/; #vector $num_tests = 12 + 24 * scalar (@measures); } use Test::More tests => $num_tests; BEGIN {use_ok ('WordNet::QueryData')} BEGIN {use_ok ('WordNet::Similarity::res')} BEGIN {use_ok ('WordNet::Similarity::lin')} BEGIN {use_ok ('WordNet::Similarity::jcn')} BEGIN {use_ok ('WordNet::Similarity::path')} BEGIN {use_ok ('WordNet::Similarity::lch')} BEGIN {use_ok ('WordNet::Similarity::wup')} BEGIN {use_ok ('WordNet::Similarity::hso')} BEGIN {use_ok ('WordNet::Similarity::lesk')} BEGIN {use_ok ('WordNet::Similarity::random')} #BEGIN {use_ok ('WordNet::Similarity::vector')} my $wn = WordNet::QueryData->new; ok ($wn); my $config = "tempconf$$.txt"; # first batch of invalid files: bad trace level foreach my $measure (@measures) { ok (open FH, ">$config") or diag "Could not create temporary file: $!"; print FH "WordNet::Similarity::$measure\n"; print FH "trace::11\n"; ok (close FH); my $module = "WordNet::Similarity::$measure"->new ($wn, $config); ok ($module); my ($err, $errstr) = $module->getError (); cmp_ok ($err, '>', 0); } # try using an unknown and nonsensical option foreach my $measure (@measures) { ok (open FH, ">$config") or diag "Could not create temporary file: $!"; print FH "WordNet::Similarity::$measure\n"; print FH "adfjkl::1\n"; ok (close FH); my $module = "WordNet::Similarity::$measure"->new ($wn, $config); ok ($module); my ($err, $errstr) = $module->getError (); cmp_ok ($err, '>', 0); } # try using an invalid value for the cache:: option foreach my $measure (@measures) { ok (open FH, ">$config") or diag "Could not create temporary file: $!"; print FH "WordNet::Similarity::$measure\n"; print FH "cache::2\n"; ok (close FH); my $module = "WordNet::Similarity::$measure"->new ($wn, $config); ok ($module); my ($err, $errstr) = $module->getError (); cmp_ok ($err, '>', 0); } # try using an invalid value for the maxCacheSize option foreach my $measure (@measures) { ok (open FH, ">$config") or diag "Could not create temporary file: $!"; print FH "WordNet::Similarity::$measure\n"; print FH "maxCacheSize::-1\n"; ok (close FH); my $module = "WordNet::Similarity::$measure"->new ($wn, $config); ok ($module); my ($err, $errstr) = $module->getError (); cmp_ok ($err, '>', 0); } # try an invalid value for the trace option foreach my $measure (@measures) { ok (open FH, ">$config") or diag "Could not create temporary file: $!"; print FH "WordNet::Similarity::$measure\n"; print FH "trace::nothanks\n"; ok (close FH); my $module = "WordNet::Similarity::$measure"->new ($wn, $config); ok ($module); my ($err, $errstr) = $module->getError (); cmp_ok ($err, '>', 0); } foreach my $measure (@measures) { ok (open FH, ">$config") or diag "Could not create temporary file: $!"; print FH "WordNet::Similarity::$measure\n"; print FH "trace::0\ntrace::1\n"; ok (close FH); my $module = "WordNet::Similarity::$measure"->new ($wn, $config); ok ($module); my ($err, $errstr) = $module->getError (); cmp_ok ($err, '>', 0); } END { ok (unlink $config); } WordNet-Similarity-2.05/t/config/random.t0000755000175000017500000000250011020102106016422 0ustar sidsid#! /usr/bin/perl -w # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/config/random.t' # A script to test configuration options specific to the # WordNet::Similarity::random module; specifically, it tests # whether the maxrand option is accepted and whether all # the relatedness values, $score, satisfy: 0 <= $score < maxrand. use strict; use warnings; my $num_tests; BEGIN { $num_tests = 208; } use Test::More tests => $num_tests; BEGIN {use_ok ('WordNet::QueryData')} BEGIN {use_ok ('WordNet::Similarity::random')} my $wn = new WordNet::QueryData; ok ($wn); my $config = "rand$$.txt"; my $maxrand = 3.14159; ok (open FH, ">$config") or diag "Cannot create temporary config file: $!"; print FH "WordNet::Similarity::random\n"; print FH "trace::0\n"; print FH "cache::0\n"; print FH "maxrand::$maxrand\n"; ok (close FH); my $module = new WordNet::Similarity::random ($wn, $config); ok ($module); my ($err, $errstr) = $module->getError (); is ($err, 0); # just test to make sure that all the numbers generated by random are # less than $maxrand and greater than or equal to 0. for (1..100) { my $score = $module->getRelatedness ("dog#n#1", "cat#n#1"); cmp_ok ($score, '<', $maxrand); cmp_ok ($score, '>=', 0); } END { ok (unlink $config); } WordNet-Similarity-2.05/t/config/lesk.t0000755000175000017500000000346111020102073016112 0ustar sidsid#! /usr/bin/perl -w # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/config/lesk.t' # A script to test configuration options specific to the # WordNet::Similarity::lesk module. This test works by # constructing a config file that contains options specific # to the lesk measure and then running some getRelatedness # queries. use strict; use warnings; my $num_tests; BEGIN { $num_tests = 12; } use Test::More tests => $num_tests; BEGIN {use_ok ('WordNet::QueryData')} BEGIN {use_ok ('WordNet::Similarity::lesk')} my $wn = new WordNet::QueryData; ok ($wn); my $config = "lesk$$.txt"; my $relation = "leskrelation$$.txt"; my $stop = "leskstop$$.txt"; # create a temporary file to use as a config file ok (open FH, ">$config") or diag "Cannot create temporary config file: $!"; print FH "WordNet::Similarity::lesk\n"; print FH "stem::1\n"; print FH "relation::$relation\n"; print FH "normalize::0\n"; ok (close FH); # create a temporary file to use as a relation file ok (open RFH, ">$relation") or diag "Cannot create temporary file: $!"; print RFH "LeskRelationFile\n"; print RFH "glosexample-glosexample\n"; print RFH "glosexample(hype)-glosexample(hype)\n"; print RFH "glosexample(hypo)-glosexample(hypo)\n"; print RFH "glosexample(hype)-glosexample(hypo)\n"; print RFH "glosexample(hypo)-glosexample(hype)\n"; ok (close RFH); my $module = new WordNet::Similarity::lesk ($wn, $config); ok ($module); my ($err, $errstr) = $module->getError(); is ($err, 0) or diag "$errstr"; my $score = $module->getRelatedness ('tree#n#1', 'shrub#n#1'); ($err, $errstr) = $module->getError(); is ($err, 0); ok ($score > 4800); # was 4963 before Text::OverlapFinder was integrated # would be 4909 without stemming END {ok unlink ($config, $relation, $stop)} WordNet-Similarity-2.05/t/pairs-noroot.t0000755000175000017500000001336611020104752016356 0ustar sidsid#! /usr/bin/perl -w # pairs-noroot.t version 2.05 # ($Id: pairs-noroot.t,v 1.8 2008/05/30 23:12:42 sidz1979 Exp $) # Copyright (C) 2004 # Jason Michelizzi, University of Minnesota Duluth # mich0212 at d.umn.edu # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # Before 'make install' is performed this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/pairs-noroot.t' # A script to run queries on a large file of words and compare the results # with a set of relatedness values stored in "keys." The list of pairs # of words was generated by the randomPairs.pl program that can be obtained # from http://www.d.umn.edu/~tpederse/wordnet.html # This script supports two options: # 1) the --key option can be used to generate a key, in which case no actual # tests are run. # 2) the --keydir option can be used to specify where the key is or should be # stored. The default value is t/keys. use strict; use warnings; use Getopt::Long; our ($opt_key, $opt_keydir); GetOptions ("key", "keydir=s"); my @measures; my $num_tests; BEGIN { @measures = qw/jcn lch lin path res wup/; $num_tests = 19 + (7 + 109) * scalar @measures; } use Test::More tests => $num_tests; BEGIN {use_ok 'WordNet::QueryData'} BEGIN {use_ok 'WordNet::Tools'} #BEGIN {use_ok 'WordNet::Similarity::hso'} BEGIN {use_ok 'WordNet::Similarity::jcn'} BEGIN {use_ok 'WordNet::Similarity::lch'} #BEGIN {use_ok 'WordNet::Similarity::lesk'} BEGIN {use_ok 'WordNet::Similarity::lin'} BEGIN {use_ok 'WordNet::Similarity::path'} # There's really no point in testing random like this #BEGIN {use_ok 'WordNet::Similarity::random'} BEGIN {use_ok 'WordNet::Similarity::res'} #BEGIN {use_ok 'WordNet::Similarity::vector'} BEGIN {use_ok 'WordNet::Similarity::wup'} BEGIN {use_ok 'File::Spec'} my $precision = 4; my $wn = WordNet::QueryData->new; ok ($wn); my $wntools = WordNet::Tools->new($wn); ok ($wntools); my $wnHash = $wntools->hashCode(); ok ($wnHash); my $infile = File::Spec->catfile ('t', 'pairs.txt'); ok (-e $infile); ok (-r $infile); ok (open FH, $infile) or diag "Could not open $infile: $!"; my @lines = ; ok (close FH); my @pairs = map {my ($w1, $w2) = split; [$w1, $w2]} @lines; my $wnverfile; if ($opt_keydir) { $wnverfile = File::Spec->catfile ($opt_keydir, "wnver.key"); } else { $wnverfile = File::Spec->catfile ('t', 'keys', "wnver.key"); } ok (open KEY, $wnverfile) or diag "Could not open $wnverfile: $!"; my $wnver = ; ok (defined $wnver); $wnver = "" if(!defined($wnver)); $wnver =~ s/[\r\f\n]+//g; $wnver =~ s/^\s+//; $wnver =~ s/\s+$//; ok (close KEY); # the temporary config file my $config = "pairs_nr.cfg"; # if the key option is given, we want to generate a "key" for the tests. # A key is essentially just a list of relatedness values. If the key # option is not given, then we use the key to test if the relatedness # values we are generating correspond to the key's values. unless ($opt_key) { # not generating a key--actually running tests foreach my $measure (@measures) { SKIP: { skip "Hash-code of key file(s) does not match installed WordNet", 116 if($wnver ne $wnHash); # make a temporary config file ok (open CFH, ">$config") or diag "Could not open $config: $!"; print CFH "WordNet::Similarity::$measure\n"; print CFH "rootNode::0\n"; ok (close CFH); my $keyfile; if ($opt_keydir) { $keyfile = File::Spec->catfile ($opt_keydir, "${measure}pairs_nr.key"); } else { $keyfile = File::Spec->catfile ('t', 'keys', "${measure}pairs_nr.key"); } ok (open KEY, $keyfile) or diag "Could not open $keyfile: $!"; my @keys = map {chomp; $_} ; is (scalar @keys, scalar @pairs); ok (close KEY); my $module = "WordNet::Similarity::$measure"->new ($wn, $config); ok ($module); my ($err, $errstr) = $module->getError (); is ($err, 0) or diag "$errstr"; for (0..$#pairs) { my ($word1, $word2) = ($pairs[$_]->[0], $pairs[$_]->[1]); my $score = $module->getRelatedness ($word1, $word2); # format the score so that we can compare it to the value from file $score = defined $score ? sprintf ("%.*f", $precision, $score) : 'undef'; is ($score, $keys[$_]) or diag "Wrong relatedness using $measure for $word1 $word2"; $module->getError(); } } } } else { foreach my $measure (@measures) { # generating keys # make a temporary config file ok (open CFH, ">$config") or diag "Could not open $config: $!"; print CFH "WordNet::Similarity::$measure\n"; print CFH "rootNode::0\n"; ok (close CFH); my $keyfile; if ($opt_keydir) { $keyfile = File::Spec->catfile ($opt_keydir, "${measure}pairs_nr.key"); } else { $keyfile = File::Spec->catfile ('t', 'keys', "${measure}pairs_nr.key"); } ok (open KEY, ">$keyfile") or diag "Could not open $keyfile: $!"; my $module = "WordNet::Similarity::$measure"->new ($wn, $config); ok ($module); my ($err, $errstr) = $module->getError (); is ($err, 0) or diag "$errstr\n"; for (0..$#pairs) { my $score = $module->getRelatedness ($pairs[$_]->[0], $pairs[$_]->[1]); if (defined $score) { printf KEY "%.*f\n", $precision, $score; } else { print KEY "undef\n"; } $module->getError(); # clears the error level } ok (close KEY); } # hack to prevent annoying warning: when we generate keys, we skip a lot # of tests. This magic avoids an irritating warning that says something # to the effect of "looks like you planned X tests but only ran Y." SKIP: { my $num_skipped = $num_tests - 20 - 6 * scalar (@measures); skip ("Generating key, no need to run test", $num_skipped); } } END {unlink $config} WordNet-Similarity-2.05/t/tests/0000755000175000017500000000000011025552366014700 5ustar sidsidWordNet-Similarity-2.05/t/tests/jcn.txt0000644000175000017500000000115711015336624016213 0ustar sidsidcontrition#n#1, compunction#n#1: 0.299568090603061 dog#n#1, hunting_dog#n#1: 0.592979495085031 dog#n#1, cat#n#1: 0.545678333968645 levity#n#1, contrition#n#1: 0 hope#n#2, compunction#n#1: 0.10071974365616 like#v#2, cotton#v#1: 0 adore#v#1, like#v#2: 0.0612494832618107 hope#n#2, like#v#2: 0 blue_sky#n#1, Earth#n#4: 0.0460980673737338 worship#v#1, adore#v#1: 8.49018701570399 outer_space#n#1, abstraction#n#6: 0.0918159446164477 languish#v#2, weep#v#1: 0.0534960490266613 hjxlq#n, ynbr#n: undefined (error level 1) intelligent#a#1, intelligence#n#1: 0 weep#v#1, erupt#v#6: 0.259888537106915 leather_carp#n#1, grotto#n#1: 0 WordNet-Similarity-2.05/t/tests/vector.txt0000644000175000017500000000000011015336624016725 0ustar sidsidWordNet-Similarity-2.05/t/tests/test-modules.pl0000755000175000017500000000760111020102214017643 0ustar sidsid#! /usr/bin/perl -w use strict; use warnings; use constant SCRIPT_NAME => 'test-modules.pl'; use Getopt::Long; use WordNet::Similarity::res; use WordNet::Similarity::lin; use WordNet::Similarity::jcn; use WordNet::Similarity::path; use WordNet::Similarity::wup; use WordNet::Similarity::lch; use WordNet::Similarity::hso; use WordNet::Similarity::lesk; use WordNet::Similarity::random; use WordNet::Similarity::vector; use WordNet::QueryData; our ($opt_wnpath, $opt_version, $opt_help, $opt_type, $opt_outfile); our ($opt_config); my $result = GetOptions ("wnpath=s", "version", "help", "type=s", "outfile=s", "config=s"); unless ($result) { showUsage (); exit (1); } if ($opt_help) { showHelp (); exit (0); } if ($opt_version) { print "".SCRIPT_NAME." version 2.05\n"; print "Copyright (C) 2003, Jason Michelizzi and Ted Pedersen\n"; exit (0); } unless ($opt_type) { showUsage (); exit (1); } my $outfh = *STDOUT; if ($opt_outfile) { open ($outfh, ">$opt_outfile") or die "Cannot open $opt_outfile: $!"; } # find WordNet my $wnhome = '/usr/local/WordNet-2.0/dict'; $wnhome = $ENV{WNHOME} if $ENV{WNHOME}; $wnhome = $opt_wnpath if $opt_wnpath; print STDERR "Loading WordNet... "; my $wn = new WordNet::QueryData ($wnhome); if ($wn) { print STDERR "done.\n"; } else { print STDERR "failed.\n"; exit (1); } my $module = $opt_type; $module = "WordNet::Similarity::".$module unless $module =~ m/WordNet::Similarity/; print STDERR "Loading $module... "; my $measure = $opt_config ? new $module ($wn, $opt_config) : new $module ($wn); print STDERR "done.\n"; my ($err, $errstr) = $measure->getError (); $err and die "Error creating $module: ($errstr)"; # now do some tests my @pairs = (['contrition#n#1', 'compunction#n#1'], ['dog#n#1', 'hunting_dog#n#1'], ['dog#n#1', 'cat#n#1'], ['levity#n#1', 'contrition#n#1'], ['hope#n#2', 'compunction#n#1'], ['like#v#2', 'cotton#v#1'], ['adore#v#1', 'like#v#2'], ['hope#n#2', 'like#v#2'], ['blue_sky#n#1', 'Earth#n#4'], ['worship#v#1', 'adore#v#1'], ['outer_space#n#1', 'abstraction#n#6'], ['languish#v#2', 'weep#v#1'], ['hjxlq#n', 'ynbr#n'], ['intelligent#a#1', 'intelligence#n#1'], ['weep#v#1', 'erupt#v#6'], ['leather_carp#n#1', 'grotto#n#1'], ); foreach my $pair (@pairs) { print $outfh "$pair->[0], $pair->[1]: "; my $score = $measure->getRelatedness ($pair->[0], $pair->[1]); if (defined $score) { print $outfh "$score\n"; } else { my ($err, $errString) = $measure->getError (); print $outfh "undefined (error level $err)\n"; } } close $outfh if $opt_outfile; sub showUsage { print "Usage: ".SCRIPT_NAME." {--type=measure | --version | --help}\n"; } sub showHelp { print "Usage: ".SCRIPT_NAME." {--type=measure | --version | --help}\n"; print "Options:\n"; print "\t--type=measure specifies the name of a measure to test\n"; print "\t--version show version information\n"; print "\t--help show this help message\n"; } __END__ =head1 NAME test-modules.pl -- run tests on WordNet::Similarity modules =head1 SYNOPSIS test-modules.pl [--type=measure | --version | --help ] =head1 DESCRIPTION B: The following is out-of-date and just plain wrong. Use this to test WordNet::Similarity. You must have run Makefile.PL and make first (but before you run make install). Runs some tests on a bunch of pairs of word senses, printing to the standard output. To see if a new distribution is working correctly, do this: ./test-distro.pl res --no-debug > stable.text ./test-distro.pl res > test.text diff stable.text test.text You may substitute the measure of your choice for 'res' above. =head1 OPTIONS B<--no-debug> When this option is given, the version of WordNet::Similarity found in /usr/lib/perl5/site_perl/5.8.0 (or something similar) is used instead of the version in ./blib/lib/ WordNet-Similarity-2.05/t/tests/test-utils.pl0000755000175000017500000000316211020102311017327 0ustar sidsid#! /usr/bin/perl -w use strict; use warnings; use Getopt::Long; our ($opt_version, $opt_help, $opt_wnpath, $opt_config, $opt_type); my $res = GetOptions ("version", "help", "wnpath=s", "config=s", "type=s"); unless ($res) { showUsage (); exit 0; } if ($opt_help) { showHelp (); exit 0; } if ($opt_version) { showVersion(); exit 0; } unless ($opt_type) { showUsage(); exit (1); } # run some tests on similarity.pl and other utils here my $wnhome = '/usr/local/WordNet-2.0/dict'; $wnhome = $ENV{WNHOME} if $ENV{WNHOME}; $wnhome = $opt_wnpath if $opt_wnpath; $opt_type = "WordNet::Similarity::".$opt_type unless $opt_type =~ m/WordNet::Similarity/; if ($opt_config) { system ('similarity.pl', "--type=$opt_type", "--wnpath=$wnhome", "--config=$opt_config", "dog#n#1", "cat#n#1"); } else { system ('similarity.pl', "--type=$opt_type", "--wnpath=$wnhome", "dog#n#1", "cat#n#1"); } exit ($? >> 8); sub showUsage { print "Usage: test-utils.pl [[--wnpath=PATH] [--config=FILE]\n"; print " | --version | --help]\n"; } sub showHelp { showUsage(); print "Options:\n"; print "\t--wnpath=PATH the path to WordNet data files\n"; print "\t--config=FILE the name of a config file\n"; print "\t--help show this help message\n"; print "\t--version show version information\n"; } sub showVersion { print "test-utils.pl version 2.05\n"; print "Copyright (C) 2003, Jason Michelizzi and Ted Pedersen\n"; print "\nThis program is free software; you may redistribute it and/or\n"; print "modify it under the same terms as Perl itself.\n"; } __END__ WordNet-Similarity-2.05/t/tests/lesk.txt0000644000175000017500000000073311015336624016376 0ustar sidsidcontrition#n#1, compunction#n#1: 86 dog#n#1, hunting_dog#n#1: 824 dog#n#1, cat#n#1: 83 levity#n#1, contrition#n#1: 2 hope#n#2, compunction#n#1: 12 like#v#2, cotton#v#1: 35 adore#v#1, like#v#2: 11 hope#n#2, like#v#2: 2 blue_sky#n#1, Earth#n#4: 6 worship#v#1, adore#v#1: 128 outer_space#n#1, abstraction#n#6: 78 languish#v#2, weep#v#1: 12 hjxlq#n, ynbr#n: undefined (error level 1) intelligent#a#1, intelligence#n#1: 319 weep#v#1, erupt#v#6: 90 leather_carp#n#1, grotto#n#1: 2 WordNet-Similarity-2.05/t/tests/random.txt0000644000175000017500000000101111015336624016706 0ustar sidsidcontrition#n#1, compunction#n#1: 0.596 dog#n#1, hunting_dog#n#1: 0.777 dog#n#1, cat#n#1: 0.735 levity#n#1, contrition#n#1: 0.822 hope#n#2, compunction#n#1: 0.877 like#v#2, cotton#v#1: 0.900 adore#v#1, like#v#2: 0.112 hope#n#2, like#v#2: 0.217 blue_sky#n#1, Earth#n#4: 0.801 worship#v#1, adore#v#1: 0.931 outer_space#n#1, abstraction#n#6: 0.454 languish#v#2, weep#v#1: 0.673 hjxlq#n, ynbr#n: undefined (error level 1) intelligent#a#1, intelligence#n#1: 0.497 weep#v#1, erupt#v#6: 0.976 leather_carp#n#1, grotto#n#1: 0.167 WordNet-Similarity-2.05/t/tests/edge.txt0000644000175000017500000000105711015336624016344 0ustar sidsidcontrition#n#1, compunction#n#1: 0.333333333333333 dog#n#1, hunting_dog#n#1: 0.5 dog#n#1, cat#n#1: 0.2 levity#n#1, contrition#n#1: 0.2 hope#n#2, compunction#n#1: 0.2 like#v#2, cotton#v#1: 0.5 adore#v#1, like#v#2: 0.25 hope#n#2, like#v#2: -1000000 blue_sky#n#1, Earth#n#4: 0.2 worship#v#1, adore#v#1: 0.5 outer_space#n#1, abstraction#n#6: 0.333333333333333 languish#v#2, weep#v#1: 0.166666666666667 hjxlq#n, ynbr#n: undefined (error level 1) intelligent#a#1, intelligence#n#1: -1000000 weep#v#1, erupt#v#6: 0.333333333333333 leather_carp#n#1, grotto#n#1: 0.05 WordNet-Similarity-2.05/t/tests/lin.txt0000644000175000017500000000111611015336624016216 0ustar sidsidcontrition#n#1, compunction#n#1: 0.857367645090549 dog#n#1, hunting_dog#n#1: 0.910791381188108 dog#n#1, cat#n#1: 0.898350990339786 levity#n#1, contrition#n#1: 0 hope#n#2, compunction#n#1: 0.514718984415405 like#v#2, cotton#v#1: 0 adore#v#1, like#v#2: 0 hope#n#2, like#v#2: 0 blue_sky#n#1, Earth#n#4: 0.0731042611389711 worship#v#1, adore#v#1: 0.993850253184671 outer_space#n#1, abstraction#n#6: 0.238590767026328 languish#v#2, weep#v#1: 0 hjxlq#n, ynbr#n: undefined (error level 1) intelligent#a#1, intelligence#n#1: 0 weep#v#1, erupt#v#6: 0.778781602035823 leather_carp#n#1, grotto#n#1: 0 WordNet-Similarity-2.05/t/tests/wup.txt0000644000175000017500000000105311015336624016247 0ustar sidsidcontrition#n#1, compunction#n#1: 0.8 dog#n#1, hunting_dog#n#1: 0.96 dog#n#1, cat#n#1: 0.833333333333333 levity#n#1, contrition#n#1: 0.5 hope#n#2, compunction#n#1: 0.5 like#v#2, cotton#v#1: 0.666666666666667 adore#v#1, like#v#2: 0 hope#n#2, like#v#2: -1000000 blue_sky#n#1, Earth#n#4: 0.333333333333333 worship#v#1, adore#v#1: 0.8 outer_space#n#1, abstraction#n#6: 0.5 languish#v#2, weep#v#1: 0 hjxlq#n, ynbr#n: undefined (error level 1) intelligent#a#1, intelligence#n#1: -1000000 weep#v#1, erupt#v#6: 0.5 leather_carp#n#1, grotto#n#1: 0.0952380952380952 WordNet-Similarity-2.05/t/tests/lch.txt0000644000175000017500000000123711015336624016206 0ustar sidsidcontrition#n#1, compunction#n#1: 2.36712361413162 dog#n#1, hunting_dog#n#1: 2.77258872223978 dog#n#1, cat#n#1: 1.85629799036563 levity#n#1, contrition#n#1: 1.85629799036563 hope#n#2, compunction#n#1: 1.85629799036563 like#v#2, cotton#v#1: 2.77258872223978 adore#v#1, like#v#2: 2.07944154167984 hope#n#2, like#v#2: -1000000 blue_sky#n#1, Earth#n#4: 1.85629799036563 worship#v#1, adore#v#1: 2.77258872223978 outer_space#n#1, abstraction#n#6: 2.36712361413162 languish#v#2, weep#v#1: 1.67397643357167 hjxlq#n, ynbr#n: undefined (error level 1) intelligent#a#1, intelligence#n#1: -1000000 weep#v#1, erupt#v#6: 2.36712361413162 leather_carp#n#1, grotto#n#1: 0.470003629245736 WordNet-Similarity-2.05/t/tests/res.txt0000644000175000017500000000116311015336624016227 0ustar sidsidcontrition#n#1, compunction#n#1: 10.0328308604412 dog#n#1, hunting_dog#n#1: 8.60879617133842 dog#n#1, cat#n#1: 8.09797054757243 levity#n#1, contrition#n#1: 5.26541101197364 hope#n#2, compunction#n#1: 5.26541101197364 like#v#2, cotton#v#1: 6.8093090758819 adore#v#1, like#v#2: 0 hope#n#2, like#v#2: 0 blue_sky#n#1, Earth#n#4: 0.855458788752587 worship#v#1, adore#v#1: 9.51735927698411 outer_space#n#1, abstraction#n#6: 1.70642573500319 languish#v#2, weep#v#1: 0 hjxlq#n, ynbr#n: undefined (error level 1) intelligent#a#1, intelligence#n#1: 0 weep#v#1, erupt#v#6: 6.77294143171102 leather_carp#n#1, grotto#n#1: 0.855458788752587 WordNet-Similarity-2.05/t/tests/hso.txt0000644000175000017500000000071711015336624016233 0ustar sidsidcontrition#n#1, compunction#n#1: 5 dog#n#1, hunting_dog#n#1: 16 dog#n#1, cat#n#1: 3 levity#n#1, contrition#n#1: 3 hope#n#2, compunction#n#1: 3 like#v#2, cotton#v#1: 5 adore#v#1, like#v#2: 0 hope#n#2, like#v#2: 0 blue_sky#n#1, Earth#n#4: 3 worship#v#1, adore#v#1: 4 outer_space#n#1, abstraction#n#6: 6 languish#v#2, weep#v#1: 0 hjxlq#n, ynbr#n: undefined (error level 1) intelligent#a#1, intelligence#n#1: 16 weep#v#1, erupt#v#6: 5 leather_carp#n#1, grotto#n#1: 0 WordNet-Similarity-2.05/t/wup.t0000755000175000017500000000707211020102003014515 0ustar sidsid#! /usr/bin/perl -w # Before `make install' is performed this script should be runnable with # `make test'. After `make install' it should work as `perl t/wup.t' # A script to test the Wu & Palmer (wup.pm) measure. This script runs the # following tests: # # 1) tests whether the modules are constructed correctly # 2) tests whether an error is given when no WordNet::QueryData object # is supplied # 3) simple getRelatedness queries are performed on valid words, invalid # words, and words from different parts of speech # We start with some black magic to print on failure. BEGIN { $| = 1; print "1..11\n"; } END {print "not ok 1\n" unless $loaded;} use WordNet::Similarity; use WordNet::QueryData; use WordNet::Similarity::wup; $loaded = 1; print "ok 1\n"; use strict; use warnings; # Load QueryData my $wn = WordNet::QueryData->new(); if($wn) { print "ok 2\n"; } else { print "not ok 2\n"; } # Load wup my $wup = WordNet::Similarity::wup->new($wn); if($wup) { my ($err, $errString) = $wup->getError(); if($err) { print "not ok 3\n"; } else { print "ok 3\n"; } } else { print "not ok 3\n"; } # Load wup with undef QueryData. my $badwup = WordNet::Similarity::wup->new(undef); if($badwup) { my ($err, $errString) = $badwup->getError(); if($err < 2) { print "not ok 4\n"; } elsif($err == 2) { if($errString =~ /A WordNet::QueryData object is required/) { print "ok 4\n"; } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } # GetRelatedness of same synset. my $value = undef; eval {$value = $wup->getRelatedness("object#n#1", "object#n#1");}; if($@) { print "not ok 5\n"; } elsif($value && $value =~ /[0-9]/) { if($value == 1) { print "ok 5\n"; } else { print "not ok 5\n"; } } else { print "not ok 5\n"; } $value = undef; eval {$value = $wup->getRelatedness("eating_apple#n#1", "eating_apple#n#1");}; if($@) { print "not ok 6\n"; } elsif(defined $value && $value =~ /\d+/) { if($value == 1) { print "ok 6\n"; } else { print "not ok 6\n"; } } else { print "not ok 6\n"; } # getRelatedness of badly formed synset. $value = undef; eval {$value = $wup->getRelatedness("hjxlq#n", "pynbr#n");}; if($@) { print "not ok 7\n"; } elsif($value) { print "not ok 7\n"; } else { my ($err, $errString) = $wup->getError(); if($err == 1) { print "ok 7\n"; } else { print "not ok 7\n"; } } # Relatedness across parts of speech. $wup->{trace} = 1; $value = undef; eval {$value = $wup->getRelatedness("object#n#1", "run#v#1");}; if($@) { print "not ok 8\n"; } elsif($value && $value >= 0) { print "not ok 8\n"; } else { print "ok 8\n"; } # Test traces. # JM 1-6-04 # we changed how words from different parts of speech are handled # if($m->getTraceString() !~ /Relatedness 0 across parts of speech/) if(($wup->getError())[0] != 1) { print "not ok 9\n"; } else { print "ok 9\n"; } # Test tilde self-similarity eval {$value = $wup->getRelatedness("tilde#n#1", "tilde#n#1");}; if($@) { print "not ok 10\n"; } elsif($value && $value =~ /[0-9]/) { if($value == 1) { print "ok 10\n"; } else { print "not ok 10\n"; } } else { print "not ok 10\n"; } # getRelatedness of badly formed synset. # (Tried getRelatedness of unknown synsets... "hjxlq#n#1", "pynbr#n#2"... # QueryData complains... trap that error myself.) $value = undef; eval {$value = $wup->getRelatedness("hjxlq#n#1", "pynbr#n#2");}; if($@) { print "ok 11\n"; } elsif(defined $value) { print "not ok 11\n"; } else { print "ok 11\n"; } WordNet-Similarity-2.05/t/jcn.t0000755000175000017500000000554611020101163014466 0ustar sidsid#! /usr/bin/perl -w # Before `make install' is performed this script should be runnable with # `make test'. After `make install' it should work as `perl t/jcn.t' # A script to test the Jiang-Conrath (jcn.pm) measure. This script runs the # following tests: # # 1) tests whether the modules are constructed correctly # 2) tests whether an error is given when no WordNet::QueryData object # is supplied # 3) simply getRelatedness queries are performed on valid words, invalid # words, and words from different parts of speech ##################### We start with some black magic to print on failure. BEGIN { $| = 1; print "1..8\n"; } END {print "not ok 1\n" unless $loaded;} use WordNet::Similarity; use WordNet::QueryData; use WordNet::Similarity::jcn; $loaded = 1; print "ok 1\n"; ######################### End of black magic. # Insert your test code below (better if it prints "ok 13" # (correspondingly "not ok 13") depending on the success of chunk 13 # of the test code): ############ Load QueryData $wn = WordNet::QueryData->new(); if($wn) { print "ok 2\n"; } else { print "not ok 2\n"; } ############ Load jcn $m = WordNet::Similarity::jcn->new($wn); if($m) { ($err, $errString) = $m->getError(); if($err) { print "not ok 3\n"; } else { print "ok 3\n"; } } else { print "not ok 3\n"; } ############ Load jcn with undef QueryData. $bad = WordNet::Similarity::jcn->new(undef); if($bad) { ($err, $errString) = $bad->getError(); if($err < 2) { print "not ok 4\n"; } elsif($err == 2) { if($errString =~ /A WordNet::QueryData object is required/) { print "ok 4\n"; } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } ############ GetRelatedness of same synset. $value = $m->getRelatedness("object#n#1", "object#n#1"); if($value && $value =~ /[0-9]+(\.[0-9]+)?/) { if($value > 0) { print "ok 5\n"; } else { print "not ok 5\n"; } } else { print "not ok 5\n"; } ############ getRelatedness of badly formed synset. ## (Tried getRelatedness of unknown synsets... "hjxlq#n#1", "pynbr#n#2"... ## QueryData complains... can't trap that error myself.) if(defined $m->getRelatedness("hjxlq#n", "pynbr#n")) { print "not ok 6\n"; } else { ($err, $errString) = $m->getError(); if($err == 1) { print "ok 6\n"; } else { print "not ok 6\n"; } } ############ Relatedness across parts of speech. $m->{'trace'} = 1; if($m->getRelatedness("object#n#1", "run#v#1") >= 0) { print "not ok 7\n"; } else { print "ok 7\n"; } ############ Test traces. # JM 1-6-04 # we changed how words from different parts of speech are handled #if($m->getTraceString() !~ /Relatedness 0 across parts of speech/) if (($m->getError())[0] != 1) { print "not ok 8\n"; } else { print "ok 8\n"; } WordNet-Similarity-2.05/t/lin.t0000755000175000017500000000553611020101223014472 0ustar sidsid#! /usr/bin/perl -w # Before `make install' is performed this script should be runnable with # `make test'. After `make install' it should work as `perl t/lin.t' # A script to test the Lin (lin.pm) measure. This script runs the # following tests: # # 1) tests whether the modules are constructed correctly # 2) tests whether an error is given when no WordNet::QueryData object # is supplied # 3) simply getRelatedness queries are performed on valid words, invalid # words, and words from different parts of speech ##################### We start with some black magic to print on failure. BEGIN { $| = 1; print "1..8\n"; } END {print "not ok 1\n" unless $loaded;} use WordNet::Similarity; use WordNet::QueryData; use WordNet::Similarity::lin; $loaded = 1; print "ok 1\n"; ######################### End of black magic. # Insert your test code below (better if it prints "ok 13" # (correspondingly "not ok 13") depending on the success of chunk 13 # of the test code): ############ Load QueryData $wn = WordNet::QueryData->new(); if($wn) { print "ok 2\n"; } else { print "not ok 2\n"; } ############ Load lin $m = WordNet::Similarity::lin->new($wn); if($m) { ($err, $errString) = $m->getError(); if($err) { print "not ok 3\n"; } else { print "ok 3\n"; } } else { print "not ok 3\n"; } ############ Load lin with undef QueryData. $bad = WordNet::Similarity::lin->new(undef); if($bad) { ($err, $errString) = $bad->getError(); if($err < 2) { print "not ok 4\n"; } elsif($err == 2) { if($errString =~ /A WordNet::QueryData object is required/) { print "ok 4\n"; } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } ############ GetRelatedness of same synset. $value = $m->getRelatedness("object#n#1", "object#n#1"); if($value && $value =~ /[0-9]+(\.[0-9]+)?/) { if($value == 1) { print "ok 5\n"; } else { print "not ok 5\n"; } } else { print "not ok 5\n"; } ############ getRelatedness of badly formed synset. ## (Tried getRelatedness of unknown synsets... "hjxlq#n#1", "pynbr#n#2"... ## QueryData complains... can't trap that error myself.) if(defined $m->getRelatedness("hjxlq#n", "pynbr#n")) { print "not ok 6\n"; } else { ($err, $errString) = $m->getError(); if($err == 1) { print "ok 6\n"; } else { print "not ok 6\n"; } } ############ Relatedness across parts of speech. $m->{'trace'} = 1; if($m->getRelatedness("object#n#1", "run#v#1") >= 0) { print "not ok 7\n"; } else { print "ok 7\n"; } ############ Test traces. # JM 1-6-04 # we changed how words from different parts of speech are handled #if($m->getTraceString() !~ /Relatedness 0 across parts of speech/) if (($m->getError())[0] != 1) { print "not ok 8\n"; } else { print "ok 8\n"; } WordNet-Similarity-2.05/t/pod-doc.t0000755000175000017500000000052211020101555015233 0ustar sidsid#! /usr/bin/perl -w # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/pod-doc.t' use Test::More; eval "use Test::Pod 1.00"; plan skip_all => "Test::Pod 1.00 required for testing POD" if $@; my @poddirs = qw(doc); all_pod_files_ok(all_pod_files(@poddirs)); WordNet-Similarity-2.05/t/vector.t0000755000175000017500000000417711020101721015215 0ustar sidsid#! /usr/bin/perl -w # Before `make install' is performed this script should be runnable with # `make test'. After `make install' it should work as `perl t/vector.t' ##################### We start with some black magic to print on failure. BEGIN { $| = 1; print "1..6\n"; } END {print "not ok 1\n" unless $loaded;} use WordNet::Similarity; use WordNet::QueryData; use WordNet::Similarity::vector; $loaded = 1; print "ok 1\n"; ######################### End of black magic. # Insert your test code below (better if it prints "ok 13" # (correspondingly "not ok 13") depending on the success of chunk 13 # of the test code): ############ Load QueryData $wn = WordNet::QueryData->new(); if($wn) { print "ok 2\n"; } else { print "not ok 2\n"; } ############ Load vector $vector = WordNet::Similarity::vector->new($wn); if($vector) { ($err, $errString) = $vector->getError(); if($err) { print "not ok 3\n"; } else { print "ok 3\n"; } } else { print "not ok 3\n"; } ############ Load vector with undef QueryData. $badVector = WordNet::Similarity::vector->new(undef); if($badVector) { ($err, $errString) = $badVector->getError(); if($err < 2) { print "not ok 4\n"; } elsif($err == 2) { if($errString =~ /A WordNet::QueryData object is required/) { print "ok 4\n"; } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } ############ getRelatedness of badly formed synset. ## (Tried getRelatedness of unknown synsets... "hjxlq#n#1", "pynbr#n#2"... ## QueryData complains... cannot trap that error myself.) if(defined $vector->getRelatedness("hjxlq#n", "pynbr#n")) { print "not ok 5\n"; } else { ($err, $errString) = $vector->getError(); if($err == 1) { print "ok 5\n"; } else { print "not ok 5\n"; } } ########### getRelatedness across parts of speech $value = $vector->getRelatedness("pay#v#1", "money#n#1"); if(defined $value && $value =~ /[\.0-9]+/) { if($value > 0) { print "ok 6\n"; } else { print "not ok 6\n"; } } else { print "not ok 6\n"; } WordNet-Similarity-2.05/t/utils/0000755000175000017500000000000011025552366014676 5ustar sidsidWordNet-Similarity-2.05/t/utils/sim-file.t0000755000175000017500000000547411020102375016567 0ustar sidsid#! /usr/bin/perl -w # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/utils/sim-file.t' # A script to ensure that similarity.pl handles input from files correctly # and ensures that invalid files result in a warning or error. The # following cases are tested: # # 1) a valid file containing comments and words in full w#p#s format # as well as w#p and w formats # 2) an invalid file with only one word on a line # 3) an invalid file with a bad part of speech ina wps string # 4) an invalid file with a bad sense number use strict; use warnings; my @measures; my $num_tests; BEGIN { @measures = qw/res lin jcn path lch wup hso lesk/; # vector $num_tests = 12 + 5 * scalar (@measures); } use Test::More tests => $num_tests; BEGIN {use_ok ('File::Spec')} @measures = map "WordNet::Similarity::$_", @measures; my $similarity = File::Spec->catfile ('utils', 'similarity.pl'); # find null device (e.g., /dev/null) my $devnull = File::Spec->devnull (); ok (-e $similarity); ok (-r $similarity); ok (-x $similarity); my $perl = $^X; # create a temporary file to serve as an input file to similarity.pl my $wordsfile = "wordpairs$$.txt"; ok (open FH, ">$wordsfile") or diag "Could not create a temporary file: $!"; print FH "dog#n cat#n\n"; print FH "dog#n#1 cat#n#1\n"; print FH "// this is a comment\n"; print FH "trombone bassoon//another comment\n"; print FH "reed brass // each has multiple senses\n"; ok (close FH); # just make sure that no errors/warnings occur with this file (since it # is a valid file) foreach my $measure (@measures) { my $output = qx|$perl -MExtUtils::testlib $similarity --type=$measure --file=$wordsfile 2>&1|; is ($?, 0); unlike ($output, qr/Warning|Error/i); } # create a new, invalid file ok (open FH, ">$wordsfile") or diag "Could not re-create a temporary file: $!"; print FH "dog\n"; ok (close FH); # check to make sure an error occurs when only one word is given foreach my $measure (@measures) { my $output = qx/$perl -MExtUtils::testlib $similarity --type=$measure --file=$wordsfile 2>&1/; like ($output, qr/Warning: line 1/i); } # create a new, invalid file ok (open FH, ">$wordsfile") or diag "Could not re-create a temporary file: $!"; print FH "cat#dog#1 dog#n#1\n"; ok (close FH); foreach my $measure (@measures) { my $output = qx/$perl -MExtUtils::testlib $similarity --type=$measure --file=$wordsfile 2>&1/; like ($output, qr/not found in WordNet/i); } # create a new, invalid file ok (open FH, ">$wordsfile") or diag "Could not re-create a temporary file: $!"; print FH "cat#n#n cat#n#2\n"; ok (close FH); foreach my $measure (@measures) { my $output = qx/$perl -MExtUtils::testlib $similarity --type=$measure --file=$wordsfile 2>&1/; like ($output, qr/not found in WordNet/i); } END { unlink $wordsfile; } WordNet-Similarity-2.05/t/utils/wnDepths.t0000755000175000017500000001065511020102443016647 0ustar sidsid#! /usr/bin/perl -w # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/utils/wnDepths.t' # A script to test the wnDepths.pl utility program. The following # tests are performed: # # 1) wnDepths.pl is run and the output is inspected for a few specific # cases. # 2) the entire output is compared to a "key" file. # This script supports two options: # 1) the --key option can be used to generate a key, in which case no actual # tests are run. # 2) the --keydir option can be used to specify where the key is or should be # stored. The default value is t/keys. use strict; use warnings; use Getopt::Long; our ($opt_key, $opt_keydir); GetOptions ("key", "keydir=s") or die "Bad command line options"; use Test::More 'no_plan'; BEGIN {use_ok 'File::Spec'} my $wndepths = File::Spec->catfile ('utils', 'wnDepths.pl'); my $devnull = File::Spec->devnull; my $perl = $^X; ok (-e $devnull); ok (-e $wndepths); my $tempfile = "depths$$.txt"; ok (open FH, "$perl -MExtUtils::testlib $wndepths 2> $devnull |") or diag "Could not open pipe from $wndepths: $!"; my @depths = ; my $line = $depths[0]; chomp $line; my (undef, $wnver) = split /::/, $line; ok ($wnver); SKIP: { unless ($wnver eq '2.1' or $wnver eq '2.0' or $wnver eq '1.7.1') { skip ("Skipping tests dependent on deprecated 'version' method", 5) } for (1..$#depths) { my $line = $depths[$_]; # here we test a few top level nodes to make sure # they are the correct depth my ($pos, $offset, $depth) = split /\s+/, $line; if ($wnver eq '2.1') { # entity#n#1 if (($pos eq 'n') and (0 + '00001740' == $offset)) { is ($depth, 18); } # hold#v#3 elsif (($pos eq 'v') and (0 + '01205350' == $offset)) { is ($depth, 5); } # move#v#3 elsif (($pos eq 'v') and (0 + '01814387' == $offset)) { is ($depth, 6); } # express_emotion#v#1 elsif (($pos eq 'v') and (0 + '01785325' == $offset)) { is ($depth, 3); } # touch#v#1 elsif (($pos eq 'v') and (0 + '01194934' == $offset)) { is ($depth, 7); } } elsif ($wnver eq '2.0') { # event#n#1 if (($pos eq 'n') and (0 + '00025950' == $offset)) { is ($depth, 9); } # entity#n#1 elsif (($pos eq 'n') and (0 + '00001740' == $offset)) { is ($depth, 17); } # hold#v#3 elsif (($pos eq 'v') and (0 + '01179760' == $offset)) { is ($depth, 5); } # move#v#3 elsif (($pos eq 'v') and (0 + '01778173' == $offset)) { is ($depth, 6); } # express_emotion#v#1 elsif (($pos eq 'v') and (0 + '01750193' == $offset)) { is ($depth, 3); } } elsif ($wnver eq '1.7.1') { # event#n#1 if (($pos eq 'n') and (0 + '00021905' == $offset)) { is ($depth, 9); } # entity#n#1 elsif (($pos eq 'n') and (0 + '00001742' == $offset)) { is ($depth, 17); } # hold#v#3 elsif (($pos eq 'v') and (0 + '00961954' == $offset)) { is ($depth, 5); } # move#v#3 elsif (($pos eq 'v') and (0 + '01438226' == $offset)) { is ($depth, 6); } # express_emotion#v#1 elsif (($pos eq 'v') and (0 + '01416015' == $offset)) { is ($depth, 3); } } } } my $keydir = File::Spec->catdir ('t', 'keys'); $keydir = $opt_keydir if $opt_keydir; my $keyfile = File::Spec->catfile ($keydir, 'wndepths.txt'); my $wfile = File::Spec->catfile ($keydir, "wnver.key"); ok (open WNF, $wfile); my $wnHash = ; ok ($wnHash); $wnHash = "" if(!defined($wnHash)); $wnHash =~ s/[\r\f\n]+//g; $wnHash =~ s/^\s+//; $wnHash =~ s/\s+$//; ok (close WNF); # Since 5.8.0, Perl hash randomized its hash function. Since wnDepths.pl # stores the depths in a hash and then just dumps the depths to a file # in the order that they come out, the order of the file might change # every time wnDepths.pl is run. @depths = sort @depths; SKIP: { skip "Hash-code of key file(s) does not match installed WordNet", 1 if($wnHash ne $wnver); unless ($opt_key) { ok (open KFFH, $keyfile) or diag "Could not open $keyfile: $!"; my @keys = ; ok (close KFFH) or diag "Could not close $keyfile: $!"; is (scalar @depths, scalar @keys); for (0..$#depths) { is ($depths[$_], $keys[$_]); } } else { ok (open KFFH, '>', $keyfile) or diag "Could not open $keyfile: $!"; print KFFH @depths; ok (close KFFH); exit 0; } } __END__ WordNet-Similarity-2.05/t/utils/compounds.t0000755000175000017500000000211011020102342017043 0ustar sidsid#! /usr/bin/perl -w # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/utils/compounds.t' # A script to test the compounds.pl utility. The utility is used # to generate a list of compounds. That list is then search for # certain pre-determined compounds. use strict; use warnings; use Test::More tests => 13; BEGIN {use_ok ('File::Spec')} my $perl = $^X; my $compounds = File::Spec->catfile ("utils", "compounds.pl"); # we need a null device my $devnull = File::Spec->devnull; ok ($devnull); ok (-e $devnull); ok (-e $compounds); ok (-r $compounds); ok (-x $compounds); my $output = qx|$perl -MExtUtils::testlib $compounds 2> $devnull|; is ($? >> 8, 0) or diag "Failed to execute '$compounds' properly"; # make sure that certain compounds are found in the output like ($output, qr/\bablative_absolute\b/); like ($output, qr/\bbaseball_diamond\b/); like ($output, qr/\bhockey_stick\b/); like ($output, qr/\bvocative_case\b/); like ($output, qr/\bclassical_greek\b/); like ($output, qr/\bvulgar_latin\b/); WordNet-Similarity-2.05/t/utils/rawtextFreq.t0000755000175000017500000000761411020102357017374 0ustar sidsid#! /usr/bin/perl -w # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/utils/rawtextFreq.t' # A script to test the rawtextFreq.pl utility. We can't really test most ' # of the other infocontent programs (*Freq.pl programs) because the users # are unlikely to have all or even some of the corpora. For this test, we # just run the rawtextFreq.pl program using the GPL as input. use strict; use warnings; use Test::More tests => 29; BEGIN {use_ok 'File::Spec'} my $rawtextFreq = File::Spec->catfile ('utils', 'rawtextFreq.pl'); # where's the null device? ' my $devnull = File::Spec->devnull; my $corpus = 'GPL.txt'; my $compfile = File::Spec->catfile ('samples', 'wn30compounds.txt'); my $stopfile = File::Spec->catfile ('samples', 'stoplist.txt'); ok (-e $devnull); ok (-e $rawtextFreq); ok (-r $rawtextFreq); ok (-x $rawtextFreq); ok (-e $corpus); ok (-r $corpus); ok (-e $compfile); ok (-r $compfile); ok (-e $stopfile); ok (-r $stopfile); my $outfile = "rtout$$.txt"; my $perl = $^X; system "$perl -MExtUtils::testlib $rawtextFreq --stopfile=$stopfile --outfile=$outfile --infile=$corpus 2>$devnull"; is ($?, 0); ok (-e $outfile); ok (open FH, "$outfile") or diag "Cannot open temporary file '$outfile': $!"; my $line = ; ok ($line =~ m/^wnver::(\S+)$/); my $wnver = $1; # Test::More lets us use this nifty SKIP block for situations like this. # Here, we are checking the results only if WordNet 2.0, 1.7.1, 1.7, or 1.6 # is used, otherwise we skip it (versions 2.0 is recommended and 1.7 and # 1.6 are deprecated). This is a simple test to ensure that the frequency # counts are (probably) correct. SKIP: { my $line = ; my ($offsetpos, $freq, $isroot) = split qr/\s+/, $line; if ($wnver eq '2.1') { is ($offsetpos, '1740n'); is ($freq, 5445); ok ($isroot); } elsif ($wnver eq '2.0') { is ($offsetpos, '1740n'); is ($freq, 1678); ok ($isroot); } elsif ($wnver eq '1.7.1') { is ($offsetpos, '1742n'); is ($freq, 1601); ok ($isroot); } elsif ($wnver eq '1.7') { is ($offsetpos, '1740n'); is ($freq, 1532); ok ($isroot); } elsif ($wnver eq '1.6') { is ($offsetpos, '1740n'); is ($freq, 1122); ok ($isroot); } else { skip ("Skipping tests dependent on deprecated 'version' method", 3); } } ok (close FH); # now do it with Resnik counting system "$perl -MExtUtils::testlib $rawtextFreq --stopfile=$stopfile --resnik --outfile=$outfile --infile=$corpus 2>$devnull"; is ($?, 0); ok (-e $outfile); ok (open FH, "$outfile") or diag "Cannot open temporary file '$outfile': $!"; $line = ; ok ($line =~ m/^wnver::(\S+)$/); $wnver = $1; # see note above. The following block does essentially the same thing, # except it uses Resnik counting. SKIP: { my $line = ; my ($offsetpos, $freq, $isroot) = split qr/\s+/, $line; if ($wnver eq '2.1') { is ($offsetpos, '1740n'); # expected frequency is 1248.2174 cmp_ok ($freq, '>=', 1200.0); cmp_ok ($freq, '<=', 1249.0); ok ($isroot); } elsif ($wnver eq '2.0') { is ($offsetpos, '1740n'); # expected frequency is 568.5933 cmp_ok ($freq, '>=', 386.7); cmp_ok ($freq, '<=', 568.8); ok ($isroot); } elsif ($wnver eq '1.7.1') { is ($offsetpos, '1742n'); # expected frequency is 550.8350 cmp_ok ($freq, '>=', 373.9); cmp_ok ($freq, '<=', 374.0); ok ($isroot); } elsif ($wnver eq '1.7') { is ($offsetpos, '1740n'); # expected frequency is 534.5786 cmp_ok ($freq, '>=', 366.1); cmp_ok ($freq, '<=', 366.2); ok ($isroot); } elsif ($wnver eq '1.6') { is ($offsetpos, '1740n'); # expected frequency is 339.2464 cmp_ok ($freq, '>=', 295.9); cmp_ok ($freq, '<=', 296.0); ok ($isroot); } else { skip ("Skipping tests dependent on deprecated 'version' method", 4); } } ok (close FH); END {ok (unlink $outfile)} WordNet-Similarity-2.05/t/utils/sim.t0000755000175000017500000000413011020104753015640 0ustar sidsid#! /usr/bin/perl -w # sim.t version 2.05 # ($Id: sim.t,v 1.8 2008/05/30 23:12:43 sidz1979 Exp $) # # Copyright (C) 2004 # # Jason Michelizzi, University of Minnesota Duluth # mich0212 at d.umn.edu # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/utils/sim.t' # A script to run general tests on the similarity.pl utility. The following # tests are run: # 1) test whether similarity.pl runs correctly given two input words on # the command line # 2) test whether similarity.pl runs correctly when a configuration file # is given and two words are specified on the command line use strict; use warnings; my @measures; my $num_tests; BEGIN { @measures = qw/res lin jcn path lch wup random lesk hso/; # vector $num_tests = 6 + 6 * scalar (@measures); } use Test::More tests => $num_tests; BEGIN {use_ok ('File::Spec')} my $perl = $^X; my $similarity = File::Spec->catfile ('utils', 'similarity.pl'); # get name of null device (such as /dev/null) my $devnull = File::Spec->devnull (); ok (-e $devnull); ok (-e $similarity); ok (-r $similarity); ok (-x $similarity); @measures = map "WordNet::Similarity::$_", @measures; # run a simple test using all measures only making sure that no errors occured foreach my $measure (@measures) { my $output = qx|$perl -MExtUtils::testlib $similarity --type=$measure dog#n#1 cat#n#1 2>&1|; unlike ($output, qr/Error|Warning/i); is ($? >> 8, 0, "Exited without errors"); } my $config = "config$$.txt"; # now we run some more simple tests, this time using config files foreach my $measure (@measures) { ok (open FH, ">$config") or diag "Could not create a temporary file: $!"; print FH "$measure\n"; print FH "cache::1\n"; print FH "trace::1\n"; ok (close FH); # again, just make sure no errors occured my $output = qx|$perl -MExtUtils::testlib $similarity --type=$measure --config=$config dog#n#1 cat#n#1 2>&1|; is ($? >> 8, 0); unlike ($output, qr/Error|Warning/i); } END { ok (unlink $config); } WordNet-Similarity-2.05/t/hso.t0000755000175000017500000000727011020101142014476 0ustar sidsid#! /usr/bin/perl -w # Before `make install' is performed this script should be runnable with # `make test'. After `make install' it should work as `perl t/hso.t' ##################### We start with some black magic to print on failure. BEGIN { $| = 1; print "1..12\n"; } END {print "not ok 1\n" unless $loaded;} use WordNet::Similarity; use WordNet::QueryData; use WordNet::Similarity::hso; $loaded = 1; print "ok 1\n"; ######################### End of black magic. # Insert your test code below (better if it prints "ok 13" # (correspondingly "not ok 13") depending on the success of chunk 13 # of the test code): ############ Load QueryData $wn = WordNet::QueryData->new; if($wn) { print "ok 2\n"; } else { print "not ok 2\n"; } ############ Load hso $hso = WordNet::Similarity::hso->new($wn); if($hso) { ($err, $errString) = $hso->getError(); if($err) { print "not ok 3\n"; } else { print "ok 3\n"; } } else { print "not ok 3\n"; } ############ Load hso with undef QueryData. $badHso = WordNet::Similarity::hso->new(undef); if($badHso) { ($err, $errString) = $badHso->getError(); if($err < 2) { print "not ok 4\n"; } elsif($err == 2) { if($errString =~ /A WordNet::QueryData object is required/) { print "ok 4\n"; } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } ############ GetRelatedness of same synset. $value = $hso->getRelatedness("object#n#1", "object#n#1"); if($value && $value =~ /[0-9]+/) { if($value == 16) { print "ok 5\n"; } else { print "not ok 5\n"; } } else { print "not ok 5\n"; } ############ getRelatedness of badly formed synset. ## (Tried getRelatedness of unknown synsets... "hjxlq#n#1", "pynbr#n#2"... ## QueryData complains... can't trap that error myself.) if(defined $hso->getRelatedness("hjxlq#n", "pynbr#n")) { print "not ok 6\n"; } else { ($err, $errString) = $hso->getError(); if($err == 1) { print "ok 6\n"; } else { print "not ok 6\n"; } } ############ Test horizontal link. $hso->{'trace'} = 1; $value = $hso->getRelatedness("intelligent#a#1", "intelligence#n#1"); if(defined $value && $value =~ /[0-9]+/) { if($value == 16) { print "ok 7\n"; } else { print "not ok 7\n"; } } else { print "not ok 7\n"; } ############ Test traces. if($hso->getTraceString() !~ /Strong Rel \(Horizontal Match\)/) { print "not ok 8\n"; } else { print "ok 8\n"; } ############ Compound Match... $value = $hso->getRelatedness("school#n#1", "private_school#n#1"); if(defined $value && $value =~ /[0-9]+/) { if($value == 16) { print "ok 9\n"; } else { print "not ok 9\n"; } } else { print "not ok 9\n"; } ############ A path search... $value = $hso->getRelatedness("bumper_car#n#1", "vehicle#n#1"); if(defined $value && $value =~ /[0-9]+/) { if($value == 4) { print "ok 10\n"; } else { print "not ok 10\n"; } } else { print "not ok 10\n"; } ## Antonymy doesn't seem to be working the way it should... as is the ## case with some of the other relations. ## Wait for new version of QueryData? ## Test 11 and 12 to check that. ## Tried "query" instead of "querySense"... seems to work. ############ Check the horizontal link. $value = $hso->getRelatedness("up#a#1", "down#a#1"); if(defined $value && $value =~ /[0-9]+/) { if($value == 16) { print "ok 11\n"; } else { print "not ok 11\n"; } } else { print "not ok 11\n"; } # ############ Test traces. if($hso->getTraceString() !~ /Strong Rel \(Horizontal Match\)/) { print "not ok 12\n"; } else { print "ok 12\n"; } WordNet-Similarity-2.05/t/random.t0000755000175000017500000000415011020101610015157 0ustar sidsid#! /usr/bin/perl -w # Before `make install' is performed this script should be runnable with # `make test'. After `make install' it should work as `perl t/random.t' ##################### We start with some black magic to print on failure. BEGIN { $| = 1; print "1..7\n"; } END {print "not ok 1\n" unless $loaded;} use WordNet::Similarity; use WordNet::QueryData; use WordNet::Similarity::random; $loaded = 1; print "ok 1\n"; ######################### End of black magic. # Insert your test code below (better if it prints "ok 13" # (correspondingly "not ok 13") depending on the success of chunk 13 # of the test code): ############ Load QueryData $wn = WordNet::QueryData->new(); if($wn) { print "ok 2\n"; } else { print "not ok 2\n"; } ############ Load random $random = WordNet::Similarity::random->new($wn); if($random) { ($err, $errString) = $random->getError(); if($err) { print "not ok 3\n"; } else { print "ok 3\n"; } } else { print "not ok 3\n"; } ############ Load random with undef QueryData. $badRandom = WordNet::Similarity::random->new(undef); if($badRandom) { ($err, $errString) = $badRandom->getError(); if($err < 2) { print "not ok 4\n"; } elsif($err == 2) { if($errString =~ /A WordNet::QueryData object is required/) { print "ok 4\n"; } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } ############ GetRelatedness of same synset. $value = $random->getRelatedness("object#n#1", "object#n#1"); if($value && $value =~ /[0-9]+(\.[0-9]+)?/) { print "ok 5\n"; } else { print "not ok 5\n"; } ########### getRelatedness across parts of speech $value2 = $random->getRelatedness("pay#v#1", "money#n#1"); if($value2 && $value2 =~ /[0-9]+/) { if($value2 > 0) { print "ok 6\n"; } else { print "not ok 6\n"; } } else { print "not ok 6\n"; } ########### Test the cache. $value2 = $random->getRelatedness("object#n#1", "object#n#1"); if($value2 && $value && $value == $value2) { print "ok 7\n"; } else { print "not ok 7\n"; } WordNet-Similarity-2.05/t/access.t0000755000175000017500000000732611020101133015150 0ustar sidsid#! /usr/bin/perl -w # Before `make install' is performed this script should be runnable with # `make test'. After `make install' it should work as `perl t/access.t' ##################### We start with some black magic to print on failure. BEGIN { $| = 1; print "1..12\n"; } END {print "not ok 1\n" unless $loaded;} use WordNet::Similarity; use WordNet::QueryData 1.40; use WordNet::Similarity::jcn; use WordNet::Similarity::res; use WordNet::Similarity::lin; use WordNet::Similarity::lch; use WordNet::Similarity::hso; use WordNet::Similarity::lesk; use WordNet::Similarity::path; use WordNet::Similarity::random; use WordNet::Similarity::vector_pairs; use WordNet::Similarity::vector; use WordNet::Similarity::wup; use WordNet::Similarity::ICFinder; use WordNet::Similarity::PathFinder; use WordNet::Similarity::DepthFinder; $loaded = 1; print "ok 1\n"; ######################### End of black magic. # Insert your test code below (better if it prints "ok 13" # (correspondingly "not ok 13") depending on the success of chunk 13 # of the test code): ############ Load QueryData $wn = WordNet::QueryData->new(); if($wn) { print "ok 2\n"; } else { print "not ok 2\n"; } ############ Load jcn $jcn = WordNet::Similarity::jcn->new($wn); if($jcn) { ($err, $errString) = $jcn->getError(); if($err) { print "not ok 3\n"; } else { print "ok 3\n"; } } else { print "not ok 3\n"; } ############ Load res $res = WordNet::Similarity::res->new($wn); if($res) { ($err, $errString) = $res->getError(); if($err) { print "not ok 4\n"; } else { print "ok 4\n"; } } else { print "not ok 4\n"; } ############ Load lin $lin = WordNet::Similarity::lin->new($wn); if($lin) { ($err, $errString) = $lin->getError(); if($err) { print "not ok 5\n"; } else { print "ok 5\n"; } } else { print "not ok 5\n"; } ############ Load lch $lch = WordNet::Similarity::lch->new($wn); if($lch) { ($err, $errString) = $lch->getError(); if($err) { print "not ok 6\n"; } else { print "ok 6\n"; } } else { print "not ok 6\n"; } ############ Load hso $hso = WordNet::Similarity::hso->new($wn); if($hso) { ($err, $errString) = $hso->getError(); if($err) { print "not ok 7\n"; } else { print "ok 7\n"; } } else { print "not ok 7\n"; } ############ Load lesk $lesk = WordNet::Similarity::lesk->new($wn); if($lesk) { ($err, $errString) = $lesk->getError(); if($err) { print "not ok 8\n"; } else { print "ok 8\n"; } } else { print "not ok 8\n"; } ############ Load path $path = WordNet::Similarity::path->new($wn); if($path) { ($err, $errString) = $path->getError(); if($err) { print "not ok 9\n"; } else { print "ok 9\n"; } } else { print "not ok 9\n"; } ############ Load random $random = WordNet::Similarity::random->new($wn); if($random) { ($err, $errString) = $random->getError(); if($err) { print "not ok 10\n"; } else { print "ok 10\n"; } } else { print "not ok 10\n"; } ############ Load vector $vector = WordNet::Similarity::vector_pairs->new($wn); if($vector) { ($err, $errString) = $vector->getError(); if($err) { print "not ok 11\n"; } else { print "ok 11\n"; } } else { print "not ok 11\n"; } ########### Load real vector $vector = WordNet::Similarity::vector->new($wn); if($vector) { ($err, $errString) = $vector->getError(); if($err) { print "not ok 12\n"; } else { print "ok 12\n"; } } else { print "not ok 12\n"; } WordNet-Similarity-2.05/t/lesk.t0000755000175000017500000000454111020101211014636 0ustar sidsid#! /usr/bin/perl -w # Before `make install' is performed this script should be runnable with # `make test'. After `make install' it should work as `perl t/lesk.t' ##################### We start with some black magic to print on failure. BEGIN { $| = 1; print "1..7\n"; } END {print "not ok 1\n" unless $loaded;} use WordNet::Similarity; use WordNet::QueryData; use WordNet::Similarity::lesk; $loaded = 1; print "ok 1\n"; ######################### End of black magic. # Insert your test code below (better if it prints "ok 13" # (correspondingly "not ok 13") depending on the success of chunk 13 # of the test code): ############ Load QueryData $wn = WordNet::QueryData->new(); if($wn) { print "ok 2\n"; } else { print "not ok 2\n"; } ############ Load lesk $lesk = WordNet::Similarity::lesk->new($wn); if($lesk) { ($err, $errString) = $lesk->getError(); if($err) { print "not ok 3\n"; } else { print "ok 3\n"; } } else { print "not ok 3\n"; } ############ Load lesk with undef QueryData. $badLesk = WordNet::Similarity::lesk->new(undef); if($badLesk) { ($err, $errString) = $badLesk->getError(); if($err < 2) { print "not ok 4\n"; } elsif($err == 2) { if($errString =~ /A WordNet::QueryData object is required/) { print "ok 4\n"; } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } ############ GetRelatedness of same synset. $value = $lesk->getRelatedness("object#n#1", "object#n#1"); if($value && $value =~ /[0-9]+/) { if($value > 10) { print "ok 5\n"; } else { print "not ok 5\n"; } } else { print "not ok 5\n"; } ############ getRelatedness of badly formed synset. ## (Tried getRelatedness of unknown synsets... "hjxlq#n#1", "pynbr#n#2"... ## QueryData complains... can't trap that error myself.) if(defined $lesk->getRelatedness("hjxlq#n", "pynbr#n")) { print "not ok 6\n"; } else { ($err, $errString) = $lesk->getError(); if($err == 1) { print "ok 6\n"; } else { print "not ok 6\n"; } } ########### getRelatedness across parts of speech $value = $lesk->getRelatedness("pay#v#1", "money#n#1"); if($value && $value =~ /[0-9]+/) { if($value > 0) { print "ok 7\n"; } else { print "not ok 7\n"; } } else { print "not ok 7\n"; } WordNet-Similarity-2.05/t/pairs.t0000755000175000017500000001304211020104750015025 0ustar sidsid#! /usr/bin/perl -w # pairs.t version 2.05 # ($Id: pairs.t,v 1.12 2008/05/30 23:12:40 sidz1979 Exp $) # Copyright (C) 2004 # Jason Michelizzi, University of Minnesota Duluth # mich0212 at d.umn.edu # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # Before 'make install' is performed this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/pairs.t' # A script to run queries on a large file of words and compare the results # with a set of relatedness values stored in "keys." The list of pairs # of words was generated by the randomPairs.pl program that can be obtained # from http://www.d.umn.edu/~tpederse/wordnet.html # This script supports two options: # 1) the --key option can be used to generate a key, in which case no actual # tests are run. # 2) the --keydir option can be used to specify where the key is or should be # stored. The default value is t/keys. use strict; use warnings; use Getopt::Long; our ($opt_key, $opt_keydir); GetOptions ("key", "keydir=s"); my @measures; my $num_tests; BEGIN { @measures = qw/hso jcn lch lesk lin path res wup/; $num_tests = 21 + (5 + 109) * scalar @measures; } use Test::More tests => $num_tests; BEGIN {use_ok 'WordNet::QueryData'} BEGIN {use_ok 'WordNet::Tools'} BEGIN {use_ok 'WordNet::Similarity::hso'} BEGIN {use_ok 'WordNet::Similarity::jcn'} BEGIN {use_ok 'WordNet::Similarity::lch'} BEGIN {use_ok 'WordNet::Similarity::lesk'} BEGIN {use_ok 'WordNet::Similarity::lin'} BEGIN {use_ok 'WordNet::Similarity::path'} # There's really no point in testing random like this #BEGIN {use_ok 'WordNet::Similarity::random'} BEGIN {use_ok 'WordNet::Similarity::res'} #BEGIN {use_ok 'WordNet::Similarity::vector'} BEGIN {use_ok 'WordNet::Similarity::wup'} BEGIN {use_ok 'File::Spec'} # number of decimal places; my $precision = 4; my $wn = WordNet::QueryData->new; ok ($wn); my $wntools = WordNet::Tools->new($wn); ok ($wntools); my $wnHash = $wntools->hashCode(); ok ($wnHash); my $infile = File::Spec->catfile ('t', 'pairs.txt'); ok (-e $infile); ok (-r $infile); ok (open FH, $infile) or diag "Could not open $infile: $!"; my @lines = ; ok (close FH); my @pairs = map {my ($w1, $w2) = split; [$w1, $w2]} @lines; my $wnverfile; if ($opt_keydir) { $wnverfile = File::Spec->catfile ($opt_keydir, "wnver.key"); } else { $wnverfile = File::Spec->catfile ('t', 'keys', "wnver.key"); } ok (open KEY, $wnverfile) or diag "Could not open $wnverfile: $!"; my $wnver = ; ok (defined $wnver); $wnver = "" if(!defined($wnver)); $wnver =~ s/[\r\f\n]+//g; $wnver =~ s/^\s+//; $wnver =~ s/\s+$//; ok (close KEY); # if the key option is given, we want to generate a "key" for the tests. # A key is essentially just a list of relatedness values. If the key # option is not given, then we use the key to test if the relatedness # values we are generating correspond to the key's values. unless ($opt_key) { # not generating a key--actually running tests foreach my $measure (@measures) { SKIP: { skip "Hash-code of key file(s) does not match installed WordNet", 114 if($wnver ne $wnHash); my $keyfile; if ($opt_keydir) { $keyfile = File::Spec->catfile ($opt_keydir, "${measure}pairs.key"); } else { $keyfile = File::Spec->catfile ('t', 'keys', "${measure}pairs.key"); } ok (open KEY, $keyfile) or diag "Could not open $keyfile: $!"; my @keys = map {chomp; $_} ; is (scalar @keys, scalar @pairs); ok (close KEY); my $module = "WordNet::Similarity::$measure"->new ($wn); ok ($module); $module->{trace} = 1; my ($err, $errstr) = $module->getError (); is ($err, 0) or diag $errstr; for (0..$#pairs) { my ($word1, $word2) = ($pairs[$_]->[0], $pairs[$_]->[1]); my $score = $module->getRelatedness ($word1, $word2); my ($err, $estr) = $module->getError (); # format $score so that we can compare it to value from file $score = defined $score ? sprintf ("%.*f", $precision, $score) : 'undef'; is ($score, $keys[$_]) or diag "Wrong relatedness using $measure for $word1 $word2"; } } } } else { # generating keys my $wfile; if($opt_keydir) { $wfile = File::Spec->catfile($opt_keydir, "wnver.key"); } else { $wfile = File::Spec->catfile('t', 'keys', "wnver.key"); } ok (open KEY, ">$wfile") or diag "Could not open $wfile: $!"; print KEY "$wnHash\n"; ok (close KEY); foreach my $measure (@measures) { my $keyfile; if ($opt_keydir) { $keyfile = File::Spec->catfile ($opt_keydir, "${measure}pairs.key"); } else { $keyfile = File::Spec->catfile ('t', 'keys', "${measure}pairs.key"); } ok (open KEY, ">$keyfile") or diag "Could not open $keyfile: $!"; my $module = "WordNet::Similarity::$measure"->new ($wn); ok ($module); my ($err, $errstr) = $module->getError (); is ($err, 0) or diag "$errstr\n"; for (0..$#pairs) { my $score = $module->getRelatedness ($pairs[$_]->[0], $pairs[$_]->[1]); if (defined $score) { printf KEY "%.*f\n", $precision, $score; } else { print KEY "undef\n"; } $module->getError(); } ok (close KEY); } # hack to prevent annoying warning: when we generate keys, we skip a lot # of tests. This magic avoids an irritating warning that says something # to the effect of "looks like you planned X tests but only ran Y." SKIP: { my $num_skipped = $num_tests - 23 - 4 * scalar (@measures); skip ("Generating key, no need to run test", $num_skipped); } } __END__ WordNet-Similarity-2.05/t/wn30loop.t0000755000175000017500000000446011020104750015374 0ustar sidsid#! /usr/bin/perl -w # wn30loop.t version 2.05 # ($Id: wn30loop.t,v 1.3 2008/05/30 23:12:40 sidz1979 Exp $) # # Copyright (C) 2007 # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/wn30loop.t' use strict; use warnings; my $num_tests; my @cases; my @measures; BEGIN { @cases = (['car#n#1', 'truck#n#1'], ['choke#v#7', 'quench#v#3'], ['choke#v#7', 'hold#v#36'], ['hold#v#36', 'quench#v#3'], ['hold#v#36', 'inhibit#v#4'], ['restrain#v#1', 'inhibit#v#4']); @measures = qw/res lin jcn lch path wup vector vector_pairs lesk hso random/; $num_tests = 13 + scalar(@measures) * (1 + scalar(@cases)); } use Test::More tests => $num_tests; BEGIN {use_ok 'WordNet::QueryData'} BEGIN {use_ok 'WordNet::Similarity::res'} BEGIN {use_ok 'WordNet::Similarity::lin'} BEGIN {use_ok 'WordNet::Similarity::jcn'} BEGIN {use_ok 'WordNet::Similarity::lch'} BEGIN {use_ok 'WordNet::Similarity::path'} BEGIN {use_ok 'WordNet::Similarity::wup'} BEGIN {use_ok 'WordNet::Similarity::vector'} BEGIN {use_ok 'WordNet::Similarity::vector_pairs'} BEGIN {use_ok 'WordNet::Similarity::lesk'} BEGIN {use_ok 'WordNet::Similarity::hso'} BEGIN {use_ok 'WordNet::Similarity::random'} my $wn = WordNet::QueryData->new(); ok ($wn) or diag "Failed to load WordNet::QueryData"; foreach my $measure (@measures) { my $module = "WordNet::Similarity::$measure"->new($wn); ok ($module) or diag "Unable to load module WordNet::Similarity::$measure"; SKIP: { skip "Unable to load module. Skipping remaining tests for WordNet::Similarity::$measure", (scalar(@cases)*2) if(!$module); if($module) { foreach my $case (@cases) { my ($wps1, $wps2) = @$case; my $offset1 = $wn->offset($wps1); my $offset2 = $wn->offset($wps2); SKIP: { skip "Test sense not present in this version of WordNet. Skipping this test", 1 if(!$offset1 or !$offset2); if($offset1 and $offset2) { my $score = $module->getRelatedness($wps1, $wps2); ok (defined($score)); } } } } } } WordNet-Similarity-2.05/t/path.t0000755000175000017500000000562511020101457014654 0ustar sidsid#! /usr/bin/perl -w # Before `make install' is performed this script should be runnable with # `make test'. After `make install' it should work as `perl t/path.t' # A script to test the path-length/edge-counting (path.pm) measure. This # script runs the following tests: # # 1) tests whether the modules are constructed correctly # 2) tests whether an error is given when no WordNet::QueryData object # is supplied # 3) simply getRelatedness queries are performed on valid words, invalid # words, and words from different parts of speech ##################### We start with some black magic to print on failure. BEGIN { $| = 1; print "1..8\n"; } END {print "not ok 1\n" unless $loaded;} use WordNet::Similarity; use WordNet::QueryData; use WordNet::Similarity::path; $loaded = 1; print "ok 1\n"; ######################### End of black magic. # Insert your test code below (better if it prints "ok 13" # (correspondingly "not ok 13") depending on the success of chunk 13 # of the test code): ############ Load QueryData $wn = WordNet::QueryData->new(); if($wn) { print "ok 2\n"; } else { print "not ok 2\n"; } ############ Load path $path = WordNet::Similarity::path->new($wn); if($path) { ($err, $errString) = $path->getError(); if($err) { print "not ok 3\n"; } else { print "ok 3\n"; } } else { print "not ok 3\n"; } ############ Load path with undef QueryData. $badPath = WordNet::Similarity::path->new(undef); if($badPath) { ($err, $errString) = $badPath->getError(); if($err < 2) { print "not ok 4\n"; } elsif($err == 2) { if($errString =~ /A WordNet::QueryData object is required/) { print "ok 4\n"; } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } ############ GetRelatedness of same synset. $value = $path->getRelatedness("object#n#1", "object#n#1"); if($value && $value =~ /[0-9]/) { if($value == 1) { print "ok 5\n"; } else { print "not ok 5\n"; } } else { print "not ok 5\n"; } ############ getRelatedness of badly formed synset. ## (Tried getRelatedness of unknown synsets... "hjxlq#n#1", "pynbr#n#2"... ## QueryData complains... can't trap that error myself.) if(defined $path->getRelatedness("hjxlq#n", "pynbr#n")) { print "not ok 6\n"; } else { ($err, $errString) = $path->getError(); if($err == 1) { print "ok 6\n"; } else { print "not ok 6\n"; } } ############ Relatedness across parts of speech. $path->{'trace'} = 1; if($path->getRelatedness("object#n#1", "run#v#1") >= 0) { print "not ok 7\n"; } else { print "ok 7\n"; } ############ Test traces. # JM 1-6-04 # we changed how words from different parts of speech are handled #if($m->getTraceString() !~ /Relatedness 0 across parts of speech/) if (($path->getError())[0] != 1) { print "not ok 8\n"; } else { print "ok 8\n"; } WordNet-Similarity-2.05/t/PathFinder.t0000755000175000017500000001273111020104750015737 0ustar sidsid#! /usr/bin/perl -w # PathFinder.t version 2.05 # ($Id: PathFinder.t,v 1.12 2008/05/30 23:12:40 sidz1979 Exp $) # # Copyright (C) 2004 # # Jason Michelizzi, University of Minnesota Duluth # mich0212 at d.umn.edu # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/PathFinder.t' # A script to run tests on the WordNet::Similarity::PathFinder module. # The following are among the tests run by this script: # 1) Using offsets: # a) test the shortest path between identical synsets # b) test the shortest path between a synset and its immediate hypernym # c) various other tests of shortest path # 2) Using wps: cases are similar to the above use strict; use warnings; use Test::More tests => 21; # Tests 1, 2 BEGIN {use_ok ('WordNet::QueryData')} BEGIN {use_ok ('WordNet::Similarity::PathFinder')} # Test 3 my $wn = WordNet::QueryData->new; ok ($wn) or diag "Failed to construct WordNet::QueryData object"; # Tests 4, 5 my $module = WordNet::Similarity::PathFinder->new ($wn); ok ($module) or diag ("Failed to construct WordNet::Similarity::PathFinder object"); my ($err, $errstr) = $module->getError(); is ($err, 0) or diag "$errstr\n"; # run a few tests finding Least Common Subsumers (LCSs) my $wnver = '0.0'; $wnver = $wn->version() if($wn->can('version')); # a few variables that we'll need repeatedly ' my $offset1; my $offset2; my $word1; my $word2; my @tree1; my @tree2; my $len; my @paths; my $path; # start by testing the methods that use offsets: # writing like this: 0 + 'numeric_offset' causes the result # to be numeric--simply writing 'numeric_offset' results # in a string, which can cause problems later, and not quoting # the offset will cause Perl to think that it is an octal number # if it begins with 0. SKIP: { skip "Skipping tests for WordNet v2.1 and v2.0", 16 unless($wnver eq '2.1' || $wnver eq '2.0'); # levitation#n#1 and levitation#n#1, using offsets if($wnver eq '2.0') { $offset1 = 0 + '10670921'; $offset2 = 0 + '10670921'; } if($wnver eq '2.1') { $offset1 = 0 + '11244172'; $offset2 = 0 + '11244172'; } # Tests 6, 7, 8, 9 @tree1 = $module->_getHypernymTrees ($offset1, 'n', 'offset'); is (($module->getError())[0], 0); @tree2 = $module->_getHypernymTrees ($offset2, 'n', 'offset'); is (($module->getError())[0], 0); @paths = $module->getShortestPath ($offset1, $offset2, 'n', 'offset'); $len = $paths[0]->[0]; is (($module->getError())[0], 0); is ($len, 1); # Tests 10, 11 # organ#n#1 and sucker#n#6 if($wnver eq '2.0') { $offset1 = 0 + '04992592'; $offset2 = 0 + '02377231'; } if($wnver eq '2.1') { $offset1 = 0 + '05234428'; $offset2 = 0 + '02442989'; } @tree1 = $module->_getHypernymTrees ($offset1, 'n', 'offset'); @tree2 = $module->_getHypernymTrees ($offset2, 'n', 'offset'); @paths = $module->getShortestPath ($offset1, $offset2, 'n', 'offset'); $len = $paths[0]->[0]; is (($module->getError())[0], 0); is ($len, 2); # Tests 12, 13 # remember#v#2 and keep_note#v#1 if($wnver eq '2.0') { $offset1 = 0 + '00589832'; $offset2 = 0 + '00712981'; } if($wnver eq '2.1') { $offset1 = 0 + '00602069'; $offset2 = 0 + '00728847'; } @tree1 = $module->_getHypernymTrees ($offset1, 'v', 'offset'); @tree2 = $module->_getHypernymTrees ($offset2, 'v', 'offset'); @paths = $module->getShortestPath ($offset1, $offset2, 'v', 'offset'); $len = $paths[0]->[0]; is (($module->getError())[0], 0); is ($len, 2); # Tests 14, 15 # entity#n#1 and phenomenon#n#1 if($wnver eq '2.0') { $offset1 = 0 + '00001740'; $offset2 = 0 + '00029881'; } if($wnver eq '2.1') { $offset1 = 0 + '00001740'; $offset2 = 0 + '00032604'; } @tree1 = $module->_getHypernymTrees ($offset1, 'n', 'offset'); @tree2 = $module->_getHypernymTrees ($offset2, 'n', 'offset'); @paths = $module->getShortestPath ($offset1, $offset2, 'n', 'offset'); $len = $paths[0]->[0]; is (($module->getError())[0], 0); is ($len, 3) if($wnver eq '2.0'); is ($len, 4) if($wnver eq '2.1'); # Tests 16, 17 # sky#n#1 and anticipation#n#4 if($wnver eq '2.0') { $offset1 = 0 + '08843058'; $offset2 = 0 + '08626236'; } if($wnver eq '2.1') { $offset1 = 0 + '09301235'; $offset2 = 0 + '14293474'; } @tree1 = $module->_getHypernymTrees ($offset1, 'n', 'offset'); @tree2 = $module->_getHypernymTrees ($offset2, 'n', 'offset'); @paths = $module->getShortestPath ($offset1, $offset2, 'n', 'offset'); $len = $paths[0]->[0]; is (($module->getError ())[0], 0); is ($len, 3) if($wnver eq '2.0'); is ($len, 14) if($wnver eq '2.1'); # now try the methods that use wps strings # Tests 18, 19 # auto#n#1 and motor_vehicle#n#1 $word1 = 'auto#n#1'; $word2 = 'motor_vehicle#n#1'; @tree1 = $module->_getHypernymTrees ($word1, 'n', 'wps'); @tree2 = $module->_getHypernymTrees ($word2, 'n', 'wps'); @paths = $module->getShortestPath ($word1, $word2, 'n', 'wps'); $len = $paths[0]->[0]; is (($module->getError())[0], 0); is ($len, 2); # Tests 20, 21 # remember#v#2 and keep_note#v#1 $word1 = 'remember#v#2'; $word2 = 'keep_note#v#1'; @tree1 = $module->_getHypernymTrees ($word1, 'v', 'wps'); @tree2 = $module->_getHypernymTrees ($word2, 'v', 'wps'); @paths = $module->getShortestPath ($word1, $word2, 'n', 'wps'); $len = $paths[0]->[0]; is (($module->getError())[0], 0); is ($len, 2); } __END__ WordNet-Similarity-2.05/t/lch.t0000755000175000017500000000606111020101177014460 0ustar sidsid#! /usr/bin/perl -w # Before `make install' is performed this script should be runnable with # `make test'. After `make install' it should work as `perl t/lch.t' # A script to test the Leacock & Chodorow (lch.pm) measure. This # script runs the following tests: # # 1) tests whether the modules are constructed correctly # 2) tests whether an error is given when no WordNet::QueryData object # is supplied # 3) simple getRelatedness queries are performed on valid words, invalid # words, and words from different parts of speech # We start with some black magic to print on failure. BEGIN { $| = 1; print "1..9\n"; } END {print "not ok 1\n" unless $loaded;} use WordNet::Similarity; use WordNet::QueryData; use WordNet::Similarity::lch; $loaded = 1; print "ok 1\n"; use strict; use warnings; # Load QueryData my $wn = WordNet::QueryData->new(); if($wn) { print "ok 2\n"; } else { print "not ok 2\n"; } # Load lch my $lch = WordNet::Similarity::lch->new($wn); if($lch) { my ($err, $errString) = $lch->getError(); if($err) { print "not ok 3\n"; } else { print "ok 3\n"; } } else { print "not ok 3\n"; } # Load lch with undef QueryData. my $badLch = WordNet::Similarity::lch->new(undef); if($badLch) { my ($err, $errString) = $badLch->getError(); if($err < 2) { print "not ok 4\n"; } elsif($err == 2) { if($errString =~ /A WordNet::QueryData object is required/) { print "ok 4\n"; } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } # GetRelatedness of same synset. my $value1 = undef; my $value2 = undef; eval {$value1 = $lch->getRelatedness("object#n#1", "object#n#1");}; eval {$value2 = $lch->getRelatedness("entity#n#1", "entity#n#1");}; if($@) { print "not ok 5\n"; } elsif($value1 && $value1 =~ /[0-9]+(\.[0-9]+)?/ && $value2 && $value2 =~ /[0-9]+(\.[0-9]+)?/) { if(($value1 - $value2) < 0.0001) { print "ok 5\n"; } else { print "not ok 5\n"; } } else { print "not ok 5\n"; } # getRelatedness of badly formed synset. $value1 = undef; eval {$value1 = $lch->getRelatedness("hjxlq#n", "pynbr#n");}; if($@ || defined($value1)) { print "not ok 6\n"; } else { my ($err, $errString) = $lch->getError(); if($err == 1) { print "ok 6\n"; } else { print "not ok 6\n"; } } # Relatedness across parts of speech. $lch->{'trace'} = 1; $value1 = undef; eval {$value1 = $lch->getRelatedness("object#n#1", "run#v#1");}; if($value1 >= 0) { print "not ok 7\n"; } else { print "ok 7\n"; } # Test traces. # JM 1-6-04 # we changed how words from different parts of speech are handled #if($m->getTraceString() !~ /Relatedness 0 across parts of speech/) if(($lch->getError())[0] != 1) { print "not ok 8\n"; } else { print "ok 8\n"; } # Testing self-similarity of tilde eval {$value1 = $lch->getRelatedness("tilde#n#1", "tilde#n#1");}; if($@) { print "not ok 9\n"; } elsif(defined($value1)) { if(($value1 - $value2) < 0.0001) { print "ok 9\n"; } else { print "not ok 9\n"; } } else { print "not ok 9\n"; } WordNet-Similarity-2.05/t/keys/0000755000175000017500000000000011025552366014511 5ustar sidsidWordNet-Similarity-2.05/t/keys/0.12/0000755000175000017500000000000011025552366015071 5ustar sidsidWordNet-Similarity-2.05/t/keys/0.12/lesktrace.key0000644000175000017500000000427211015336624017561 0ustar sidsidSynset 1: dog#n#1 Synset 2: cat#n#1 Functions: glos - hype glos : 2 Overlaps: 1 x "of" 1 x "many" Functions: glos - hypo glos : 19 Overlaps: 1 x "the" 1 x "member of the genus" 1 x "in" 1 x "domesticated" Functions: holo glos - glos : 2 Overlaps: 1 x "domestic" 1 x "and" Functions: holo glos - hype glos : 1 Overlaps: 1 x "of" Functions: holo glos - hypo glos : 8 Overlaps: 1 x "genus" 1 x "domestic" 1 x "of the" 1 x "and" 1 x "wild" Functions: hype glos - glos : 1 Overlaps: 1 x "and" Functions: hype glos - hype glos : 15 Overlaps: 1 x "claws" 1 x "fissiped mammals" 1 x "with" 1 x "any of various" Functions: hype glos - hypo glos : 3 Overlaps: 1 x "of" 1 x "any" 1 x "and" Functions: hypo glos - glos : 4 Overlaps: 1 x "to" 1 x "usually" 1 x "having" 1 x "and" Functions: hypo glos - hype glos : 10 Overlaps: 1 x "with" 1 x "any of various" Functions: hypo glos - hypo glos : 15 Overlaps: 1 x "the" 1 x "small" 1 x "in the" 1 x "or" 1 x "of" 1 x "resembling the" 2 x "any" 1 x "and" Functions: mero glos - hypo glos : 1 Overlaps: 1 x "or" Synset 1: untruly#r#1 Synset 2: expositive#a#1 Synset 1: noncyclic#a#1 Synset 2: indecisive#a#2 Functions: glos - also glos : 1 Overlaps: 1 x "not" Functions: glos - glos : 1 Overlaps: 1 x "not" Synset 1: art#n#3 Synset 2: Hadrian#n#1 Functions: example - glos : 2 Overlaps: 1 x "the" 1 x "of" Functions: glos - glos : 3 Overlaps: 1 x "a" 2 x "and" Functions: hypo glos - glos : 10 Overlaps: 2 x "the" 2 x "to" 1 x "a" 1 x "in" 2 x "of" 2 x "and" Functions: hypo glos - hype glos : 2 Overlaps: 1 x "the" 1 x "of" Synset 1: mortise#v#1 Synset 2: Cystophora_cristata#n#1 Functions: glos - hype glos : 1 Overlaps: 1 x "a" Functions: hype glos - glos : 1 Overlaps: 1 x "with" Functions: hype glos - hype glos : 1 Overlaps: 1 x "with" Synset 1: polychromize#v#1 Synset 2: stress#v#2 Functions: glos - glos : 1 Overlaps: 1 x "with" Synset 1: professional_football#n#1 Synset 2: scat#n#1 Functions: glos - glos : 1 Overlaps: 1 x "for" Functions: hype glos - glos : 6 Overlaps: 2 x "the" 1 x "to" 1 x "a" 1 x "for" 1 x "of" Functions: hype glos - hype glos : 5 Overlaps: 1 x "the" 1 x "a" 1 x "in" 2 x "of" WordNet-Similarity-2.05/t/keys/0.12/lchpairs.key0000644000175000017500000000150111015336624017401 0ustar sidsid0.9445 0.4925 1.0186 0.8755 0.6391 0.8755 1.7918 0.8755 1.2809 0.6931 0.6391 0.5878 0.8755 0.6391 0.8755 0.8109 0.8109 1.0986 0.6391 1.0186 0.8755 0.9445 0.9445 0.6931 0.8109 0.7503 1.0986 0.8109 0.9445 0.9445 1.1856 0.8755 0.9445 0.8109 0.8755 1.0186 1.2809 1.1856 1.1856 0.6931 0.9445 0.9445 0.7503 0.7503 0.7503 0.8755 0.8755 0.8755 0.9445 0.9445 1.3863 1.5041 1.0986 1.0986 1.0986 0.8755 0.9445 0.8109 1.1856 0.9445 1.1350 1.3863 1.0296 1.9459 1.2528 1.3863 1.7228 1.1350 1.5404 0.6242 1.5404 1.2528 1.3863 1.1350 1.7228 1.2528 1.1350 1.0296 1.1350 1.7228 1.1350 1.3863 0.7673 1.2528 1.5404 -1000000.0000 0.6391 0.8755 0.7503 -1000000.0000 0.7503 -1000000.0000 0.8109 1.3863 -1000000.0000 0.7503 1.2528 -1000000.0000 1.2809 0.8755 1.2809 -1000000.0000 1.0186 -1000000.0000 0.8755 1.0986 1.0186 -1000000.0000 -1000000.0000 0.9445 WordNet-Similarity-2.05/t/keys/0.12/pathpairs.key0000644000175000017500000000150111015336624017567 0ustar sidsid0.0714 0.0455 0.0769 0.0667 0.0526 0.0667 0.1667 0.0667 0.1000 0.0556 0.0526 0.0500 0.0667 0.0526 0.0667 0.0625 0.0625 0.0833 0.0526 0.0769 0.0667 0.0714 0.0714 0.0556 0.0625 0.0588 0.0833 0.0625 0.0714 0.0714 0.0909 0.0667 0.0714 0.0625 0.0667 0.0769 0.1000 0.0909 0.0909 0.0556 0.0714 0.0714 0.0588 0.0588 0.0588 0.0667 0.0667 0.0667 0.0714 0.0714 0.1111 0.1250 0.0833 0.0833 0.0833 0.0667 0.0714 0.0625 0.0909 0.0714 0.1111 0.1429 0.1000 0.2500 0.1250 0.1429 0.2000 0.1111 0.1667 0.0667 0.1667 0.1250 0.1429 0.1111 0.2000 0.1250 0.1111 0.1000 0.1111 0.2000 0.1111 0.1429 0.0769 0.1250 0.1667 -1000000.0000 0.0526 0.0667 0.0588 -1000000.0000 0.0588 -1000000.0000 0.0625 0.1111 -1000000.0000 0.0588 0.1250 -1000000.0000 0.1000 0.0667 0.1000 -1000000.0000 0.0769 -1000000.0000 0.0667 0.0833 0.0769 -1000000.0000 -1000000.0000 0.0714 WordNet-Similarity-2.05/t/keys/0.12/leskpairs.key0000644000175000017500000000144011015336624017573 0ustar sidsid6.0000 5.0000 3.0000 37.0000 7.0000 5.0000 38.0000 6.0000 7.0000 47.0000 13.0000 14.0000 3.0000 2.0000 3.0000 2.0000 4.0000 7.0000 5.0000 5.0000 4.0000 9.0000 0.0000 4.0000 3.0000 7.0000 5.0000 25.0000 14.0000 15.0000 28.0000 15.0000 11.0000 20.0000 7.0000 7.0000 2.0000 8.0000 8.0000 9.0000 6.0000 8.0000 7.0000 0.0000 2.0000 13.0000 6.0000 14.0000 2.0000 1.0000 10.0000 13.0000 3.0000 5.0000 4.0000 5.0000 8.0000 3.0000 11.0000 15.0000 5.0000 3.0000 1.0000 2.0000 8.0000 7.0000 3.0000 1.0000 6.0000 0.0000 11.0000 0.0000 2.0000 1.0000 14.0000 6.0000 1.0000 5.0000 4.0000 4.0000 1.0000 3.0000 3.0000 5.0000 7.0000 0.0000 15.0000 41.0000 13.0000 3.0000 15.0000 3.0000 5.0000 3.0000 1.0000 2.0000 9.0000 2.0000 12.0000 12.0000 16.0000 1.0000 5.0000 2.0000 25.0000 11.0000 5.0000 8.0000 7.0000 10.0000 WordNet-Similarity-2.05/t/keys/0.12/jcntrace.key0000644000175000017500000000634211015336624017375 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.12/lchtrace.key0000644000175000017500000000660111015336624017367 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (Length=5) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (Length=14) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=19) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (Length=6)HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): activity#n#1 (Length=8) activity#n#1 (Length=10) activity#n#1 (Length=11) activity#n#1 (Length=12) diversion#n#1 (Length=12) activity#n#1 (Length=12) diversion#n#1 (Length=14) activity#n#1 (Length=15) *Root*#n#1 (Length=15) *Root*#n#1 (Length=16) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=20) *Root*#n#1 (Length=20)WordNet-Similarity-2.05/t/keys/0.12/jcnpairs.key0000644000175000017500000000150111015336624017405 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0424 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0408 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0502 0.0774 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0532 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0415 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.12/respairs.key0000644000175000017500000000150111015336624017424 0ustar sidsid0.0000 0.0000 0.8555 0.0000 0.0000 0.0000 4.3095 0.0000 2.5580 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.3709 1.3709 0.0000 0.0000 0.8555 0.8555 1.3709 0.0000 1.3709 0.0000 0.8555 0.0000 0.0000 0.0000 0.8555 0.0000 0.0000 0.8555 0.0000 0.0000 2.5356 2.4549 0.0000 0.0000 2.4596 0.0000 0.0000 0.0000 0.8555 0.0000 0.8555 1.3709 1.7064 0.0000 2.1429 3.7388 0.0000 0.0000 2.5580 0.0000 0.8555 0.8555 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.12/wuptrace.key0000644000175000017500000000606511015336624017440 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumers: carnivore#n#1 (Depth=11) Depth(dog#n#1) = 13 Depth(cat#n#1) = 13 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumers: *Root*#n#1 (Depth=1) Depth(art#n#3) = 6 Depth(Hadrian#n#1) = 9 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumers: *Root*#v#1 (Depth=1) Depth(polychromize#v#1) = 4 Depth(stress#v#2) = 3 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumers: diversion#n#1 (Depth=4) Depth(professional_football#n#1) = 8 Depth(scat#n#1) = 9 WordNet-Similarity-2.05/t/keys/0.12/restrace.key0000644000175000017500000000567711015336624017426 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.12/respairs_nr.key0000644000175000017500000000251611015336624020132 0ustar sidsid-1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 4.3095 -1000000.0000 2.5580 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 1.3709 -1000000.0000 -1000000.0000 0.8555 0.8555 1.3709 -1000000.0000 1.3709 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 2.5356 2.4549 -1000000.0000 -1000000.0000 2.4596 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 0.8555 1.3709 1.7064 -1000000.0000 2.1429 3.7388 -1000000.0000 -1000000.0000 2.5580 -1000000.0000 0.8555 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.12/pathtrace.key0000644000175000017500000000633411015336624017560 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Shortest path: dog#n#1 canine#n#2 carnivore#n#1 feline#n#1 cat#n#1 Path length = 5 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Shortest path: art#n#3 superior_skill#n#1 ability#n#2 cognition#n#1 psychological_feature#n#1 *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Path length = 14 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Shortest path: polychrome#v#1 color#v#1 change#v#2 *Root*#v#1 pronounce#v#1 stress#v#2 Path length = 6HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Shortest path: professional_football#n#1 sport#n#2 occupation#n#1 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 Path length = 8WordNet-Similarity-2.05/t/keys/0.12/linpairs.key0000644000175000017500000000150111015336624017415 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.12/wndepths.txt0000644000175000017500000001631011015336624017463 0ustar sidsidn 00000000 18 n 00001740 17 n 00020333 13 n 00020486 14 n 00024568 12 n 00025950 9 n 00026194 11 n 00026769 11 n 00027371 13 n 00029881 11 v 00000000 14 v 00001740 4 v 00010141 4 v 00014236 2 v 00015400 1 v 00016389 1 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0.4444 0.1667 0.1053 0.4348 0.1333 0.1111 0.1111 0.2000 0.1250 0.2222 0.3000 0.2353 0.1333 0.3333 0.5882 0.1538 0.1538 0.4211 0.1250 0.2353 0.2105 0.1667 0.1333 0.2000 0.2500 0.1818 0.4000 0.2222 0.2500 0.3333 0.2000 0.2857 0.1250 0.2857 0.2222 0.2500 0.2000 0.3333 0.2222 0.2000 0.1818 0.2000 0.3333 0.2000 0.2500 0.1429 0.2222 0.2857 -1000000.0000 0.1000 0.1250 0.1111 -1000000.0000 0.1111 -1000000.0000 0.1176 0.4286 -1000000.0000 0.1111 0.2222 -1000000.0000 0.1818 0.1250 0.1818 -1000000.0000 0.1429 -1000000.0000 0.1250 0.3529 0.1429 -1000000.0000 -1000000.0000 0.1333 WordNet-Similarity-2.05/t/keys/0.12/hsopairs.key0000644000175000017500000000140211015336624017424 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 3.0000 0.0000 0.0000 3.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.12/lintrace.key0000644000175000017500000000634211015336624017405 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.12/pathpairs_nr.key0000644000175000017500000000251611015336624020275 0ustar sidsid-1000000.0000 -1000000.0000 0.0769 -1000000.0000 -1000000.0000 -1000000.0000 0.1667 -1000000.0000 0.1000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 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-1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.0415 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.12/jcnpairs_nr.key0000644000175000017500000000251611015336624020113 0ustar sidsid-1000000.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.12/hsotrace.key0000644000175000017500000000434511015336624017415 0ustar sidsidHorizontal Links of dog#n#1: Upward Links of dog#n#1: canine#n#2 flag#n#7 Downward Links of dog#n#1: Canis#n#1 pack#n#6 pooch#n#1 cur#n#1 lapdog#n#1 toy_dog#n#1 hunting_dog#n#1 working_dog#n#1 dalmatian#n#2 basenji#n#1 pug#n#1 Leonberg#n#1 Newfoundland#n#1 Great_Pyrenees#n#1 spitz#n#1 griffon#n#1 corgi#n#1 poodle#n#1 Mexican_hairless#n#1 Horizontal Links of cat#n#1: Upward Links of cat#n#1: feline#n#1 Downward Links of cat#n#1: domestic_cat#n#1 wildcat#n#3 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] carnivore#n#1 [D] feline#n#1 [D] cat#n#1 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] paw#n#1 [D] feline#n#1 [D] cat#n#1 Horizontal Links of untruly#r#1: untrue#a#1 Upward Links of untruly#r#1: Downward Links of untruly#r#1: Horizontal Links of expository#a#1: instructive#a#1 Upward Links of expository#a#1: Downward Links of expository#a#1: Horizontal Links of noncyclic#a#1: cyclic#a#4 cyclicity#n#1 Upward Links of noncyclic#a#1: Downward Links of noncyclic#a#1: Horizontal Links of indecisive#a#2: inconclusive#a#1 irresolute#a#1 decisive#a#1 inconclusive#a#2 Upward Links of indecisive#a#2: Downward Links of indecisive#a#2: Horizontal Links of art#n#3: Upward Links of art#n#3: superior_skill#n#1 Downward Links of art#n#3: aviation#n#3 eristic#n#2 falconry#n#1 fortification#n#2 homiletics#n#2 horology#n#1 minstrelsy#n#3 musicianship#n#1 enology#n#1 puppetry#n#1 taxidermy#n#1 ventriloquism#n#1 Horizontal Links of Hadrian#n#1: Upward Links of Hadrian#n#1: Roman_Emperor#n#1 Downward Links of Hadrian#n#1: Horizontal Links of mortise#v#1: Upward Links of mortise#v#1: cut#v#1 Downward Links of mortise#v#1: Horizontal Links of hooded_seal#n#1: Upward Links of hooded_seal#n#1: earless_seal#n#1 Downward Links of hooded_seal#n#1: Cystophora#n#1Horizontal Links of polychrome#v#1: Upward Links of polychrome#v#1: color#v#1 Downward Links of polychrome#v#1: Horizontal Links of stress#v#2: Upward Links of stress#v#2: pronounce#v#1 Downward Links of stress#v#2: Horizontal Links of professional_football#n#1: Upward Links of professional_football#n#1: football#n#1 sport#n#2 Downward Links of professional_football#n#1: Horizontal Links of scat#n#1: Upward Links of scat#n#1: singing#n#1 jazz#n#2 Downward Links of scat#n#1: WordNet-Similarity-2.05/t/keys/0.11/0000755000175000017500000000000011025552366015070 5ustar sidsidWordNet-Similarity-2.05/t/keys/0.11/lesktrace.key0000644000175000017500000000434411015336624017560 0ustar sidsidSynset 1: dog#n#1 Synset 2: cat#n#1 Functions: glos - hype glos : 2 Overlaps: 1 x "of" 1 x "many" Functions: glos - hypo glos : 19 Overlaps: 1 x "member of the genus" 1 x "the" 1 x "in" 1 x "domesticated" Functions: holo glos - glos : 2 Overlaps: 1 x "domestic" 1 x "and" Functions: holo glos - hype glos : 1 Overlaps: 1 x "of" Functions: holo glos - hypo glos : 8 Overlaps: 1 x "genus" 1 x "of the" 1 x "domestic" 1 x "and" 1 x "wild" Functions: hype glos - glos : 1 Overlaps: 1 x "and" Functions: hype glos - hype glos : 15 Overlaps: 1 x "claws" 1 x "fissiped mammals" 1 x "any of various" 1 x "with" Functions: hype glos - hypo glos : 3 Overlaps: 1 x "of" 1 x "any" 1 x "and" Functions: hypo glos - glos : 4 Overlaps: 1 x "to" 1 x "usually" 1 x "and" 1 x "having" Functions: hypo glos - hype glos : 11 Overlaps: 1 x "bodied" 1 x "any of various" 1 x "with" Functions: hypo glos - hypo glos : 16 Overlaps: 1 x "small" 1 x "the" 1 x "of" 2 x "any" 1 x "in the" 1 x "or" 1 x "resembling the" 1 x "in" 1 x "and" Functions: mero glos - hypo glos : 1 Overlaps: 1 x "or" Synset 1: untruly#r#1 Synset 2: expositive#a#1 Synset 1: noncyclic#a#1 Synset 2: indecisive#a#2 Functions: glos - also glos : 1 Overlaps: 1 x "not" Functions: glos - glos : 1 Overlaps: 1 x "not" Synset 1: art#n#3 Synset 2: Hadrian#n#1 Functions: example - glos : 3 Overlaps: 1 x "the" 1 x "of" 1 x "s" Functions: glos - glos : 3 Overlaps: 1 x "a" 2 x "and" Functions: hypo glos - glos : 13 Overlaps: 3 x "the" 2 x "to" 2 x "a" 1 x "in" 2 x "of" 2 x "and" 1 x "s" Functions: hypo glos - hype glos : 2 Overlaps: 1 x "the" 1 x "of" Synset 1: mortise#v#1 Synset 2: Cystophora_cristata#n#1 Functions: glos - hype glos : 2 Overlaps: 2 x "a" Functions: hype glos - glos : 1 Overlaps: 1 x "with" Functions: hype glos - hype glos : 1 Overlaps: 1 x "with" Synset 1: polychromize#v#1 Synset 2: stress#v#2 Functions: glos - glos : 1 Overlaps: 1 x "with" Synset 1: professional_football#n#1 Synset 2: scat#n#1 Functions: glos - glos : 1 Overlaps: 1 x "for" Functions: hype glos - glos : 6 Overlaps: 2 x "the" 1 x "to" 1 x "a" 1 x "of" 1 x "for" Functions: hype glos - hype glos : 5 Overlaps: 1 x "the" 1 x "a" 1 x "in" 2 x "of" WordNet-Similarity-2.05/t/keys/0.11/lchpairs.key0000644000175000017500000000150111015336624017400 0ustar sidsid0.9445 0.4925 1.0186 0.8755 0.6391 0.8755 1.7918 0.8755 1.2809 0.6931 0.6391 0.5878 0.8755 0.6391 0.8755 0.8109 0.8109 1.0986 0.6391 1.0186 0.8755 0.9445 0.9445 0.6931 0.8109 0.7503 1.0986 0.8109 0.9445 0.9445 1.1856 0.8755 0.9445 0.8109 0.8755 1.0186 1.2809 1.1856 1.1856 0.6931 0.9445 0.9445 0.7503 0.7503 0.7503 0.8755 0.8755 0.8755 0.9445 0.9445 1.3863 1.5041 1.0986 1.0986 1.0986 0.8755 0.9445 0.8109 1.1856 0.9445 1.1350 1.3863 1.0296 1.9459 1.2528 1.3863 1.7228 1.1350 1.5404 0.6242 1.5404 1.2528 1.3863 1.1350 1.7228 1.2528 1.1350 1.0296 1.1350 1.7228 1.1350 1.3863 0.7673 1.2528 1.5404 -1000000.0000 0.6391 0.8755 0.7503 -1000000.0000 0.7503 -1000000.0000 0.8109 1.3863 -1000000.0000 0.7503 1.2528 -1000000.0000 1.2809 0.8755 1.2809 -1000000.0000 1.0186 -1000000.0000 0.8755 1.0986 1.0186 -1000000.0000 -1000000.0000 0.9445 WordNet-Similarity-2.05/t/keys/0.11/pathpairs.key0000644000175000017500000000150111015336624017566 0ustar sidsid0.0714 0.0455 0.0769 0.0667 0.0526 0.0667 0.1667 0.0667 0.1000 0.0556 0.0526 0.0500 0.0667 0.0526 0.0667 0.0625 0.0625 0.0833 0.0526 0.0769 0.0667 0.0714 0.0714 0.0556 0.0625 0.0588 0.0833 0.0625 0.0714 0.0714 0.0909 0.0667 0.0714 0.0625 0.0667 0.0769 0.1000 0.0909 0.0909 0.0556 0.0714 0.0714 0.0588 0.0588 0.0588 0.0667 0.0667 0.0667 0.0714 0.0714 0.1111 0.1250 0.0833 0.0833 0.0833 0.0667 0.0714 0.0625 0.0909 0.0714 0.1111 0.1429 0.1000 0.2500 0.1250 0.1429 0.2000 0.1111 0.1667 0.0667 0.1667 0.1250 0.1429 0.1111 0.2000 0.1250 0.1111 0.1000 0.1111 0.2000 0.1111 0.1429 0.0769 0.1250 0.1667 -1000000.0000 0.0526 0.0667 0.0588 -1000000.0000 0.0588 -1000000.0000 0.0625 0.1111 -1000000.0000 0.0588 0.1250 -1000000.0000 0.1000 0.0667 0.1000 -1000000.0000 0.0769 -1000000.0000 0.0667 0.0833 0.0769 -1000000.0000 -1000000.0000 0.0714 WordNet-Similarity-2.05/t/keys/0.11/hsopairs_nr.key0000644000175000017500000000033411015336624020125 0ustar sidsid0 0 0 0 0 0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 WordNet-Similarity-2.05/t/keys/0.11/leskpairs.key0000644000175000017500000000144511015336624017577 0ustar sidsid6.0000 5.0000 3.0000 39.0000 7.0000 5.0000 38.0000 6.0000 7.0000 50.0000 18.0000 14.0000 3.0000 2.0000 7.0000 4.0000 4.0000 11.0000 5.0000 5.0000 4.0000 10.0000 0.0000 4.0000 3.0000 8.0000 5.0000 27.0000 14.0000 15.0000 50.0000 17.0000 13.0000 25.0000 8.0000 7.0000 2.0000 9.0000 12.0000 12.0000 6.0000 8.0000 8.0000 0.0000 4.0000 14.0000 6.0000 15.0000 2.0000 1.0000 11.0000 17.0000 5.0000 5.0000 4.0000 5.0000 9.0000 3.0000 14.0000 15.0000 5.0000 3.0000 1.0000 2.0000 8.0000 7.0000 3.0000 2.0000 8.0000 0.0000 11.0000 0.0000 2.0000 1.0000 16.0000 6.0000 1.0000 5.0000 4.0000 5.0000 1.0000 4.0000 3.0000 5.0000 8.0000 0.0000 16.0000 41.0000 13.0000 3.0000 15.0000 3.0000 6.0000 3.0000 1.0000 2.0000 10.0000 2.0000 12.0000 12.0000 16.0000 1.0000 5.0000 3.0000 25.0000 11.0000 5.0000 8.0000 7.0000 12.0000 WordNet-Similarity-2.05/t/keys/0.11/jcntrace.key0000644000175000017500000000634211015336624017374 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.11/lchtrace.key0000644000175000017500000000660111015336624017366 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (Length=5) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (Length=14) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=19) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (Length=6)HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): activity#n#1 (Length=8) activity#n#1 (Length=10) activity#n#1 (Length=11) activity#n#1 (Length=12) diversion#n#1 (Length=12) activity#n#1 (Length=12) diversion#n#1 (Length=14) activity#n#1 (Length=15) *Root*#n#1 (Length=15) *Root*#n#1 (Length=16) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=20) *Root*#n#1 (Length=20)WordNet-Similarity-2.05/t/keys/0.11/jcnpairs.key0000644000175000017500000000150111015336624017404 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0424 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0408 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0502 0.0774 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0532 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0415 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.11/respairs.key0000644000175000017500000000150111015336624017423 0ustar sidsid0.0000 0.0000 0.8555 0.0000 0.0000 0.0000 4.3095 0.0000 2.5580 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.3709 1.3709 0.0000 0.0000 0.8555 0.8555 1.3709 0.0000 1.3709 0.0000 0.8555 0.0000 0.0000 0.0000 0.8555 0.0000 0.0000 0.8555 0.0000 0.0000 2.5356 2.4549 0.0000 0.0000 2.4596 0.0000 0.0000 0.0000 0.8555 0.0000 0.8555 1.3709 1.7064 0.0000 2.1429 3.7388 0.0000 0.0000 2.5580 0.0000 0.8555 0.8555 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.11/wuptrace.key0000644000175000017500000000606511015336624017437 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumers: carnivore#n#1 (Depth=11) Depth(dog#n#1) = 13 Depth(cat#n#1) = 13 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumers: *Root*#n#1 (Depth=1) Depth(art#n#3) = 6 Depth(Hadrian#n#1) = 9 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumers: *Root*#v#1 (Depth=1) Depth(polychromize#v#1) = 4 Depth(stress#v#2) = 3 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumers: diversion#n#1 (Depth=4) Depth(professional_football#n#1) = 8 Depth(scat#n#1) = 9 WordNet-Similarity-2.05/t/keys/0.11/restrace.key0000644000175000017500000000567711015336624017425 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.11/respairs_nr.key0000644000175000017500000000251611015336624020131 0ustar sidsid-1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 4.3095 -1000000.0000 2.5580 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 1.3709 -1000000.0000 -1000000.0000 0.8555 0.8555 1.3709 -1000000.0000 1.3709 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 2.5356 2.4549 -1000000.0000 -1000000.0000 2.4596 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 0.8555 1.3709 1.7064 -1000000.0000 2.1429 3.7388 -1000000.0000 -1000000.0000 2.5580 -1000000.0000 0.8555 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.11/pathtrace.key0000644000175000017500000000633411015336624017557 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Shortest path: dog#n#1 canine#n#2 carnivore#n#1 feline#n#1 cat#n#1 Path length = 5 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Shortest path: art#n#3 superior_skill#n#1 ability#n#2 cognition#n#1 psychological_feature#n#1 *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Path length = 14 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Shortest path: polychrome#v#1 color#v#1 change#v#2 *Root*#v#1 pronounce#v#1 stress#v#2 Path length = 6HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Shortest path: professional_football#n#1 sport#n#2 occupation#n#1 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 Path length = 8WordNet-Similarity-2.05/t/keys/0.11/linpairs.key0000644000175000017500000000150111015336624017414 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 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0.4444 0.1667 0.1053 0.4348 0.1333 0.1111 0.1111 0.2000 0.1250 0.2222 0.3000 0.2353 0.1333 0.3333 0.5882 0.1538 0.1538 0.4211 0.1250 0.2353 0.2105 0.1667 0.1333 0.2000 0.2500 0.1818 0.4000 0.2222 0.2500 0.3333 0.2000 0.2857 0.1250 0.2857 0.2222 0.2500 0.2000 0.3333 0.2222 0.2000 0.1818 0.2000 0.3333 0.2000 0.2500 0.1429 0.2222 0.2857 -1000000.0000 0.1000 0.1250 0.1111 -1000000.0000 0.1111 -1000000.0000 0.1176 0.4286 -1000000.0000 0.1111 0.2222 -1000000.0000 0.1818 0.1250 0.1818 -1000000.0000 0.1429 -1000000.0000 0.1250 0.3529 0.1429 -1000000.0000 -1000000.0000 0.1333 WordNet-Similarity-2.05/t/keys/0.11/hsopairs.key0000644000175000017500000000140211015336624017423 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 3.0000 0.0000 0.0000 3.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.11/lintrace.key0000644000175000017500000000634211015336624017404 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.11/pathpairs_nr.key0000644000175000017500000000251611015336624020274 0ustar sidsid-1000000.0000 -1000000.0000 0.0769 -1000000.0000 -1000000.0000 -1000000.0000 0.1667 -1000000.0000 0.1000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 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-1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.11/lchpairs_nr.key0000644000175000017500000000251611015336624020106 0ustar sidsid-1000000.0000 -1000000.0000 0.9614 -1000000.0000 -1000000.0000 -1000000.0000 1.7346 -1000000.0000 1.2238 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.7538 1.0415 -1000000.0000 -1000000.0000 0.8183 0.8873 0.8873 -1000000.0000 0.7538 -1000000.0000 1.0415 -1000000.0000 -1000000.0000 -1000000.0000 1.1285 -1000000.0000 -1000000.0000 0.7538 -1000000.0000 -1000000.0000 1.2238 1.1285 -1000000.0000 -1000000.0000 0.8873 -1000000.0000 -1000000.0000 -1000000.0000 0.6931 -1000000.0000 0.8183 0.8183 0.6931 -1000000.0000 0.8938 1.4469 -1000000.0000 -1000000.0000 1.0415 -1000000.0000 0.8873 0.7538 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 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WordNet-Similarity-2.05/t/keys/0.11/hsotrace.key0000644000175000017500000000434511015336624017414 0ustar sidsidHorizontal Links of dog#n#1: Upward Links of dog#n#1: canine#n#2 flag#n#7 Downward Links of dog#n#1: Canis#n#1 pack#n#6 pooch#n#1 cur#n#1 lapdog#n#1 toy_dog#n#1 hunting_dog#n#1 working_dog#n#1 dalmatian#n#2 basenji#n#1 pug#n#1 Leonberg#n#1 Newfoundland#n#1 Great_Pyrenees#n#1 spitz#n#1 griffon#n#1 corgi#n#1 poodle#n#1 Mexican_hairless#n#1 Horizontal Links of cat#n#1: Upward Links of cat#n#1: feline#n#1 Downward Links of cat#n#1: domestic_cat#n#1 wildcat#n#3 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] carnivore#n#1 [D] feline#n#1 [D] cat#n#1 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] paw#n#1 [D] feline#n#1 [D] cat#n#1 Horizontal Links of untruly#r#1: untrue#a#1 Upward Links of untruly#r#1: Downward Links of untruly#r#1: Horizontal Links of expository#a#1: instructive#a#1 Upward Links of expository#a#1: Downward Links of expository#a#1: Horizontal Links of noncyclic#a#1: cyclic#a#4 cyclicity#n#1 Upward Links of noncyclic#a#1: Downward Links of noncyclic#a#1: Horizontal Links of indecisive#a#2: inconclusive#a#1 irresolute#a#1 decisive#a#1 inconclusive#a#2 Upward Links of indecisive#a#2: Downward Links of indecisive#a#2: Horizontal Links of art#n#3: Upward Links of art#n#3: superior_skill#n#1 Downward Links of art#n#3: aviation#n#3 eristic#n#2 falconry#n#1 fortification#n#2 homiletics#n#2 horology#n#1 minstrelsy#n#3 musicianship#n#1 enology#n#1 puppetry#n#1 taxidermy#n#1 ventriloquism#n#1 Horizontal Links of Hadrian#n#1: Upward Links of Hadrian#n#1: Roman_Emperor#n#1 Downward Links of Hadrian#n#1: Horizontal Links of mortise#v#1: Upward Links of mortise#v#1: cut#v#1 Downward Links of mortise#v#1: Horizontal Links of hooded_seal#n#1: Upward Links of hooded_seal#n#1: earless_seal#n#1 Downward Links of hooded_seal#n#1: Cystophora#n#1Horizontal Links of polychrome#v#1: Upward Links of polychrome#v#1: color#v#1 Downward Links of polychrome#v#1: Horizontal Links of stress#v#2: Upward Links of stress#v#2: pronounce#v#1 Downward Links of stress#v#2: Horizontal Links of professional_football#n#1: Upward Links of professional_football#n#1: football#n#1 sport#n#2 Downward Links of professional_football#n#1: Horizontal Links of scat#n#1: Upward Links of scat#n#1: singing#n#1 jazz#n#2 Downward Links of scat#n#1: WordNet-Similarity-2.05/t/keys/lesktrace.key0000644000175000017500000000430711015336623017177 0ustar sidsidSynset 1: dog#n#1 Synset 2: cat#n#1 Functions: glos - hype glos : 2 Overlaps: 1 x "of" 1 x "many" Functions: glos - hypo glos : 19 Overlaps: 1 x "the" 1 x "member of the genus" 1 x "in" 1 x "domesticated" Functions: holo glos - glos : 2 Overlaps: 1 x "domestic" 1 x "and" Functions: holo glos - hype glos : 1 Overlaps: 1 x "of" Functions: holo glos - hypo glos : 8 Overlaps: 1 x "genus" 1 x "domestic" 1 x "of the" 1 x "and" 1 x "wild" Functions: hype glos - glos : 1 Overlaps: 1 x "and" Functions: hype glos - hype glos : 15 Overlaps: 1 x "claws" 1 x "fissiped mammals" 1 x "with" 1 x "any of various" Functions: hype glos - hypo glos : 4 Overlaps: 1 x "of" 2 x "any" 1 x "and" Functions: hypo glos - glos : 5 Overlaps: 1 x "to" 1 x "thick" 1 x "usually" 1 x "having" 1 x "and" Functions: hypo glos - hype glos : 10 Overlaps: 1 x "with" 1 x "any of various" Functions: hypo glos - hypo glos : 15 Overlaps: 1 x "the" 1 x "small" 1 x "in the" 1 x "or" 1 x "of" 1 x "resembling the" 2 x "any" 1 x "and" Functions: mero glos - hypo glos : 1 Overlaps: 1 x "or" Synset 1: untruly#r#1 Synset 2: expositive#a#1 Synset 1: noncyclic#a#1 Synset 2: indecisive#a#2 Functions: glos - also glos : 1 Overlaps: 1 x "not" Functions: glos - glos : 1 Overlaps: 1 x "not" Synset 1: art#n#3 Synset 2: Hadrian#n#1 Functions: example - glos : 2 Overlaps: 1 x "the" 1 x "of" Functions: glos - glos : 3 Overlaps: 1 x "a" 2 x "and" Functions: hypo glos - glos : 10 Overlaps: 2 x "the" 2 x "to" 1 x "a" 1 x "in" 2 x "of" 2 x "and" Functions: hypo glos - hype glos : 2 Overlaps: 1 x "the" 1 x "of" Synset 1: mortise#v#1 Synset 2: Cystophora_cristata#n#1 Functions: glos - hype glos : 1 Overlaps: 1 x "a" Functions: hype glos - glos : 1 Overlaps: 1 x "with" Functions: hype glos - hype glos : 1 Overlaps: 1 x "with" Synset 1: polychromize#v#1 Synset 2: stress#v#2 Functions: glos - glos : 1 Overlaps: 1 x "with" Synset 1: professional_football#n#1 Synset 2: scat#n#1 Functions: glos - glos : 1 Overlaps: 1 x "for" Functions: hype glos - glos : 6 Overlaps: 2 x "the" 1 x "to" 1 x "a" 1 x "for" 1 x "of" Functions: hype glos - hype glos : 5 Overlaps: 1 x "the" 1 x "a" 1 x "in" 2 x "of" WordNet-Similarity-2.05/t/keys/lchpairs.key0000644000175000017500000000146211015336623017026 0ustar sidsid1.1239 0.5108 1.0498 1.1239 0.9163 0.7985 1.8971 0.8557 1.3863 0.6931 0.7985 0.7444 0.9163 0.6444 0.8557 1.0498 0.9163 1.2040 0.7985 1.0498 0.9163 0.9163 1.0498 0.5978 0.7985 0.9808 1.1239 0.7985 0.9808 0.9163 1.2910 0.9808 0.9808 0.8557 1.2040 0.9163 1.3863 1.2910 1.4917 0.5978 1.0498 0.5978 0.9808 0.7444 0.7985 0.9163 0.9163 0.9808 1.2040 1.1239 1.4917 1.6094 0.9163 1.0498 1.2910 0.9163 0.9808 0.7985 1.2040 0.9163 1.2528 1.3863 1.0296 1.9459 undef 1.3863 1.2528 1.1350 1.5404 0.6242 1.5404 1.2528 1.3863 1.1350 1.7228 1.2528 1.1350 1.0296 1.1350 1.7228 1.1350 1.3863 0.7673 1.2528 1.5404 -1000000.0000 0.7444 0.8557 0.7444 -1000000.0000 0.7444 -1000000.0000 0.6931 1.4917 -1000000.0000 0.8557 1.2528 -1000000.0000 1.1239 1.0498 1.2910 -1000000.0000 0.9163 1.1239 1.2040 0.9163 -1000000.0000 -1000000.0000 0.8557 WordNet-Similarity-2.05/t/keys/pathpairs.key0000644000175000017500000000146211015336623017214 0ustar sidsid0.0769 0.0417 0.0714 0.0769 0.0625 0.0556 0.1667 0.0588 0.1000 0.0500 0.0556 0.0526 0.0625 0.0476 0.0588 0.0714 0.0625 0.0833 0.0556 0.0714 0.0625 0.0625 0.0714 0.0455 0.0556 0.0667 0.0769 0.0556 0.0667 0.0625 0.0909 0.0667 0.0667 0.0588 0.0833 0.0625 0.1000 0.0909 0.1111 0.0455 0.0714 0.0455 0.0667 0.0526 0.0556 0.0625 0.0625 0.0667 0.0833 0.0769 0.1111 0.1250 0.0625 0.0714 0.0909 0.0625 0.0667 0.0556 0.0833 0.0625 0.1250 0.1429 0.1000 0.2500 undef 0.1429 0.1250 0.1111 0.1667 0.0667 0.1667 0.1250 0.1429 0.1111 0.2000 0.1250 0.1111 0.1000 0.1111 0.2000 0.1111 0.1429 0.0769 0.1250 0.1667 -1000000.0000 0.0526 0.0588 0.0526 -1000000.0000 0.0526 -1000000.0000 0.0500 0.1111 -1000000.0000 0.0588 0.1250 -1000000.0000 0.0769 0.0714 0.0909 -1000000.0000 0.0625 0.0769 0.0833 0.0625 -1000000.0000 -1000000.0000 0.0588 WordNet-Similarity-2.05/t/keys/hsopairs_nr.key0000644000175000017500000000033411015336623017545 0ustar sidsid0 0 0 0 0 0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 WordNet-Similarity-2.05/t/keys/leskpairs.key0000644000175000017500000000143511015336623017216 0ustar sidsid6.0000 5.0000 3.0000 37.0000 7.0000 8.0000 38.0000 12.0000 10.0000 47.0000 13.0000 14.0000 3.0000 2.0000 3.0000 2.0000 4.0000 7.0000 5.0000 5.0000 4.0000 14.0000 0.0000 9.0000 4.0000 12.0000 5.0000 25.0000 14.0000 15.0000 34.0000 15.0000 11.0000 21.0000 7.0000 4.0000 2.0000 23.0000 8.0000 9.0000 7.0000 8.0000 7.0000 0.0000 2.0000 17.0000 6.0000 14.0000 2.0000 1.0000 10.0000 13.0000 3.0000 5.0000 4.0000 5.0000 8.0000 3.0000 12.0000 15.0000 1.0000 3.0000 1.0000 3.0000 undef 7.0000 0.0000 1.0000 6.0000 2.0000 11.0000 0.0000 2.0000 1.0000 15.0000 6.0000 1.0000 5.0000 4.0000 4.0000 1.0000 3.0000 3.0000 5.0000 7.0000 0.0000 18.0000 41.0000 13.0000 3.0000 15.0000 3.0000 5.0000 4.0000 1.0000 2.0000 7.0000 1.0000 11.0000 12.0000 14.0000 1.0000 8.0000 25.0000 11.0000 4.0000 8.0000 7.0000 10.0000 WordNet-Similarity-2.05/t/keys/jcntrace.key0000644000175000017500000000773211015336623017020 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 domestic_animal#n#1 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=7.254900) animal#n#1 (IC=4.741111) Concept1: dog#n#1 (IC=7.740408) Concept2: cat#n#1 (IC=8.630266) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 person#n#1 ruler#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 ruler#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): entity#n#1 (IC=0.000000) Concept1: art#n#3 (IC=9.200810) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#1 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.085132) HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 game#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#2 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#2 performance#n#2 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.885227) activity#n#1 (IC=3.382555) psychological_feature#n#1 (IC=1.779817) abstraction#n#6 (IC=0.779417) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/lchtrace.key0000644000175000017500000001025711015336623017010 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 domestic_animal#n#1 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (Length=5) animal#n#1 (Length=10) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 person#n#1 ruler#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 ruler#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): entity#n#1 (Length=15) entity#n#1 (Length=18) entity#n#1 (Length=18) entity#n#1 (Length=21) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#1 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (Length=6)HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 game#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#2 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#2 performance#n#2 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): activity#n#1 (Length=8) activity#n#1 (Length=10) activity#n#1 (Length=11) activity#n#1 (Length=12) diversion#n#1 (Length=12) activity#n#1 (Length=12) diversion#n#1 (Length=14) abstraction#n#6 (Length=14) activity#n#1 (Length=15) abstraction#n#6 (Length=15) abstraction#n#6 (Length=16) psychological_feature#n#1 (Length=16) abstraction#n#6 (Length=17) abstraction#n#6 (Length=17) abstraction#n#6 (Length=18) abstraction#n#6 (Length=18) psychological_feature#n#1 (Length=18) abstraction#n#6 (Length=19) psychological_feature#n#1 (Length=19) psychological_feature#n#1 (Length=20)WordNet-Similarity-2.05/t/keys/jcnpairs.key0000644000175000017500000000146211015336623017032 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0456 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0438 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0514 0.0792 undef 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0546 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0446 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/respairs.key0000644000175000017500000000146211015336623017051 0ustar sidsid0.7794 0.0000 1.1692 0.7794 0.7794 0.0000 3.7607 0.7794 2.4934 0.0000 0.0000 0.0000 0.7794 0.0000 0.0000 0.7794 1.3696 1.3696 0.7794 0.7794 0.6144 0.6144 1.3696 0.0000 1.1692 0.0000 0.6144 0.0000 0.0000 0.0000 0.6144 0.0000 0.0000 0.6144 0.7794 0.6144 1.9033 1.8184 0.6144 0.0000 1.8231 0.0000 0.0000 0.0000 0.6144 0.0000 0.6144 1.3696 0.7794 0.7794 2.6044 3.4451 0.0000 0.0000 2.4934 0.6144 0.6144 0.6144 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 undef 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 0.7794 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3696 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.7794 0.0000 -1000000.0000 0.0000 0.0000 1.3696 0.0000 -1000000.0000 -1000000.0000 0.7794 WordNet-Similarity-2.05/t/keys/wuptrace.key0000644000175000017500000000735311015336623017060 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 domestic_animal#n#1 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumers: carnivore#n#1 (Depth=13) Depth(dog#n#1) = 15 Depth(cat#n#1) = 15 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 person#n#1 ruler#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 ruler#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumers: entity#n#1 (Depth=2) Depth(art#n#3) = 8 Depth(Hadrian#n#1) = 10 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#1 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumers: *Root*#v#1 (Depth=1) Depth(polychromize#v#1) = 4 Depth(stress#v#2) = 3 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 game#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#2 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#2 performance#n#2 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumers: diversion#n#1 (Depth=8) Depth(professional_football#n#1) = 12 Depth(scat#n#1) = 13 WordNet-Similarity-2.05/t/keys/restrace.key0000644000175000017500000000727011015336623017034 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 domestic_animal#n#1 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=7.254900) animal#n#1 (IC=4.741111) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 person#n#1 ruler#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 ruler#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): entity#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#1 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 game#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#2 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#2 performance#n#2 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.885227) activity#n#1 (IC=3.382555) psychological_feature#n#1 (IC=1.779817) abstraction#n#6 (IC=0.779417) WordNet-Similarity-2.05/t/keys/0.14/0000755000175000017500000000000011025552366015073 5ustar sidsidWordNet-Similarity-2.05/t/keys/0.14/lesktrace.key0000644000175000017500000000427211015336624017563 0ustar sidsidSynset 1: dog#n#1 Synset 2: cat#n#1 Functions: glos - hype glos : 2 Overlaps: 1 x "of" 1 x "many" Functions: glos - hypo glos : 19 Overlaps: 1 x "the" 1 x "member of the genus" 1 x "in" 1 x "domesticated" Functions: holo glos - glos : 2 Overlaps: 1 x "domestic" 1 x "and" Functions: holo glos - hype glos : 1 Overlaps: 1 x "of" Functions: holo glos - hypo glos : 8 Overlaps: 1 x "genus" 1 x "domestic" 1 x "of the" 1 x "and" 1 x "wild" Functions: hype glos - glos : 1 Overlaps: 1 x "and" Functions: hype glos - hype glos : 15 Overlaps: 1 x "claws" 1 x "fissiped mammals" 1 x "with" 1 x "any of various" Functions: hype glos - hypo glos : 3 Overlaps: 1 x "of" 1 x "any" 1 x "and" Functions: hypo glos - glos : 4 Overlaps: 1 x "to" 1 x "usually" 1 x "having" 1 x "and" Functions: hypo glos - hype glos : 10 Overlaps: 1 x "with" 1 x "any of various" Functions: hypo glos - hypo glos : 15 Overlaps: 1 x "the" 1 x "small" 1 x "in the" 1 x "or" 1 x "of" 1 x "resembling the" 2 x "any" 1 x "and" Functions: mero glos - hypo glos : 1 Overlaps: 1 x "or" Synset 1: untruly#r#1 Synset 2: expositive#a#1 Synset 1: noncyclic#a#1 Synset 2: indecisive#a#2 Functions: glos - also glos : 1 Overlaps: 1 x "not" Functions: glos - glos : 1 Overlaps: 1 x "not" Synset 1: art#n#3 Synset 2: Hadrian#n#1 Functions: example - glos : 2 Overlaps: 1 x "the" 1 x "of" Functions: glos - glos : 3 Overlaps: 1 x "a" 2 x "and" Functions: hypo glos - glos : 10 Overlaps: 2 x "the" 2 x "to" 1 x "a" 1 x "in" 2 x "of" 2 x "and" Functions: hypo glos - hype glos : 2 Overlaps: 1 x "the" 1 x "of" Synset 1: mortise#v#1 Synset 2: Cystophora_cristata#n#1 Functions: glos - hype glos : 1 Overlaps: 1 x "a" Functions: hype glos - glos : 1 Overlaps: 1 x "with" Functions: hype glos - hype glos : 1 Overlaps: 1 x "with" Synset 1: polychromize#v#1 Synset 2: stress#v#2 Functions: glos - glos : 1 Overlaps: 1 x "with" Synset 1: professional_football#n#1 Synset 2: scat#n#1 Functions: glos - glos : 1 Overlaps: 1 x "for" Functions: hype glos - glos : 6 Overlaps: 2 x "the" 1 x "to" 1 x "a" 1 x "for" 1 x "of" Functions: hype glos - hype glos : 5 Overlaps: 1 x "the" 1 x "a" 1 x "in" 2 x "of" WordNet-Similarity-2.05/t/keys/0.14/lchpairs.key0000644000175000017500000000150111015336624017403 0ustar sidsid0.9445 0.4925 1.0186 0.8755 0.6391 0.8755 1.7918 0.8755 1.2809 0.6931 0.6391 0.5878 0.8755 0.6391 0.8755 0.8109 0.8109 1.0986 0.6391 1.0186 0.8755 0.9445 0.9445 0.6931 0.8109 0.7503 1.0986 0.8109 0.9445 0.9445 1.1856 0.8755 0.9445 0.8109 0.8755 1.0186 1.2809 1.1856 1.1856 0.6931 0.9445 0.9445 0.7503 0.7503 0.7503 0.8755 0.8755 0.8755 0.9445 0.9445 1.3863 1.5041 1.0986 1.0986 1.0986 0.8755 0.9445 0.8109 1.1856 0.9445 1.1350 1.3863 1.0296 1.9459 1.2528 1.3863 1.7228 1.1350 1.5404 0.6242 1.5404 1.2528 1.3863 1.1350 1.7228 1.2528 1.1350 1.0296 1.1350 1.7228 1.1350 1.3863 0.7673 1.2528 1.5404 -1000000.0000 0.6391 0.8755 0.7503 -1000000.0000 0.7503 -1000000.0000 0.8109 1.3863 -1000000.0000 0.7503 1.2528 -1000000.0000 1.2809 0.8755 1.2809 -1000000.0000 1.0186 -1000000.0000 0.8755 1.0986 1.0186 -1000000.0000 -1000000.0000 0.9445 WordNet-Similarity-2.05/t/keys/0.14/pathpairs.key0000644000175000017500000000150111015336624017571 0ustar sidsid0.0714 0.0455 0.0769 0.0667 0.0526 0.0667 0.1667 0.0667 0.1000 0.0556 0.0526 0.0500 0.0667 0.0526 0.0667 0.0625 0.0625 0.0833 0.0526 0.0769 0.0667 0.0714 0.0714 0.0556 0.0625 0.0588 0.0833 0.0625 0.0714 0.0714 0.0909 0.0667 0.0714 0.0625 0.0667 0.0769 0.1000 0.0909 0.0909 0.0556 0.0714 0.0714 0.0588 0.0588 0.0588 0.0667 0.0667 0.0667 0.0714 0.0714 0.1111 0.1250 0.0833 0.0833 0.0833 0.0667 0.0714 0.0625 0.0909 0.0714 0.1111 0.1429 0.1000 0.2500 0.1250 0.1429 0.2000 0.1111 0.1667 0.0667 0.1667 0.1250 0.1429 0.1111 0.2000 0.1250 0.1111 0.1000 0.1111 0.2000 0.1111 0.1429 0.0769 0.1250 0.1667 -1000000.0000 0.0526 0.0667 0.0588 -1000000.0000 0.0588 -1000000.0000 0.0625 0.1111 -1000000.0000 0.0588 0.1250 -1000000.0000 0.1000 0.0667 0.1000 -1000000.0000 0.0769 -1000000.0000 0.0667 0.0833 0.0769 -1000000.0000 -1000000.0000 0.0714 WordNet-Similarity-2.05/t/keys/0.14/hsopairs_nr.key0000644000175000017500000000033411015336624020130 0ustar sidsid0 0 0 0 0 0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 WordNet-Similarity-2.05/t/keys/0.14/leskpairs.key0000644000175000017500000000144011015336624017575 0ustar sidsid6.0000 5.0000 3.0000 37.0000 7.0000 5.0000 38.0000 6.0000 7.0000 47.0000 13.0000 14.0000 3.0000 2.0000 3.0000 2.0000 4.0000 7.0000 5.0000 5.0000 4.0000 9.0000 0.0000 4.0000 3.0000 7.0000 5.0000 25.0000 14.0000 15.0000 28.0000 15.0000 11.0000 20.0000 7.0000 7.0000 2.0000 8.0000 8.0000 9.0000 6.0000 8.0000 7.0000 0.0000 2.0000 13.0000 6.0000 14.0000 2.0000 1.0000 10.0000 13.0000 3.0000 5.0000 4.0000 5.0000 8.0000 3.0000 11.0000 15.0000 5.0000 3.0000 1.0000 2.0000 8.0000 7.0000 3.0000 1.0000 6.0000 0.0000 11.0000 0.0000 2.0000 1.0000 14.0000 6.0000 1.0000 5.0000 4.0000 4.0000 1.0000 3.0000 3.0000 5.0000 7.0000 0.0000 15.0000 41.0000 13.0000 3.0000 15.0000 3.0000 5.0000 3.0000 1.0000 2.0000 9.0000 2.0000 12.0000 12.0000 16.0000 1.0000 5.0000 2.0000 25.0000 11.0000 5.0000 8.0000 7.0000 10.0000 WordNet-Similarity-2.05/t/keys/0.14/jcntrace.key0000644000175000017500000000634211015336624017377 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.14/lchtrace.key0000644000175000017500000000660111015336624017371 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (Length=5) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (Length=14) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=19) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (Length=6)HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): activity#n#1 (Length=8) activity#n#1 (Length=10) activity#n#1 (Length=11) activity#n#1 (Length=12) diversion#n#1 (Length=12) activity#n#1 (Length=12) diversion#n#1 (Length=14) activity#n#1 (Length=15) *Root*#n#1 (Length=15) *Root*#n#1 (Length=16) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=20) *Root*#n#1 (Length=20)WordNet-Similarity-2.05/t/keys/0.14/jcnpairs.key0000644000175000017500000000150111015336624017407 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0424 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0408 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0502 0.0774 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0532 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0415 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.14/respairs.key0000644000175000017500000000150111015336624017426 0ustar sidsid0.0000 0.0000 0.8555 0.0000 0.0000 0.0000 4.3095 0.0000 2.5580 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.3709 1.3709 0.0000 0.0000 0.8555 0.8555 1.3709 0.0000 1.3709 0.0000 0.8555 0.0000 0.0000 0.0000 0.8555 0.0000 0.0000 0.8555 0.0000 0.0000 2.5356 2.4549 0.0000 0.0000 2.4596 0.0000 0.0000 0.0000 0.8555 0.0000 0.8555 1.3709 1.7064 0.0000 2.1429 3.7388 0.0000 0.0000 2.5580 0.0000 0.8555 0.8555 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.14/wuptrace.key0000644000175000017500000000606511015336624017442 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumers: carnivore#n#1 (Depth=11) Depth(dog#n#1) = 13 Depth(cat#n#1) = 13 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumers: *Root*#n#1 (Depth=1) Depth(art#n#3) = 6 Depth(Hadrian#n#1) = 9 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumers: *Root*#v#1 (Depth=1) Depth(polychromize#v#1) = 4 Depth(stress#v#2) = 3 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumers: diversion#n#1 (Depth=4) Depth(professional_football#n#1) = 8 Depth(scat#n#1) = 9 WordNet-Similarity-2.05/t/keys/0.14/restrace.key0000644000175000017500000000567711015336624017430 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.14/respairs_nr.key0000644000175000017500000000251611015336624020134 0ustar sidsid-1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 4.3095 -1000000.0000 2.5580 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 1.3709 -1000000.0000 -1000000.0000 0.8555 0.8555 1.3709 -1000000.0000 1.3709 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 2.5356 2.4549 -1000000.0000 -1000000.0000 2.4596 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 0.8555 1.3709 1.7064 -1000000.0000 2.1429 3.7388 -1000000.0000 -1000000.0000 2.5580 -1000000.0000 0.8555 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.14/pathtrace.key0000644000175000017500000000633411015336624017562 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Shortest path: dog#n#1 canine#n#2 carnivore#n#1 feline#n#1 cat#n#1 Path length = 5 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Shortest path: art#n#3 superior_skill#n#1 ability#n#2 cognition#n#1 psychological_feature#n#1 *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Path length = 14 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Shortest path: polychrome#v#1 color#v#1 change#v#2 *Root*#v#1 pronounce#v#1 stress#v#2 Path length = 6HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Shortest path: professional_football#n#1 sport#n#2 occupation#n#1 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 Path length = 8WordNet-Similarity-2.05/t/keys/0.14/linpairs.key0000644000175000017500000000150111015336624017417 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 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-1000000.0000 0.1429 -1000000.0000 0.1250 0.3529 0.1429 -1000000.0000 -1000000.0000 0.1333 WordNet-Similarity-2.05/t/keys/0.14/hsopairs.key0000644000175000017500000000140211015336624017426 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 3.0000 0.0000 0.0000 3.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.14/lintrace.key0000644000175000017500000000634211015336624017407 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.14/pathpairs_nr.key0000644000175000017500000000251611015336624020277 0ustar sidsid-1000000.0000 -1000000.0000 0.0769 -1000000.0000 -1000000.0000 -1000000.0000 0.1667 -1000000.0000 0.1000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0625 0.0833 -1000000.0000 -1000000.0000 0.0667 0.0714 0.0714 -1000000.0000 0.0625 -1000000.0000 0.0833 -1000000.0000 -1000000.0000 -1000000.0000 0.0909 -1000000.0000 -1000000.0000 0.0625 -1000000.0000 -1000000.0000 0.1000 0.0909 -1000000.0000 -1000000.0000 0.0714 -1000000.0000 -1000000.0000 -1000000.0000 0.0588 -1000000.0000 0.0667 0.0667 0.0714 -1000000.0000 0.1111 0.1250 -1000000.0000 -1000000.0000 0.0833 -1000000.0000 0.0714 0.0625 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.1111 -1000000.0000 -1000000.0000 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-1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.14/lchpairs_nr.key0000644000175000017500000000251611015336624020111 0ustar sidsid-1000000.0000 -1000000.0000 0.9614 -1000000.0000 -1000000.0000 -1000000.0000 1.7346 -1000000.0000 1.2238 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.7538 1.0415 -1000000.0000 -1000000.0000 0.8183 0.8873 0.8873 -1000000.0000 0.7538 -1000000.0000 1.0415 -1000000.0000 -1000000.0000 -1000000.0000 1.1285 -1000000.0000 -1000000.0000 0.7538 -1000000.0000 -1000000.0000 1.2238 1.1285 -1000000.0000 -1000000.0000 0.8873 -1000000.0000 -1000000.0000 -1000000.0000 0.6931 -1000000.0000 0.8183 0.8183 0.6931 -1000000.0000 0.8938 1.4469 -1000000.0000 -1000000.0000 1.0415 -1000000.0000 0.8873 0.7538 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3291 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.0415 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.14/jcnpairs_nr.key0000644000175000017500000000251611015336624020115 0ustar sidsid-1000000.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.14/hsotrace.key0000644000175000017500000000434511015336624017417 0ustar sidsidHorizontal Links of dog#n#1: Upward Links of dog#n#1: canine#n#2 flag#n#7 Downward Links of dog#n#1: Canis#n#1 pack#n#6 pooch#n#1 cur#n#1 lapdog#n#1 toy_dog#n#1 hunting_dog#n#1 working_dog#n#1 dalmatian#n#2 basenji#n#1 pug#n#1 Leonberg#n#1 Newfoundland#n#1 Great_Pyrenees#n#1 spitz#n#1 griffon#n#1 corgi#n#1 poodle#n#1 Mexican_hairless#n#1 Horizontal Links of cat#n#1: Upward Links of cat#n#1: feline#n#1 Downward Links of cat#n#1: domestic_cat#n#1 wildcat#n#3 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] carnivore#n#1 [D] feline#n#1 [D] cat#n#1 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] paw#n#1 [D] feline#n#1 [D] cat#n#1 Horizontal Links of untruly#r#1: untrue#a#1 Upward Links of untruly#r#1: Downward Links of untruly#r#1: Horizontal Links of expository#a#1: instructive#a#1 Upward Links of expository#a#1: Downward Links of expository#a#1: Horizontal Links of noncyclic#a#1: cyclic#a#4 cyclicity#n#1 Upward Links of noncyclic#a#1: Downward Links of noncyclic#a#1: Horizontal Links of indecisive#a#2: inconclusive#a#1 irresolute#a#1 decisive#a#1 inconclusive#a#2 Upward Links of indecisive#a#2: Downward Links of indecisive#a#2: Horizontal Links of art#n#3: Upward Links of art#n#3: superior_skill#n#1 Downward Links of art#n#3: aviation#n#3 eristic#n#2 falconry#n#1 fortification#n#2 homiletics#n#2 horology#n#1 minstrelsy#n#3 musicianship#n#1 enology#n#1 puppetry#n#1 taxidermy#n#1 ventriloquism#n#1 Horizontal Links of Hadrian#n#1: Upward Links of Hadrian#n#1: Roman_Emperor#n#1 Downward Links of Hadrian#n#1: Horizontal Links of mortise#v#1: Upward Links of mortise#v#1: cut#v#1 Downward Links of mortise#v#1: Horizontal Links of hooded_seal#n#1: Upward Links of hooded_seal#n#1: earless_seal#n#1 Downward Links of hooded_seal#n#1: Cystophora#n#1Horizontal Links of polychrome#v#1: Upward Links of polychrome#v#1: color#v#1 Downward Links of polychrome#v#1: Horizontal Links of stress#v#2: Upward Links of stress#v#2: pronounce#v#1 Downward Links of stress#v#2: Horizontal Links of professional_football#n#1: Upward Links of professional_football#n#1: football#n#1 sport#n#2 Downward Links of professional_football#n#1: Horizontal Links of scat#n#1: Upward Links of scat#n#1: singing#n#1 jazz#n#2 Downward Links of scat#n#1: WordNet-Similarity-2.05/t/keys/1.04/0000755000175000017500000000000011025552366015073 5ustar sidsidWordNet-Similarity-2.05/t/keys/1.04/lesktrace.key0000644000175000017500000000430711015336624017562 0ustar sidsidSynset 1: dog#n#1 Synset 2: cat#n#1 Functions: glos - hype glos : 2 Overlaps: 1 x "of" 1 x "many" Functions: glos - hypo glos : 19 Overlaps: 1 x "the" 1 x "member of the genus" 1 x "in" 1 x "domesticated" Functions: holo glos - glos : 2 Overlaps: 1 x "domestic" 1 x "and" Functions: holo glos - hype glos : 1 Overlaps: 1 x "of" Functions: holo glos - hypo glos : 8 Overlaps: 1 x "genus" 1 x "domestic" 1 x "of the" 1 x "and" 1 x "wild" Functions: hype glos - glos : 1 Overlaps: 1 x "and" Functions: hype glos - hype glos : 15 Overlaps: 1 x "claws" 1 x "fissiped mammals" 1 x "with" 1 x "any of various" Functions: hype glos - hypo glos : 3 Overlaps: 1 x "of" 1 x "any" 1 x "and" Functions: hypo glos - glos : 5 Overlaps: 1 x "to" 1 x "thick" 1 x "usually" 1 x "having" 1 x "and" Functions: hypo glos - hype glos : 10 Overlaps: 1 x "with" 1 x "any of various" Functions: hypo glos - hypo glos : 15 Overlaps: 1 x "the" 1 x "small" 1 x "in the" 1 x "or" 1 x "of" 1 x "resembling the" 2 x "any" 1 x "and" Functions: mero glos - hypo glos : 1 Overlaps: 1 x "or" Synset 1: untruly#r#1 Synset 2: expositive#a#1 Synset 1: noncyclic#a#1 Synset 2: indecisive#a#2 Functions: glos - also glos : 1 Overlaps: 1 x "not" Functions: glos - glos : 1 Overlaps: 1 x "not" Synset 1: art#n#3 Synset 2: Hadrian#n#1 Functions: example - glos : 2 Overlaps: 1 x "the" 1 x "of" Functions: glos - glos : 3 Overlaps: 1 x "a" 2 x "and" Functions: hypo glos - glos : 10 Overlaps: 2 x "the" 2 x "to" 1 x "a" 1 x "in" 2 x "of" 2 x "and" Functions: hypo glos - hype glos : 2 Overlaps: 1 x "the" 1 x "of" Synset 1: mortise#v#1 Synset 2: Cystophora_cristata#n#1 Functions: glos - hype glos : 1 Overlaps: 1 x "a" Functions: hype glos - glos : 1 Overlaps: 1 x "with" Functions: hype glos - hype glos : 1 Overlaps: 1 x "with" Synset 1: polychromize#v#1 Synset 2: stress#v#2 Functions: glos - glos : 1 Overlaps: 1 x "with" Synset 1: professional_football#n#1 Synset 2: scat#n#1 Functions: glos - glos : 1 Overlaps: 1 x "for" Functions: hype glos - glos : 6 Overlaps: 2 x "the" 1 x "to" 1 x "a" 1 x "for" 1 x "of" Functions: hype glos - hype glos : 5 Overlaps: 1 x "the" 1 x "a" 1 x "in" 2 x "of" WordNet-Similarity-2.05/t/keys/1.04/lchpairs.key0000644000175000017500000000146311015336624017412 0ustar sidsid1.0726 0.4595 0.9985 1.0726 0.8650 0.7472 1.8458 0.8044 1.3350 0.6419 0.6931 0.6419 0.8650 0.5931 0.7472 0.9985 0.8044 1.0726 0.8044 0.9985 0.9295 0.9295 0.9985 0.5021 0.8044 0.8650 1.0726 0.6931 0.8650 0.8650 1.2397 0.8650 0.8650 0.8650 1.1527 0.7472 1.3350 1.2397 1.4404 0.5465 0.9985 0.5465 0.8650 0.6419 0.8044 0.6931 0.9295 0.8650 1.1527 0.9295 1.4404 1.5581 0.8650 0.9295 1.1527 0.9295 0.9295 0.8650 1.0726 0.8044 1.1350 1.3863 1.0296 1.9459 1.2528 1.3863 1.2528 1.1350 1.5404 0.6242 1.5404 1.2528 1.3863 1.1350 1.7228 1.2528 1.1350 1.0296 1.1350 1.7228 1.1350 1.3863 0.7673 1.2528 1.5404 -1000000.0000 0.6931 0.8044 0.6419 -1000000.0000 0.6419 -1000000.0000 0.5931 1.3350 -1000000.0000 0.7472 1.2528 -1000000.0000 0.9985 0.9985 1.1527 -1000000.0000 0.8044 0.9985 1.1527 0.8044 -1000000.0000 -1000000.0000 0.8650 WordNet-Similarity-2.05/t/keys/1.04/pathpairs.key0000644000175000017500000000146311015336624017600 0ustar sidsid0.0769 0.0417 0.0714 0.0769 0.0625 0.0556 0.1667 0.0588 0.1000 0.0500 0.0526 0.0500 0.0625 0.0476 0.0556 0.0714 0.0588 0.0769 0.0588 0.0714 0.0667 0.0667 0.0714 0.0435 0.0588 0.0625 0.0769 0.0526 0.0625 0.0625 0.0909 0.0625 0.0625 0.0625 0.0833 0.0556 0.1000 0.0909 0.1111 0.0455 0.0714 0.0455 0.0625 0.0500 0.0588 0.0526 0.0667 0.0625 0.0833 0.0667 0.1111 0.1250 0.0625 0.0667 0.0833 0.0667 0.0667 0.0625 0.0769 0.0588 0.1111 0.1429 0.1000 0.2500 0.1250 0.1429 0.1250 0.1111 0.1667 0.0667 0.1667 0.1250 0.1429 0.1111 0.2000 0.1250 0.1111 0.1000 0.1111 0.2000 0.1111 0.1429 0.0769 0.1250 0.1667 -1000000.0000 0.0526 0.0588 0.0500 -1000000.0000 0.0500 -1000000.0000 0.0476 0.1000 -1000000.0000 0.0556 0.1250 -1000000.0000 0.0714 0.0714 0.0833 -1000000.0000 0.0588 0.0714 0.0833 0.0588 -1000000.0000 -1000000.0000 0.0625 WordNet-Similarity-2.05/t/keys/1.04/hsopairs_nr.key0000644000175000017500000000033411015336624020130 0ustar sidsid0 0 0 0 0 0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 WordNet-Similarity-2.05/t/keys/1.04/leskpairs.key0000644000175000017500000000143511015336624017601 0ustar sidsid6.0000 5.0000 3.0000 37.0000 7.0000 8.0000 38.0000 12.0000 7.0000 47.0000 13.0000 14.0000 3.0000 2.0000 3.0000 2.0000 4.0000 7.0000 5.0000 5.0000 4.0000 14.0000 0.0000 9.0000 4.0000 12.0000 5.0000 25.0000 14.0000 15.0000 34.0000 15.0000 11.0000 20.0000 7.0000 7.0000 2.0000 23.0000 8.0000 9.0000 7.0000 8.0000 7.0000 0.0000 2.0000 16.0000 6.0000 14.0000 2.0000 1.0000 10.0000 13.0000 3.0000 5.0000 4.0000 5.0000 8.0000 3.0000 12.0000 15.0000 5.0000 3.0000 1.0000 3.0000 8.0000 7.0000 0.0000 1.0000 6.0000 0.0000 11.0000 0.0000 2.0000 1.0000 14.0000 6.0000 1.0000 5.0000 4.0000 4.0000 1.0000 3.0000 3.0000 5.0000 7.0000 0.0000 18.0000 41.0000 13.0000 3.0000 15.0000 3.0000 5.0000 3.0000 1.0000 2.0000 9.0000 1.0000 11.0000 12.0000 16.0000 1.0000 5.0000 25.0000 11.0000 4.0000 8.0000 7.0000 10.0000 WordNet-Similarity-2.05/t/keys/1.04/jcntrace.key0000644000175000017500000000772211015336624017402 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=7.993246) Concept1: dog#n#1 (IC=8.489683) Concept2: cat#n#1 (IC=9.336980) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): entity#n#1 (IC=0.000000) Concept1: art#n#3 (IC=9.950085) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.327881) HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#2 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.840473) activity#n#1 (IC=2.884734) psychological_feature#n#1 (IC=1.402631) abstraction#n#6 (IC=0.516189) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/1.04/lchtrace.key0000644000175000017500000001025111015336624017365 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (Length=5) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): entity#n#1 (Length=16) entity#n#1 (Length=18) entity#n#1 (Length=19) entity#n#1 (Length=21) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (Length=6)HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#2 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): activity#n#1 (Length=8) activity#n#1 (Length=10) activity#n#1 (Length=11) activity#n#1 (Length=12) diversion#n#1 (Length=12) activity#n#1 (Length=12) diversion#n#1 (Length=14) abstraction#n#6 (Length=14) activity#n#1 (Length=15) abstraction#n#6 (Length=15) abstraction#n#6 (Length=16) psychological_feature#n#1 (Length=16) abstraction#n#6 (Length=17) abstraction#n#6 (Length=17) abstraction#n#6 (Length=18) abstraction#n#6 (Length=18) psychological_feature#n#1 (Length=18) abstraction#n#6 (Length=19) psychological_feature#n#1 (Length=19) psychological_feature#n#1 (Length=20)WordNet-Similarity-2.05/t/keys/1.04/jcnpairs.key0000644000175000017500000000146311015336624017416 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0427 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0411 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0502 0.0774 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0532 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0418 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/1.04/respairs.key0000644000175000017500000000146311015336624017435 0ustar sidsid0.5162 0.0000 1.4362 0.5162 0.5162 0.0000 4.2270 0.0000 2.4760 0.0000 0.0000 0.0000 0.5162 0.0000 0.0000 0.5162 1.4362 1.4362 0.5162 0.5162 0.9091 0.9091 1.4362 0.0000 1.4362 0.0000 0.9091 0.0000 0.0000 0.0000 0.9091 0.0000 0.0000 0.9091 0.5162 0.0000 2.4185 2.3401 0.9091 0.0000 2.3447 0.0000 0.0000 0.0000 0.9091 0.0000 0.9091 1.4362 0.5162 0.5162 2.0614 3.6545 0.0000 0.0000 2.4760 0.9091 0.9091 0.9091 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 0.5162 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.4362 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.5162 0.0000 -1000000.0000 0.0000 0.0000 2.3678 0.0000 -1000000.0000 -1000000.0000 0.5162 WordNet-Similarity-2.05/t/keys/1.04/wuptrace.key0000644000175000017500000000737411015336624017446 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumers: carnivore#n#1 (Depth=12) Depth(dog#n#1) = 14 Depth(cat#n#1) = 14 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumers: entity#n#1 (Depth=2) Depth(art#n#3) = 9 Depth(Hadrian#n#1) = 10 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumers: *Root*#v#1 (Depth=1) Depth(polychromize#v#1) = 4 Depth(stress#v#2) = 3 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#2 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumers: diversion#n#1 (Depth=9) Depth(professional_football#n#1) = 13 Depth(scat#n#1) = 14 WordNet-Similarity-2.05/t/keys/1.04/restrace.key0000644000175000017500000000726011015336624017416 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=7.993246) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): entity#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#2 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.840473) activity#n#1 (IC=2.884734) psychological_feature#n#1 (IC=1.402631) abstraction#n#6 (IC=0.516189) WordNet-Similarity-2.05/t/keys/1.04/respairs_nr.key0000644000175000017500000000175111015336624020134 0ustar sidsid0.5162 0.0000 1.4362 0.5162 0.5162 0.0000 4.2270 0.0000 2.4760 0.0000 0.0000 0.0000 0.5162 0.0000 0.0000 0.5162 1.4362 1.4362 0.5162 0.5162 0.9091 0.9091 1.4362 0.0000 1.4362 0.0000 0.9091 0.0000 0.0000 0.0000 0.9091 0.0000 0.0000 0.9091 0.5162 0.0000 2.4185 2.3401 0.9091 0.0000 2.3447 0.0000 0.0000 0.0000 0.9091 0.0000 0.9091 1.4362 0.5162 0.5162 2.0614 3.6545 0.0000 0.0000 2.4760 0.9091 0.9091 0.9091 0.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.5162 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.4362 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 0.5162 0.0000 -1000000.0000 0.0000 0.0000 2.3678 0.0000 -1000000.0000 -1000000.0000 0.5162 WordNet-Similarity-2.05/t/keys/1.04/pathtrace.key0000644000175000017500000000771511015336624017566 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Shortest path: dog#n#1 canine#n#2 carnivore#n#1 feline#n#1 cat#n#1 Path length = 5 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Shortest path: art#n#3 superior_skill#n#1 ability#n#2 cognition#n#1 psychological_feature#n#1 abstraction#n#6 abstract_entity#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Path length = 16 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Shortest path: polychrome#v#1 color#v#1 change#v#2 *Root*#v#1 pronounce#v#1 stress#v#2 Path length = 6HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#2 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Shortest path: professional_football#n#1 sport#n#2 occupation#n#1 activity#n#1 music#n#3 vocal_music#n#2 singing#n#1 scat#n#1 Path length = 8WordNet-Similarity-2.05/t/keys/1.04/linpairs.key0000644000175000017500000000146311015336624017426 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/1.04/wndepths.txt0000644000175000017500000001620011015336624017463 0ustar sidsidn 00000000 19 n 00001740 18 v 00000000 14 v 00001740 4 v 00010473 4 v 00014569 2 v 00015733 1 v 00016722 1 v 00017302 2 v 00017885 3 v 00018178 1 v 00020097 2 v 00020413 1 v 00051608 1 v 00056596 1 v 00058835 1 v 00076552 1 v 00076641 1 v 00077958 5 v 00098726 3 v 00103834 5 v 00106792 2 v 00108238 8 v 00116498 1 v 00120850 4 v 00123976 8 v 00170552 5 v 00221220 1 v 00254742 2 v 00302560 1 v 00336141 3 v 00341793 3 v 00344775 4 v 00356117 2 v 00409546 3 v 00410743 2 v 00411442 1 v 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1 v 02647924 1 v 02648259 2 v 02650259 4 v 02652643 1 v 02653426 1 v 02655013 3 v 02655838 1 v 02655994 4 v 02661466 1 v 02661600 1 v 02674635 1 v 02674741 1 v 02677242 2 v 02679957 1 v 02681791 1 v 02683126 3 v 02685997 2 v 02686493 2 v 02688047 3 v 02688503 1 v 02688832 1 v 02689432 3 v 02690117 1 v 02690791 1 v 02692092 2 v 02694762 2 v 02695676 1 v 02696370 1 v 02698122 1 v 02701387 3 v 02706698 2 v 02708472 2 v 02709480 1 v 02710327 1 v 02711933 1 v 02712861 1 v 02716393 1 v 02716507 1 v 02716651 1 v 02716797 2 v 02717330 1 v 02717441 1 v 02717880 1 v 02718130 1 v 02719569 1 v 02719910 1 v 02720110 1 v 02720945 1 v 02725216 1 v 02731140 4 v 02733145 1 v 02736216 2 v 02736939 4 v 02737754 1 v 02738211 3 v 02739935 2 v 02741779 3 v 02743712 2 v 02744641 1 v 02745006 1 v 02745640 1 wnver::2.1 WordNet-Similarity-2.05/t/keys/1.04/linpairs_nr.key0000644000175000017500000000175111015336624020125 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 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WordNet-Similarity-2.05/t/keys/1.04/wuppairs.key0000644000175000017500000000146311015336624017457 0ustar sidsid0.4000 0.1481 0.3810 0.4000 0.3478 0.1905 0.7059 0.2000 0.5714 0.1739 0.1818 0.1739 0.3478 0.1667 0.1905 0.3810 0.3333 0.4000 0.3333 0.3810 0.3000 0.3000 0.3478 0.1538 0.3333 0.2105 0.3333 0.1818 0.2105 0.2105 0.3750 0.2105 0.2105 0.2857 0.4211 0.1905 0.5263 0.5000 0.4286 0.1600 0.4800 0.1600 0.2105 0.1739 0.2727 0.1818 0.3000 0.3478 0.4211 0.3636 0.6364 0.6667 0.2105 0.2222 0.5217 0.3000 0.3000 0.2857 0.2500 0.2000 0.2000 0.2500 0.1818 0.4000 0.2222 0.2500 0.2222 0.2000 0.2857 0.1250 0.2857 0.2222 0.2500 0.2000 0.3333 0.2222 0.2000 0.1818 0.2000 0.3333 0.2000 0.2500 0.1429 0.2222 0.2857 -1000000.0000 0.3077 0.2000 0.1739 -1000000.0000 0.1739 -1000000.0000 0.1667 0.4706 -1000000.0000 0.1905 0.2222 -1000000.0000 0.2353 0.3810 0.2667 -1000000.0000 0.2000 0.2353 0.4762 0.2000 -1000000.0000 -1000000.0000 0.3478 WordNet-Similarity-2.05/t/keys/1.04/hsopairs.key0000644000175000017500000000137311015336624017435 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 3.0000 0.0000 0.0000 3.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 WordNet-Similarity-2.05/t/keys/1.04/lintrace.key0000644000175000017500000000772211015336624017412 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=7.993246) Concept1: dog#n#1 (IC=8.489683) Concept2: cat#n#1 (IC=9.336980) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): entity#n#1 (IC=0.000000) Concept1: art#n#3 (IC=9.950085) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.327881) HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#2 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstract_entity#n#1 abstraction#n#6 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.840473) activity#n#1 (IC=2.884734) psychological_feature#n#1 (IC=1.402631) abstraction#n#6 (IC=0.516189) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/1.04/pathpairs_nr.key0000644000175000017500000000175111015336624020277 0ustar sidsid0.0769 0.0417 0.0714 0.0769 0.0625 0.0556 0.1667 0.0588 0.1000 0.0500 0.0526 0.0500 0.0625 0.0476 0.0556 0.0714 0.0588 0.0769 0.0588 0.0714 0.0667 0.0667 0.0714 0.0435 0.0588 0.0625 0.0769 0.0526 0.0625 0.0625 0.0909 0.0625 0.0625 0.0625 0.0833 0.0556 0.1000 0.0909 0.1111 0.0455 0.0714 0.0455 0.0625 0.0500 0.0588 0.0526 0.0667 0.0625 0.0833 0.0667 0.1111 0.1250 0.0625 0.0667 0.0833 0.0667 0.0667 0.0625 0.0769 0.0588 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0526 0.0588 0.0500 -1000000.0000 0.0500 -1000000.0000 0.0476 0.1000 -1000000.0000 0.0556 -1000000.0000 -1000000.0000 0.0714 0.0714 0.0833 -1000000.0000 0.0588 0.0714 0.0833 0.0588 -1000000.0000 -1000000.0000 0.0625 WordNet-Similarity-2.05/t/keys/1.04/leskpairs_nr.key0000644000175000017500000000037711015336624020304 0ustar sidsid6 5 3 39 7 5 38 6 7 50 18 14 3 2 7 4 4 11 5 5 4 10 0 4 3 8 5 27 14 15 50 17 13 25 8 7 2 9 12 12 6 8 8 0 4 14 6 15 2 1 11 17 5 5 4 5 9 3 14 15 5 3 1 2 8 7 3 2 8 0 11 0 2 1 16 6 1 5 4 5 1 4 3 5 8 0 16 41 13 3 15 3 6 3 1 2 10 2 12 12 16 1 5 3 25 11 5 8 7 12 WordNet-Similarity-2.05/t/keys/1.04/wuppairs_nr.key0000644000175000017500000000175111015336624020156 0ustar sidsid0.3333 0.0800 0.3158 0.3333 0.2857 0.1053 0.6667 0.1111 0.5263 0.0952 0.1000 0.0952 0.2857 0.0909 0.1053 0.3158 0.2727 0.3333 0.2727 0.3158 0.2222 0.2222 0.2857 0.0833 0.2727 0.1176 0.2500 0.1000 0.1176 0.1176 0.2857 0.1176 0.1176 0.2105 0.3529 0.1053 0.4706 0.4444 0.3333 0.0870 0.4348 0.0870 0.1176 0.0952 0.2000 0.1000 0.2222 0.2857 0.3529 0.3000 0.6000 0.6316 0.1176 0.1250 0.4762 0.2222 0.2222 0.2105 0.1429 0.1111 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.2500 0.1111 0.0952 -1000000.0000 0.0952 -1000000.0000 0.0909 0.4000 -1000000.0000 0.1053 -1000000.0000 -1000000.0000 0.1333 0.3158 0.1538 -1000000.0000 0.1111 0.1333 0.4211 0.1111 -1000000.0000 -1000000.0000 0.2857 WordNet-Similarity-2.05/t/keys/1.04/lchpairs_nr.key0000644000175000017500000000175111015336624020111 0ustar sidsid1.0186 0.4055 0.9445 1.0186 0.8109 0.6931 1.7918 0.7503 1.2809 0.5878 0.6391 0.5878 0.8109 0.5390 0.6931 0.9445 0.7503 1.0186 0.7503 0.9445 0.8755 0.8755 0.9445 0.4480 0.7503 0.8109 1.0186 0.6391 0.8109 0.8109 1.1856 0.8109 0.8109 0.8109 1.0986 0.6931 1.2809 1.1856 1.3863 0.4925 0.9445 0.4925 0.8109 0.5878 0.7503 0.6391 0.8755 0.8109 1.0986 0.8755 1.3863 1.5041 0.8109 0.8755 1.0986 0.8755 0.8755 0.8109 1.0186 0.7503 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.6391 0.7503 0.5878 -1000000.0000 0.5878 -1000000.0000 0.5390 1.2809 -1000000.0000 0.6931 -1000000.0000 -1000000.0000 0.9445 0.9445 1.0986 -1000000.0000 0.7503 0.9445 1.0986 0.7503 -1000000.0000 -1000000.0000 0.8109 WordNet-Similarity-2.05/t/keys/1.04/jcnpairs_nr.key0000644000175000017500000000175111015336624020115 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0427 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0411 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 0.0418 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/1.04/hsotrace.key0000644000175000017500000000437511015336624017422 0ustar sidsidHorizontal Links of dog#n#1: Upward Links of dog#n#1: canine#n#2 flag#n#7 Downward Links of dog#n#1: Canis#n#1 pack#n#6 puppy#n#1 pooch#n#1 cur#n#1 lapdog#n#1 toy_dog#n#1 hunting_dog#n#1 working_dog#n#1 dalmatian#n#2 basenji#n#1 pug#n#1 Leonberg#n#1 Newfoundland#n#1 Great_Pyrenees#n#1 spitz#n#1 griffon#n#2 corgi#n#1 poodle#n#1 Mexican_hairless#n#1 Horizontal Links of cat#n#1: Upward Links of cat#n#1: feline#n#1 Downward Links of cat#n#1: domestic_cat#n#1 wildcat#n#3 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] carnivore#n#1 [D] feline#n#1 [D] cat#n#1 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] paw#n#1 [D] feline#n#1 [D] cat#n#1 Horizontal Links of untruly#r#1: untrue#a#1 Upward Links of untruly#r#1: Downward Links of untruly#r#1: Horizontal Links of expository#a#1: instructive#a#1 Upward Links of expository#a#1: Downward Links of expository#a#1: Horizontal Links of noncyclic#a#1: cyclic#a#4 cyclicity#n#1 Upward Links of noncyclic#a#1: Downward Links of noncyclic#a#1: Horizontal Links of indecisive#a#2: inconclusive#a#1 irresolute#a#1 decisive#a#1 inconclusive#a#2 Upward Links of indecisive#a#2: Downward Links of indecisive#a#2: Horizontal Links of art#n#3: Upward Links of art#n#3: superior_skill#n#1 Downward Links of art#n#3: aviation#n#3 eristic#n#2 falconry#n#1 fortification#n#2 homiletics#n#2 horology#n#1 minstrelsy#n#3 musicianship#n#1 enology#n#1 puppetry#n#1 taxidermy#n#1 telescopy#n#1 ventriloquism#n#1 Horizontal Links of Hadrian#n#1: Upward Links of Hadrian#n#1: Roman_Emperor#n#1 Downward Links of Hadrian#n#1: Horizontal Links of mortise#v#1: Upward Links of mortise#v#1: cut#v#1 Downward Links of mortise#v#1: Horizontal Links of hooded_seal#n#1: Upward Links of hooded_seal#n#1: earless_seal#n#1 Downward Links of hooded_seal#n#1: Cystophora#n#1Horizontal Links of polychrome#v#1: Upward Links of polychrome#v#1: color#v#1 Downward Links of polychrome#v#1: Horizontal Links of stress#v#2: Upward Links of stress#v#2: pronounce#v#1 Downward Links of stress#v#2: Horizontal Links of professional_football#n#1: Upward Links of professional_football#n#1: football#n#1 sport#n#2 Downward Links of professional_football#n#1: Horizontal Links of scat#n#1: Upward Links of scat#n#1: singing#n#1 jazz#n#2 Downward Links of scat#n#1: WordNet-Similarity-2.05/t/keys/respairs_nr.key0000644000175000017500000000174111015336623017550 0ustar sidsid0.7794 0.0000 1.1692 0.7794 0.7794 0.0000 3.7607 0.7794 2.4934 0.0000 0.0000 0.0000 0.7794 0.0000 0.0000 0.7794 1.3696 1.3696 0.7794 0.7794 0.6144 0.6144 1.3696 0.0000 1.1692 0.0000 0.6144 0.0000 0.0000 0.0000 0.6144 0.0000 0.0000 0.6144 0.7794 0.6144 1.9033 1.8184 0.6144 0.0000 1.8231 0.0000 0.0000 0.0000 0.6144 0.0000 0.6144 1.3696 0.7794 0.7794 2.6044 3.4451 0.0000 0.0000 2.4934 0.6144 0.6144 0.6144 0.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 undef -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.7794 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3696 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 0.7794 0.0000 -1000000.0000 0.0000 0.0000 1.3696 0.0000 -1000000.0000 -1000000.0000 0.7794 WordNet-Similarity-2.05/t/keys/pathtrace.key0000644000175000017500000000765011015336623017201 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 domestic_animal#n#1 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Shortest path: dog#n#1 canine#n#2 carnivore#n#1 feline#n#1 cat#n#1 Path length = 5 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 person#n#1 ruler#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 ruler#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Shortest path: art#n#3 superior_skill#n#1 ability#n#2 cognition#n#1 psychological_feature#n#1 abstraction#n#6 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 ruler#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Path length = 15 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#1 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Shortest path: polychrome#v#1 color#v#1 change#v#1 *Root*#v#1 pronounce#v#1 stress#v#2 Path length = 6HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 game#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#2 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#2 performance#n#2 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Shortest path: professional_football#n#1 sport#n#2 occupation#n#1 activity#n#1 music#n#3 vocal_music#n#2 singing#n#1 scat#n#1 Path length = 8WordNet-Similarity-2.05/t/keys/0.15/0000755000175000017500000000000011025552366015074 5ustar sidsidWordNet-Similarity-2.05/t/keys/0.15/lesktrace.key0000644000175000017500000000427211015336624017564 0ustar sidsidSynset 1: dog#n#1 Synset 2: cat#n#1 Functions: glos - hype glos : 2 Overlaps: 1 x "of" 1 x "many" Functions: glos - hypo glos : 19 Overlaps: 1 x "the" 1 x "member of the genus" 1 x "in" 1 x "domesticated" Functions: holo glos - glos : 2 Overlaps: 1 x "domestic" 1 x "and" Functions: holo glos - hype glos : 1 Overlaps: 1 x "of" Functions: holo glos - hypo glos : 8 Overlaps: 1 x "genus" 1 x "domestic" 1 x "of the" 1 x "and" 1 x "wild" Functions: hype glos - glos : 1 Overlaps: 1 x "and" Functions: hype glos - hype glos : 15 Overlaps: 1 x "claws" 1 x "fissiped mammals" 1 x "with" 1 x "any of various" Functions: hype glos - hypo glos : 3 Overlaps: 1 x "of" 1 x "any" 1 x "and" Functions: hypo glos - glos : 4 Overlaps: 1 x "to" 1 x "usually" 1 x "having" 1 x "and" Functions: hypo glos - hype glos : 10 Overlaps: 1 x "with" 1 x "any of various" Functions: hypo glos - hypo glos : 15 Overlaps: 1 x "the" 1 x "small" 1 x "in the" 1 x "or" 1 x "of" 1 x "resembling the" 2 x "any" 1 x "and" Functions: mero glos - hypo glos : 1 Overlaps: 1 x "or" Synset 1: untruly#r#1 Synset 2: expositive#a#1 Synset 1: noncyclic#a#1 Synset 2: indecisive#a#2 Functions: glos - also glos : 1 Overlaps: 1 x "not" Functions: glos - glos : 1 Overlaps: 1 x "not" Synset 1: art#n#3 Synset 2: Hadrian#n#1 Functions: example - glos : 2 Overlaps: 1 x "the" 1 x "of" Functions: glos - glos : 3 Overlaps: 1 x "a" 2 x "and" Functions: hypo glos - glos : 10 Overlaps: 2 x "the" 2 x "to" 1 x "a" 1 x "in" 2 x "of" 2 x "and" Functions: hypo glos - hype glos : 2 Overlaps: 1 x "the" 1 x "of" Synset 1: mortise#v#1 Synset 2: Cystophora_cristata#n#1 Functions: glos - hype glos : 1 Overlaps: 1 x "a" Functions: hype glos - glos : 1 Overlaps: 1 x "with" Functions: hype glos - hype glos : 1 Overlaps: 1 x "with" Synset 1: polychromize#v#1 Synset 2: stress#v#2 Functions: glos - glos : 1 Overlaps: 1 x "with" Synset 1: professional_football#n#1 Synset 2: scat#n#1 Functions: glos - glos : 1 Overlaps: 1 x "for" Functions: hype glos - glos : 6 Overlaps: 2 x "the" 1 x "to" 1 x "a" 1 x "for" 1 x "of" Functions: hype glos - hype glos : 5 Overlaps: 1 x "the" 1 x "a" 1 x "in" 2 x "of" WordNet-Similarity-2.05/t/keys/0.15/lchpairs.key0000644000175000017500000000150111015336624017404 0ustar sidsid0.9445 0.4925 1.0186 0.8755 0.6391 0.8755 1.7918 0.8755 1.2809 0.6931 0.6391 0.5878 0.8755 0.6391 0.8755 0.8109 0.8109 1.0986 0.6391 1.0186 0.8755 0.9445 0.9445 0.6931 0.8109 0.7503 1.0986 0.8109 0.9445 0.9445 1.1856 0.8755 0.9445 0.8109 0.8755 1.0186 1.2809 1.1856 1.1856 0.6931 0.9445 0.9445 0.7503 0.7503 0.7503 0.8755 0.8755 0.8755 0.9445 0.9445 1.3863 1.5041 1.0986 1.0986 1.0986 0.8755 0.9445 0.8109 1.1856 0.9445 1.1350 1.3863 1.0296 1.9459 1.2528 1.3863 1.7228 1.1350 1.5404 0.6242 1.5404 1.2528 1.3863 1.1350 1.7228 1.2528 1.1350 1.0296 1.1350 1.7228 1.1350 1.3863 0.7673 1.2528 1.5404 -1000000.0000 0.6391 0.8755 0.7503 -1000000.0000 0.7503 -1000000.0000 0.8109 1.3863 -1000000.0000 0.7503 1.2528 -1000000.0000 1.2809 0.8755 1.2809 -1000000.0000 1.0186 -1000000.0000 0.8755 1.0986 1.0186 -1000000.0000 -1000000.0000 0.9445 WordNet-Similarity-2.05/t/keys/0.15/pathpairs.key0000644000175000017500000000150111015336624017572 0ustar sidsid0.0714 0.0455 0.0769 0.0667 0.0526 0.0667 0.1667 0.0667 0.1000 0.0556 0.0526 0.0500 0.0667 0.0526 0.0667 0.0625 0.0625 0.0833 0.0526 0.0769 0.0667 0.0714 0.0714 0.0556 0.0625 0.0588 0.0833 0.0625 0.0714 0.0714 0.0909 0.0667 0.0714 0.0625 0.0667 0.0769 0.1000 0.0909 0.0909 0.0556 0.0714 0.0714 0.0588 0.0588 0.0588 0.0667 0.0667 0.0667 0.0714 0.0714 0.1111 0.1250 0.0833 0.0833 0.0833 0.0667 0.0714 0.0625 0.0909 0.0714 0.1111 0.1429 0.1000 0.2500 0.1250 0.1429 0.2000 0.1111 0.1667 0.0667 0.1667 0.1250 0.1429 0.1111 0.2000 0.1250 0.1111 0.1000 0.1111 0.2000 0.1111 0.1429 0.0769 0.1250 0.1667 -1000000.0000 0.0526 0.0667 0.0588 -1000000.0000 0.0588 -1000000.0000 0.0625 0.1111 -1000000.0000 0.0588 0.1250 -1000000.0000 0.1000 0.0667 0.1000 -1000000.0000 0.0769 -1000000.0000 0.0667 0.0833 0.0769 -1000000.0000 -1000000.0000 0.0714 WordNet-Similarity-2.05/t/keys/0.15/hsopairs_nr.key0000644000175000017500000000033411015336624020131 0ustar sidsid0 0 0 0 0 0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 WordNet-Similarity-2.05/t/keys/0.15/leskpairs.key0000644000175000017500000000144011015336624017576 0ustar sidsid6.0000 5.0000 3.0000 37.0000 7.0000 5.0000 38.0000 6.0000 7.0000 47.0000 13.0000 14.0000 3.0000 2.0000 3.0000 2.0000 4.0000 7.0000 5.0000 5.0000 4.0000 9.0000 0.0000 4.0000 3.0000 7.0000 5.0000 25.0000 14.0000 15.0000 28.0000 15.0000 11.0000 20.0000 7.0000 7.0000 2.0000 8.0000 8.0000 9.0000 6.0000 8.0000 7.0000 0.0000 2.0000 13.0000 6.0000 14.0000 2.0000 1.0000 10.0000 13.0000 3.0000 5.0000 4.0000 5.0000 8.0000 3.0000 11.0000 15.0000 5.0000 3.0000 1.0000 2.0000 8.0000 7.0000 3.0000 1.0000 6.0000 0.0000 11.0000 0.0000 2.0000 1.0000 14.0000 6.0000 1.0000 5.0000 4.0000 4.0000 1.0000 3.0000 3.0000 5.0000 7.0000 0.0000 15.0000 41.0000 13.0000 3.0000 15.0000 3.0000 5.0000 3.0000 1.0000 2.0000 9.0000 2.0000 12.0000 12.0000 16.0000 1.0000 5.0000 2.0000 25.0000 11.0000 5.0000 8.0000 7.0000 10.0000 WordNet-Similarity-2.05/t/keys/0.15/jcntrace.key0000644000175000017500000000634211015336624017400 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.15/lchtrace.key0000644000175000017500000000660111015336624017372 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (Length=5) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (Length=14) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=19) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (Length=6)HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): activity#n#1 (Length=8) activity#n#1 (Length=10) activity#n#1 (Length=11) activity#n#1 (Length=12) diversion#n#1 (Length=12) activity#n#1 (Length=12) diversion#n#1 (Length=14) activity#n#1 (Length=15) *Root*#n#1 (Length=15) *Root*#n#1 (Length=16) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=20) *Root*#n#1 (Length=20)WordNet-Similarity-2.05/t/keys/0.15/jcnpairs.key0000644000175000017500000000150111015336624017410 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0424 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0408 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0502 0.0774 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0532 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0415 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.15/respairs.key0000644000175000017500000000150111015336624017427 0ustar sidsid0.0000 0.0000 0.8555 0.0000 0.0000 0.0000 4.3095 0.0000 2.5580 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.3709 1.3709 0.0000 0.0000 0.8555 0.8555 1.3709 0.0000 1.3709 0.0000 0.8555 0.0000 0.0000 0.0000 0.8555 0.0000 0.0000 0.8555 0.0000 0.0000 2.5356 2.4549 0.0000 0.0000 2.4596 0.0000 0.0000 0.0000 0.8555 0.0000 0.8555 1.3709 1.7064 0.0000 2.1429 3.7388 0.0000 0.0000 2.5580 0.0000 0.8555 0.8555 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.15/wuptrace.key0000644000175000017500000000606511015336624017443 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumers: carnivore#n#1 (Depth=11) Depth(dog#n#1) = 13 Depth(cat#n#1) = 13 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumers: *Root*#n#1 (Depth=1) Depth(art#n#3) = 6 Depth(Hadrian#n#1) = 9 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumers: *Root*#v#1 (Depth=1) Depth(polychromize#v#1) = 4 Depth(stress#v#2) = 3 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumers: diversion#n#1 (Depth=4) Depth(professional_football#n#1) = 8 Depth(scat#n#1) = 9 WordNet-Similarity-2.05/t/keys/0.15/restrace.key0000644000175000017500000000567711015336624017431 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.15/respairs_nr.key0000644000175000017500000000251611015336624020135 0ustar sidsid-1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 4.3095 -1000000.0000 2.5580 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 1.3709 -1000000.0000 -1000000.0000 0.8555 0.8555 1.3709 -1000000.0000 1.3709 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 2.5356 2.4549 -1000000.0000 -1000000.0000 2.4596 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 0.8555 1.3709 1.7064 -1000000.0000 2.1429 3.7388 -1000000.0000 -1000000.0000 2.5580 -1000000.0000 0.8555 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.15/pathtrace.key0000644000175000017500000000633411015336624017563 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Shortest path: dog#n#1 canine#n#2 carnivore#n#1 feline#n#1 cat#n#1 Path length = 5 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Shortest path: art#n#3 superior_skill#n#1 ability#n#2 cognition#n#1 psychological_feature#n#1 *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Path length = 14 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Shortest path: polychrome#v#1 color#v#1 change#v#2 *Root*#v#1 pronounce#v#1 stress#v#2 Path length = 6HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Shortest path: professional_football#n#1 sport#n#2 occupation#n#1 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 Path length = 8WordNet-Similarity-2.05/t/keys/0.15/linpairs.key0000644000175000017500000000150111015336624017420 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 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WordNet-Similarity-2.05/t/keys/0.15/lintrace.key0000644000175000017500000000634211015336624017410 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.15/pathpairs_nr.key0000644000175000017500000000251611015336624020300 0ustar sidsid-1000000.0000 -1000000.0000 0.0769 -1000000.0000 -1000000.0000 -1000000.0000 0.1667 -1000000.0000 0.1000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 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-1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.15/lchpairs_nr.key0000644000175000017500000000251611015336624020112 0ustar sidsid-1000000.0000 -1000000.0000 0.9614 -1000000.0000 -1000000.0000 -1000000.0000 1.7346 -1000000.0000 1.2238 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.7538 1.0415 -1000000.0000 -1000000.0000 0.8183 0.8873 0.8873 -1000000.0000 0.7538 -1000000.0000 1.0415 -1000000.0000 -1000000.0000 -1000000.0000 1.1285 -1000000.0000 -1000000.0000 0.7538 -1000000.0000 -1000000.0000 1.2238 1.1285 -1000000.0000 -1000000.0000 0.8873 -1000000.0000 -1000000.0000 -1000000.0000 0.6931 -1000000.0000 0.8183 0.8183 0.6931 -1000000.0000 0.8938 1.4469 -1000000.0000 -1000000.0000 1.0415 -1000000.0000 0.8873 0.7538 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 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WordNet-Similarity-2.05/t/keys/0.15/hsotrace.key0000644000175000017500000000434511015336624017420 0ustar sidsidHorizontal Links of dog#n#1: Upward Links of dog#n#1: canine#n#2 flag#n#7 Downward Links of dog#n#1: Canis#n#1 pack#n#6 pooch#n#1 cur#n#1 lapdog#n#1 toy_dog#n#1 hunting_dog#n#1 working_dog#n#1 dalmatian#n#2 basenji#n#1 pug#n#1 Leonberg#n#1 Newfoundland#n#1 Great_Pyrenees#n#1 spitz#n#1 griffon#n#1 corgi#n#1 poodle#n#1 Mexican_hairless#n#1 Horizontal Links of cat#n#1: Upward Links of cat#n#1: feline#n#1 Downward Links of cat#n#1: domestic_cat#n#1 wildcat#n#3 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] carnivore#n#1 [D] feline#n#1 [D] cat#n#1 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] paw#n#1 [D] feline#n#1 [D] cat#n#1 Horizontal Links of untruly#r#1: untrue#a#1 Upward Links of untruly#r#1: Downward Links of untruly#r#1: Horizontal Links of expository#a#1: instructive#a#1 Upward Links of expository#a#1: Downward Links of expository#a#1: Horizontal Links of noncyclic#a#1: cyclic#a#4 cyclicity#n#1 Upward Links of noncyclic#a#1: Downward Links of noncyclic#a#1: Horizontal Links of indecisive#a#2: inconclusive#a#1 irresolute#a#1 decisive#a#1 inconclusive#a#2 Upward Links of indecisive#a#2: Downward Links of indecisive#a#2: Horizontal Links of art#n#3: Upward Links of art#n#3: superior_skill#n#1 Downward Links of art#n#3: aviation#n#3 eristic#n#2 falconry#n#1 fortification#n#2 homiletics#n#2 horology#n#1 minstrelsy#n#3 musicianship#n#1 enology#n#1 puppetry#n#1 taxidermy#n#1 ventriloquism#n#1 Horizontal Links of Hadrian#n#1: Upward Links of Hadrian#n#1: Roman_Emperor#n#1 Downward Links of Hadrian#n#1: Horizontal Links of mortise#v#1: Upward Links of mortise#v#1: cut#v#1 Downward Links of mortise#v#1: Horizontal Links of hooded_seal#n#1: Upward Links of hooded_seal#n#1: earless_seal#n#1 Downward Links of hooded_seal#n#1: Cystophora#n#1Horizontal Links of polychrome#v#1: Upward Links of polychrome#v#1: color#v#1 Downward Links of polychrome#v#1: Horizontal Links of stress#v#2: Upward Links of stress#v#2: pronounce#v#1 Downward Links of stress#v#2: Horizontal Links of professional_football#n#1: Upward Links of professional_football#n#1: football#n#1 sport#n#2 Downward Links of professional_football#n#1: Horizontal Links of scat#n#1: Upward Links of scat#n#1: singing#n#1 jazz#n#2 Downward Links of scat#n#1: WordNet-Similarity-2.05/t/keys/0.08/0000755000175000017500000000000011025552366015076 5ustar sidsidWordNet-Similarity-2.05/t/keys/0.08/lesktrace.key0000644000175000017500000000434411015336623017565 0ustar sidsidSynset 1: dog#n#1 Synset 2: cat#n#1 Functions: glos - hype glos : 2 Overlaps: 1 x "of" 1 x "many" Functions: glos - hypo glos : 19 Overlaps: 1 x "member of the genus" 1 x "the" 1 x "in" 1 x "domesticated" Functions: holo glos - glos : 2 Overlaps: 1 x "domestic" 1 x "and" Functions: holo glos - hype glos : 1 Overlaps: 1 x "of" Functions: holo glos - hypo glos : 8 Overlaps: 1 x "genus" 1 x "of the" 1 x "domestic" 1 x "and" 1 x "wild" Functions: hype glos - glos : 1 Overlaps: 1 x "and" Functions: hype glos - hype glos : 15 Overlaps: 1 x "claws" 1 x "fissiped mammals" 1 x "any of various" 1 x "with" Functions: hype glos - hypo glos : 3 Overlaps: 1 x "of" 1 x "any" 1 x "and" Functions: hypo glos - glos : 4 Overlaps: 1 x "to" 1 x "usually" 1 x "and" 1 x "having" Functions: hypo glos - hype glos : 11 Overlaps: 1 x "bodied" 1 x "any of various" 1 x "with" Functions: hypo glos - hypo glos : 16 Overlaps: 1 x "small" 1 x "the" 1 x "of" 2 x "any" 1 x "in the" 1 x "or" 1 x "resembling the" 1 x "in" 1 x "and" Functions: mero glos - hypo glos : 1 Overlaps: 1 x "or" Synset 1: untruly#r#1 Synset 2: expositive#a#1 Synset 1: noncyclic#a#1 Synset 2: indecisive#a#2 Functions: glos - also glos : 1 Overlaps: 1 x "not" Functions: glos - glos : 1 Overlaps: 1 x "not" Synset 1: art#n#3 Synset 2: Hadrian#n#1 Functions: example - glos : 3 Overlaps: 1 x "the" 1 x "of" 1 x "s" Functions: glos - glos : 3 Overlaps: 1 x "a" 2 x "and" Functions: hypo glos - glos : 13 Overlaps: 3 x "the" 2 x "to" 2 x "a" 1 x "in" 2 x "of" 2 x "and" 1 x "s" Functions: hypo glos - hype glos : 2 Overlaps: 1 x "the" 1 x "of" Synset 1: mortise#v#1 Synset 2: Cystophora_cristata#n#1 Functions: glos - hype glos : 2 Overlaps: 2 x "a" Functions: hype glos - glos : 1 Overlaps: 1 x "with" Functions: hype glos - hype glos : 1 Overlaps: 1 x "with" Synset 1: polychromize#v#1 Synset 2: stress#v#2 Functions: glos - glos : 1 Overlaps: 1 x "with" Synset 1: professional_football#n#1 Synset 2: scat#n#1 Functions: glos - glos : 1 Overlaps: 1 x "for" Functions: hype glos - glos : 6 Overlaps: 2 x "the" 1 x "to" 1 x "a" 1 x "of" 1 x "for" Functions: hype glos - hype glos : 5 Overlaps: 1 x "the" 1 x "a" 1 x "in" 2 x "of" WordNet-Similarity-2.05/t/keys/0.08/lchpairs.key0000644000175000017500000000150111015336623017405 0ustar sidsid0.9445 0.4925 1.0186 0.8755 0.6391 0.8755 1.7918 0.8755 1.2809 0.6931 0.6391 0.5878 0.8755 0.6391 0.8755 0.8109 0.8109 1.0986 0.6391 1.0186 0.8755 0.9445 0.9445 0.6931 0.8109 0.7503 1.0986 0.8109 0.9445 0.9445 1.1856 0.8755 0.9445 0.8109 0.8755 1.0186 1.2809 1.1856 1.1856 0.6931 0.9445 0.9445 0.7503 0.7503 0.7503 0.8755 0.8755 0.8755 0.9445 0.9445 1.3863 1.5041 1.0986 1.0986 1.0986 0.8755 0.9445 0.8109 1.1856 0.9445 1.1350 1.3863 1.0296 1.9459 1.2528 1.3863 1.7228 1.1350 1.5404 0.6242 1.5404 1.2528 1.3863 1.1350 1.7228 1.2528 1.1350 1.0296 1.1350 1.7228 1.1350 1.3863 0.7673 1.2528 1.5404 -1000000.0000 0.6391 0.8755 0.7503 -1000000.0000 0.7503 -1000000.0000 0.8109 1.3863 -1000000.0000 0.7503 1.2528 -1000000.0000 1.2809 0.8755 1.2809 -1000000.0000 1.0186 -1000000.0000 0.8755 1.0986 1.0186 -1000000.0000 -1000000.0000 0.9445 WordNet-Similarity-2.05/t/keys/0.08/pathpairs.key0000644000175000017500000000150111015336623017573 0ustar sidsid0.0714 0.0455 0.0769 0.0667 0.0526 0.0667 0.1667 0.0667 0.1000 0.0556 0.0526 0.0500 0.0667 0.0526 0.0667 0.0625 0.0625 0.0833 0.0526 0.0769 0.0667 0.0714 0.0714 0.0556 0.0625 0.0588 0.0833 0.0625 0.0714 0.0714 0.0909 0.0667 0.0714 0.0625 0.0667 0.0769 0.1000 0.0909 0.0909 0.0556 0.0714 0.0714 0.0588 0.0588 0.0588 0.0667 0.0667 0.0667 0.0714 0.0714 0.1111 0.1250 0.0833 0.0833 0.0833 0.0667 0.0714 0.0625 0.0909 0.0714 0.1111 0.1429 0.1000 0.2500 0.1250 0.1429 0.2000 0.1111 0.1667 0.0667 0.1667 0.1250 0.1429 0.1111 0.2000 0.1250 0.1111 0.1000 0.1111 0.2000 0.1111 0.1429 0.0769 0.1250 0.1667 -1000000.0000 0.0526 0.0667 0.0588 -1000000.0000 0.0588 -1000000.0000 0.0625 0.1111 -1000000.0000 0.0588 0.1250 -1000000.0000 0.1000 0.0667 0.1000 -1000000.0000 0.0769 -1000000.0000 0.0667 0.0833 0.0769 -1000000.0000 -1000000.0000 0.0714 WordNet-Similarity-2.05/t/keys/0.08/hsopairs_nr.key0000644000175000017500000000033411015336623020132 0ustar sidsid0 0 0 0 0 0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 WordNet-Similarity-2.05/t/keys/0.08/leskpairs.key0000644000175000017500000000144511015336623017604 0ustar sidsid6.0000 5.0000 3.0000 39.0000 7.0000 5.0000 38.0000 6.0000 7.0000 50.0000 18.0000 14.0000 3.0000 2.0000 7.0000 4.0000 4.0000 11.0000 5.0000 5.0000 4.0000 10.0000 0.0000 4.0000 3.0000 8.0000 5.0000 27.0000 14.0000 15.0000 50.0000 17.0000 13.0000 25.0000 8.0000 7.0000 2.0000 9.0000 12.0000 12.0000 6.0000 8.0000 8.0000 0.0000 4.0000 14.0000 6.0000 15.0000 2.0000 1.0000 11.0000 17.0000 5.0000 5.0000 4.0000 5.0000 9.0000 3.0000 14.0000 15.0000 5.0000 3.0000 1.0000 2.0000 8.0000 7.0000 3.0000 2.0000 8.0000 0.0000 11.0000 0.0000 2.0000 1.0000 16.0000 6.0000 1.0000 5.0000 4.0000 5.0000 1.0000 4.0000 3.0000 5.0000 8.0000 0.0000 16.0000 41.0000 13.0000 3.0000 15.0000 3.0000 6.0000 3.0000 1.0000 2.0000 10.0000 2.0000 12.0000 12.0000 16.0000 1.0000 5.0000 3.0000 25.0000 11.0000 5.0000 8.0000 7.0000 12.0000 WordNet-Similarity-2.05/t/keys/0.08/jcntrace.key0000644000175000017500000000634211015336623017401 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.08/lchtrace.key0000644000175000017500000000660111015336623017373 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (Length=5) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (Length=14) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=19) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (Length=6)HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): activity#n#1 (Length=8) activity#n#1 (Length=10) activity#n#1 (Length=11) activity#n#1 (Length=12) diversion#n#1 (Length=12) activity#n#1 (Length=12) diversion#n#1 (Length=14) activity#n#1 (Length=15) *Root*#n#1 (Length=15) *Root*#n#1 (Length=16) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=20) *Root*#n#1 (Length=20)WordNet-Similarity-2.05/t/keys/0.08/jcnpairs.key0000644000175000017500000000150111015336623017411 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0424 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0408 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0502 0.0774 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0532 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0415 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.08/respairs.key0000644000175000017500000000150111015336623017430 0ustar sidsid0.0000 0.0000 0.8555 0.0000 0.0000 0.0000 4.3095 0.0000 2.5580 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.3709 1.3709 0.0000 0.0000 0.8555 0.8555 1.3709 0.0000 1.3709 0.0000 0.8555 0.0000 0.0000 0.0000 0.8555 0.0000 0.0000 0.8555 0.0000 0.0000 2.5356 2.4549 0.0000 0.0000 2.4596 0.0000 0.0000 0.0000 0.8555 0.0000 0.8555 1.3709 1.7064 0.0000 2.1429 3.7388 0.0000 0.0000 2.5580 0.0000 0.8555 0.8555 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.08/wuptrace.key0000644000175000017500000000606511015336623017444 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumers: carnivore#n#1 (Depth=11) Depth(dog#n#1) = 13 Depth(cat#n#1) = 13 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumers: *Root*#n#1 (Depth=1) Depth(art#n#3) = 6 Depth(Hadrian#n#1) = 9 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumers: *Root*#v#1 (Depth=1) Depth(polychromize#v#1) = 4 Depth(stress#v#2) = 3 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumers: diversion#n#1 (Depth=4) Depth(professional_football#n#1) = 6 Depth(scat#n#1) = 7 WordNet-Similarity-2.05/t/keys/0.08/restrace.key0000644000175000017500000000567711015336623017432 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.08/respairs_nr.key0000644000175000017500000000251611015336623020136 0ustar sidsid-1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 4.3095 -1000000.0000 2.5580 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 1.3709 -1000000.0000 -1000000.0000 0.8555 0.8555 1.3709 -1000000.0000 1.3709 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 2.5356 2.4549 -1000000.0000 -1000000.0000 2.4596 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 0.8555 1.3709 1.7064 -1000000.0000 2.1429 3.7388 -1000000.0000 -1000000.0000 2.5580 -1000000.0000 0.8555 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.08/pathtrace.key0000644000175000017500000000633411015336623017564 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Shortest path: dog#n#1 canine#n#2 carnivore#n#1 feline#n#1 cat#n#1 Path length = 5 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Shortest path: art#n#3 superior_skill#n#1 ability#n#2 cognition#n#1 psychological_feature#n#1 *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Path length = 14 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Shortest path: polychrome#v#1 color#v#1 change#v#2 *Root*#v#1 pronounce#v#1 stress#v#2 Path length = 6HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Shortest path: professional_football#n#1 sport#n#2 occupation#n#1 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 Path length = 8WordNet-Similarity-2.05/t/keys/0.08/linpairs.key0000644000175000017500000000150111015336623017421 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 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0.5000 0.1667 0.1053 0.4762 0.1333 0.1111 0.1111 0.2000 0.1250 0.2222 0.3333 0.2353 0.1333 0.3333 0.5882 0.1538 0.1538 0.4211 0.1250 0.2353 0.2105 0.1667 0.1333 0.2000 0.2500 0.1818 0.4000 0.2222 0.2500 0.3333 0.2000 0.2857 0.1250 0.2857 0.2222 0.2500 0.2000 0.3333 0.2222 0.2000 0.1818 0.2000 0.3333 0.2000 0.2500 0.1429 0.2222 0.2857 -1000000.0000 0.1000 0.1250 0.1111 -1000000.0000 0.1111 -1000000.0000 0.1176 0.4615 -1000000.0000 0.1111 0.2222 -1000000.0000 0.1818 0.1250 0.1818 -1000000.0000 0.1429 -1000000.0000 0.1250 0.3529 0.1429 -1000000.0000 -1000000.0000 0.1333 WordNet-Similarity-2.05/t/keys/0.08/hsopairs.key0000644000175000017500000000140211015336623017430 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 3.0000 0.0000 0.0000 3.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.08/lintrace.key0000644000175000017500000000634211015336623017411 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.08/pathpairs_nr.key0000644000175000017500000000251611015336623020301 0ustar sidsid-1000000.0000 -1000000.0000 0.0769 -1000000.0000 -1000000.0000 -1000000.0000 0.1667 -1000000.0000 0.1000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 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-1000000.0000 -1000000.0000 0.1667 -1000000.0000 -1000000.0000 0.1176 -1000000.0000 -1000000.0000 0.4000 0.4286 -1000000.0000 -1000000.0000 0.4211 -1000000.0000 -1000000.0000 -1000000.0000 0.1111 -1000000.0000 0.1250 0.2500 0.1333 -1000000.0000 0.2000 0.5333 -1000000.0000 -1000000.0000 0.3529 -1000000.0000 0.1333 0.1176 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.3636 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.2667 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.08/lchpairs_nr.key0000644000175000017500000000251611015336623020113 0ustar sidsid-1000000.0000 -1000000.0000 0.9614 -1000000.0000 -1000000.0000 -1000000.0000 1.7346 -1000000.0000 1.2238 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.7538 1.0415 -1000000.0000 -1000000.0000 0.8183 0.8873 0.8873 -1000000.0000 0.7538 -1000000.0000 1.0415 -1000000.0000 -1000000.0000 -1000000.0000 1.1285 -1000000.0000 -1000000.0000 0.7538 -1000000.0000 -1000000.0000 1.2238 1.1285 -1000000.0000 -1000000.0000 0.8873 -1000000.0000 -1000000.0000 -1000000.0000 0.6931 -1000000.0000 0.8183 0.8183 0.6931 -1000000.0000 0.8938 1.4469 -1000000.0000 -1000000.0000 1.0415 -1000000.0000 0.8873 0.7538 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 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WordNet-Similarity-2.05/t/keys/0.08/hsotrace.key0000644000175000017500000000434511015336623017421 0ustar sidsidHorizontal Links of dog#n#1: Upward Links of dog#n#1: canine#n#2 flag#n#7 Downward Links of dog#n#1: Canis#n#1 pack#n#6 pooch#n#1 cur#n#1 lapdog#n#1 toy_dog#n#1 hunting_dog#n#1 working_dog#n#1 dalmatian#n#2 basenji#n#1 pug#n#1 Leonberg#n#1 Newfoundland#n#1 Great_Pyrenees#n#1 spitz#n#1 griffon#n#1 corgi#n#1 poodle#n#1 Mexican_hairless#n#1 Horizontal Links of cat#n#1: Upward Links of cat#n#1: feline#n#1 Downward Links of cat#n#1: domestic_cat#n#1 wildcat#n#3 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] carnivore#n#1 [D] feline#n#1 [D] cat#n#1 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] paw#n#1 [D] feline#n#1 [D] cat#n#1 Horizontal Links of untruly#r#1: untrue#a#1 Upward Links of untruly#r#1: Downward Links of untruly#r#1: Horizontal Links of expository#a#1: instructive#a#1 Upward Links of expository#a#1: Downward Links of expository#a#1: Horizontal Links of noncyclic#a#1: cyclic#a#4 cyclicity#n#1 Upward Links of noncyclic#a#1: Downward Links of noncyclic#a#1: Horizontal Links of indecisive#a#2: inconclusive#a#1 irresolute#a#1 decisive#a#1 inconclusive#a#2 Upward Links of indecisive#a#2: Downward Links of indecisive#a#2: Horizontal Links of art#n#3: Upward Links of art#n#3: superior_skill#n#1 Downward Links of art#n#3: aviation#n#3 eristic#n#2 falconry#n#1 fortification#n#2 homiletics#n#2 horology#n#1 minstrelsy#n#3 musicianship#n#1 enology#n#1 puppetry#n#1 taxidermy#n#1 ventriloquism#n#1 Horizontal Links of Hadrian#n#1: Upward Links of Hadrian#n#1: Roman_Emperor#n#1 Downward Links of Hadrian#n#1: Horizontal Links of mortise#v#1: Upward Links of mortise#v#1: cut#v#1 Downward Links of mortise#v#1: Horizontal Links of hooded_seal#n#1: Upward Links of hooded_seal#n#1: earless_seal#n#1 Downward Links of hooded_seal#n#1: Cystophora#n#1Horizontal Links of polychrome#v#1: Upward Links of polychrome#v#1: color#v#1 Downward Links of polychrome#v#1: Horizontal Links of stress#v#2: Upward Links of stress#v#2: pronounce#v#1 Downward Links of stress#v#2: Horizontal Links of professional_football#n#1: Upward Links of professional_football#n#1: football#n#1 sport#n#2 Downward Links of professional_football#n#1: Horizontal Links of scat#n#1: Upward Links of scat#n#1: singing#n#1 jazz#n#2 Downward Links of scat#n#1: WordNet-Similarity-2.05/t/keys/wnver.key0000644000175000017500000000003411015336623016354 0ustar sidsideOS9lXC6GvMWznF1wkZofDdtbBU WordNet-Similarity-2.05/t/keys/0.16/0000755000175000017500000000000011025552366015075 5ustar sidsidWordNet-Similarity-2.05/t/keys/0.16/lesktrace.key0000644000175000017500000000427211015336624017565 0ustar sidsidSynset 1: dog#n#1 Synset 2: cat#n#1 Functions: glos - hype glos : 2 Overlaps: 1 x "of" 1 x "many" Functions: glos - hypo glos : 19 Overlaps: 1 x "the" 1 x "member of the genus" 1 x "in" 1 x "domesticated" Functions: holo glos - glos : 2 Overlaps: 1 x "domestic" 1 x "and" Functions: holo glos - hype glos : 1 Overlaps: 1 x "of" Functions: holo glos - hypo glos : 8 Overlaps: 1 x "genus" 1 x "domestic" 1 x "of the" 1 x "and" 1 x "wild" Functions: hype glos - glos : 1 Overlaps: 1 x "and" Functions: hype glos - hype glos : 15 Overlaps: 1 x "claws" 1 x "fissiped mammals" 1 x "with" 1 x "any of various" Functions: hype glos - hypo glos : 3 Overlaps: 1 x "of" 1 x "any" 1 x "and" Functions: hypo glos - glos : 4 Overlaps: 1 x "to" 1 x "usually" 1 x "having" 1 x "and" Functions: hypo glos - hype glos : 10 Overlaps: 1 x "with" 1 x "any of various" Functions: hypo glos - hypo glos : 15 Overlaps: 1 x "the" 1 x "small" 1 x "in the" 1 x "or" 1 x "of" 1 x "resembling the" 2 x "any" 1 x "and" Functions: mero glos - hypo glos : 1 Overlaps: 1 x "or" Synset 1: untruly#r#1 Synset 2: expositive#a#1 Synset 1: noncyclic#a#1 Synset 2: indecisive#a#2 Functions: glos - also glos : 1 Overlaps: 1 x "not" Functions: glos - glos : 1 Overlaps: 1 x "not" Synset 1: art#n#3 Synset 2: Hadrian#n#1 Functions: example - glos : 2 Overlaps: 1 x "the" 1 x "of" Functions: glos - glos : 3 Overlaps: 1 x "a" 2 x "and" Functions: hypo glos - glos : 10 Overlaps: 2 x "the" 2 x "to" 1 x "a" 1 x "in" 2 x "of" 2 x "and" Functions: hypo glos - hype glos : 2 Overlaps: 1 x "the" 1 x "of" Synset 1: mortise#v#1 Synset 2: Cystophora_cristata#n#1 Functions: glos - hype glos : 1 Overlaps: 1 x "a" Functions: hype glos - glos : 1 Overlaps: 1 x "with" Functions: hype glos - hype glos : 1 Overlaps: 1 x "with" Synset 1: polychromize#v#1 Synset 2: stress#v#2 Functions: glos - glos : 1 Overlaps: 1 x "with" Synset 1: professional_football#n#1 Synset 2: scat#n#1 Functions: glos - glos : 1 Overlaps: 1 x "for" Functions: hype glos - glos : 6 Overlaps: 2 x "the" 1 x "to" 1 x "a" 1 x "for" 1 x "of" Functions: hype glos - hype glos : 5 Overlaps: 1 x "the" 1 x "a" 1 x "in" 2 x "of" WordNet-Similarity-2.05/t/keys/0.16/lchpairs.key0000644000175000017500000000150111015336624017405 0ustar sidsid0.9445 0.4925 1.0186 0.8755 0.6391 0.8755 1.7918 0.8755 1.2809 0.6931 0.6391 0.5878 0.8755 0.6391 0.8755 0.8109 0.8109 1.0986 0.6391 1.0186 0.8755 0.9445 0.9445 0.6931 0.8109 0.7503 1.0986 0.8109 0.9445 0.9445 1.1856 0.8755 0.9445 0.8109 0.8755 1.0186 1.2809 1.1856 1.1856 0.6931 0.9445 0.9445 0.7503 0.7503 0.7503 0.8755 0.8755 0.8755 0.9445 0.9445 1.3863 1.5041 1.0986 1.0986 1.0986 0.8755 0.9445 0.8109 1.1856 0.9445 1.1350 1.3863 1.0296 1.9459 1.2528 1.3863 1.7228 1.1350 1.5404 0.6242 1.5404 1.2528 1.3863 1.1350 1.7228 1.2528 1.1350 1.0296 1.1350 1.7228 1.1350 1.3863 0.7673 1.2528 1.5404 -1000000.0000 0.6391 0.8755 0.7503 -1000000.0000 0.7503 -1000000.0000 0.8109 1.3863 -1000000.0000 0.7503 1.2528 -1000000.0000 1.2809 0.8755 1.2809 -1000000.0000 1.0186 -1000000.0000 0.8755 1.0986 1.0186 -1000000.0000 -1000000.0000 0.9445 WordNet-Similarity-2.05/t/keys/0.16/pathpairs.key0000644000175000017500000000150111015336624017573 0ustar sidsid0.0714 0.0455 0.0769 0.0667 0.0526 0.0667 0.1667 0.0667 0.1000 0.0556 0.0526 0.0500 0.0667 0.0526 0.0667 0.0625 0.0625 0.0833 0.0526 0.0769 0.0667 0.0714 0.0714 0.0556 0.0625 0.0588 0.0833 0.0625 0.0714 0.0714 0.0909 0.0667 0.0714 0.0625 0.0667 0.0769 0.1000 0.0909 0.0909 0.0556 0.0714 0.0714 0.0588 0.0588 0.0588 0.0667 0.0667 0.0667 0.0714 0.0714 0.1111 0.1250 0.0833 0.0833 0.0833 0.0667 0.0714 0.0625 0.0909 0.0714 0.1111 0.1429 0.1000 0.2500 0.1250 0.1429 0.2000 0.1111 0.1667 0.0667 0.1667 0.1250 0.1429 0.1111 0.2000 0.1250 0.1111 0.1000 0.1111 0.2000 0.1111 0.1429 0.0769 0.1250 0.1667 -1000000.0000 0.0526 0.0667 0.0588 -1000000.0000 0.0588 -1000000.0000 0.0625 0.1111 -1000000.0000 0.0588 0.1250 -1000000.0000 0.1000 0.0667 0.1000 -1000000.0000 0.0769 -1000000.0000 0.0667 0.0833 0.0769 -1000000.0000 -1000000.0000 0.0714 WordNet-Similarity-2.05/t/keys/0.16/hsopairs_nr.key0000644000175000017500000000033411015336624020132 0ustar sidsid0 0 0 0 0 0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 WordNet-Similarity-2.05/t/keys/0.16/leskpairs.key0000644000175000017500000000144011015336624017577 0ustar sidsid6.0000 5.0000 3.0000 37.0000 7.0000 5.0000 38.0000 6.0000 7.0000 47.0000 13.0000 14.0000 3.0000 2.0000 3.0000 2.0000 4.0000 7.0000 5.0000 5.0000 4.0000 9.0000 0.0000 4.0000 3.0000 7.0000 5.0000 25.0000 14.0000 15.0000 28.0000 15.0000 11.0000 20.0000 7.0000 7.0000 2.0000 8.0000 8.0000 9.0000 6.0000 8.0000 7.0000 0.0000 2.0000 13.0000 6.0000 14.0000 2.0000 1.0000 10.0000 13.0000 3.0000 5.0000 4.0000 5.0000 8.0000 3.0000 11.0000 15.0000 5.0000 3.0000 1.0000 2.0000 8.0000 7.0000 3.0000 1.0000 6.0000 0.0000 11.0000 0.0000 2.0000 1.0000 14.0000 6.0000 1.0000 5.0000 4.0000 4.0000 1.0000 3.0000 3.0000 5.0000 7.0000 0.0000 15.0000 41.0000 13.0000 3.0000 15.0000 3.0000 5.0000 3.0000 1.0000 2.0000 9.0000 2.0000 12.0000 12.0000 16.0000 1.0000 5.0000 2.0000 25.0000 11.0000 5.0000 8.0000 7.0000 10.0000 WordNet-Similarity-2.05/t/keys/0.16/jcntrace.key0000644000175000017500000000634211015336624017401 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.16/lchtrace.key0000644000175000017500000000660111015336624017373 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (Length=5) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (Length=14) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=19) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (Length=6)HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): activity#n#1 (Length=8) activity#n#1 (Length=10) activity#n#1 (Length=11) activity#n#1 (Length=12) diversion#n#1 (Length=12) activity#n#1 (Length=12) diversion#n#1 (Length=14) activity#n#1 (Length=15) *Root*#n#1 (Length=15) *Root*#n#1 (Length=16) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=20) *Root*#n#1 (Length=20)WordNet-Similarity-2.05/t/keys/0.16/jcnpairs.key0000644000175000017500000000150111015336624017411 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0424 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0408 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0502 0.0774 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0532 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0415 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.16/respairs.key0000644000175000017500000000150111015336624017430 0ustar sidsid0.0000 0.0000 0.8555 0.0000 0.0000 0.0000 4.3095 0.0000 2.5580 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.3709 1.3709 0.0000 0.0000 0.8555 0.8555 1.3709 0.0000 1.3709 0.0000 0.8555 0.0000 0.0000 0.0000 0.8555 0.0000 0.0000 0.8555 0.0000 0.0000 2.5356 2.4549 0.0000 0.0000 2.4596 0.0000 0.0000 0.0000 0.8555 0.0000 0.8555 1.3709 1.7064 0.0000 2.1429 3.7388 0.0000 0.0000 2.5580 0.0000 0.8555 0.8555 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.16/wuptrace.key0000644000175000017500000000606511015336624017444 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumers: carnivore#n#1 (Depth=11) Depth(dog#n#1) = 13 Depth(cat#n#1) = 13 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumers: *Root*#n#1 (Depth=1) Depth(art#n#3) = 6 Depth(Hadrian#n#1) = 9 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumers: *Root*#v#1 (Depth=1) Depth(polychromize#v#1) = 4 Depth(stress#v#2) = 3 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumers: diversion#n#1 (Depth=4) Depth(professional_football#n#1) = 8 Depth(scat#n#1) = 9 WordNet-Similarity-2.05/t/keys/0.16/restrace.key0000644000175000017500000000567711015336624017432 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.16/respairs_nr.key0000644000175000017500000000251611015336624020136 0ustar sidsid-1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 4.3095 -1000000.0000 2.5580 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 1.3709 -1000000.0000 -1000000.0000 0.8555 0.8555 1.3709 -1000000.0000 1.3709 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 2.5356 2.4549 -1000000.0000 -1000000.0000 2.4596 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 0.8555 1.3709 1.7064 -1000000.0000 2.1429 3.7388 -1000000.0000 -1000000.0000 2.5580 -1000000.0000 0.8555 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.16/pathtrace.key0000644000175000017500000000633411015336624017564 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Shortest path: dog#n#1 canine#n#2 carnivore#n#1 feline#n#1 cat#n#1 Path length = 5 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Shortest path: art#n#3 superior_skill#n#1 ability#n#2 cognition#n#1 psychological_feature#n#1 *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Path length = 14 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Shortest path: polychrome#v#1 color#v#1 change#v#2 *Root*#v#1 pronounce#v#1 stress#v#2 Path length = 6HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Shortest path: professional_football#n#1 sport#n#2 occupation#n#1 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 Path length = 8WordNet-Similarity-2.05/t/keys/0.16/linpairs.key0000644000175000017500000000150111015336624017421 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 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0.4444 0.1667 0.1053 0.4348 0.1333 0.1111 0.1111 0.2000 0.1250 0.2222 0.3000 0.2353 0.1333 0.3333 0.5882 0.1538 0.1538 0.4211 0.1250 0.2353 0.2105 0.1667 0.1333 0.2000 0.2500 0.1818 0.4000 0.2222 0.2500 0.3333 0.2000 0.2857 0.1250 0.2857 0.2222 0.2500 0.2000 0.3333 0.2222 0.2000 0.1818 0.2000 0.3333 0.2000 0.2500 0.1429 0.2222 0.2857 -1000000.0000 0.1000 0.1250 0.1111 -1000000.0000 0.1111 -1000000.0000 0.1176 0.4286 -1000000.0000 0.1111 0.2222 -1000000.0000 0.1818 0.1250 0.1818 -1000000.0000 0.1429 -1000000.0000 0.1250 0.3529 0.1429 -1000000.0000 -1000000.0000 0.1333 WordNet-Similarity-2.05/t/keys/0.16/hsopairs.key0000644000175000017500000000140211015336624017430 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 3.0000 0.0000 0.0000 3.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.16/lintrace.key0000644000175000017500000000634211015336624017411 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.16/pathpairs_nr.key0000644000175000017500000000251611015336624020301 0ustar sidsid-1000000.0000 -1000000.0000 0.0769 -1000000.0000 -1000000.0000 -1000000.0000 0.1667 -1000000.0000 0.1000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 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-1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.16/lchpairs_nr.key0000644000175000017500000000251611015336624020113 0ustar sidsid-1000000.0000 -1000000.0000 0.9614 -1000000.0000 -1000000.0000 -1000000.0000 1.7346 -1000000.0000 1.2238 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.7538 1.0415 -1000000.0000 -1000000.0000 0.8183 0.8873 0.8873 -1000000.0000 0.7538 -1000000.0000 1.0415 -1000000.0000 -1000000.0000 -1000000.0000 1.1285 -1000000.0000 -1000000.0000 0.7538 -1000000.0000 -1000000.0000 1.2238 1.1285 -1000000.0000 -1000000.0000 0.8873 -1000000.0000 -1000000.0000 -1000000.0000 0.6931 -1000000.0000 0.8183 0.8183 0.6931 -1000000.0000 0.8938 1.4469 -1000000.0000 -1000000.0000 1.0415 -1000000.0000 0.8873 0.7538 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 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WordNet-Similarity-2.05/t/keys/0.16/hsotrace.key0000644000175000017500000000434511015336624017421 0ustar sidsidHorizontal Links of dog#n#1: Upward Links of dog#n#1: canine#n#2 flag#n#7 Downward Links of dog#n#1: Canis#n#1 pack#n#6 pooch#n#1 cur#n#1 lapdog#n#1 toy_dog#n#1 hunting_dog#n#1 working_dog#n#1 dalmatian#n#2 basenji#n#1 pug#n#1 Leonberg#n#1 Newfoundland#n#1 Great_Pyrenees#n#1 spitz#n#1 griffon#n#1 corgi#n#1 poodle#n#1 Mexican_hairless#n#1 Horizontal Links of cat#n#1: Upward Links of cat#n#1: feline#n#1 Downward Links of cat#n#1: domestic_cat#n#1 wildcat#n#3 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] carnivore#n#1 [D] feline#n#1 [D] cat#n#1 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] paw#n#1 [D] feline#n#1 [D] cat#n#1 Horizontal Links of untruly#r#1: untrue#a#1 Upward Links of untruly#r#1: Downward Links of untruly#r#1: Horizontal Links of expository#a#1: instructive#a#1 Upward Links of expository#a#1: Downward Links of expository#a#1: Horizontal Links of noncyclic#a#1: cyclic#a#4 cyclicity#n#1 Upward Links of noncyclic#a#1: Downward Links of noncyclic#a#1: Horizontal Links of indecisive#a#2: inconclusive#a#1 irresolute#a#1 decisive#a#1 inconclusive#a#2 Upward Links of indecisive#a#2: Downward Links of indecisive#a#2: Horizontal Links of art#n#3: Upward Links of art#n#3: superior_skill#n#1 Downward Links of art#n#3: aviation#n#3 eristic#n#2 falconry#n#1 fortification#n#2 homiletics#n#2 horology#n#1 minstrelsy#n#3 musicianship#n#1 enology#n#1 puppetry#n#1 taxidermy#n#1 ventriloquism#n#1 Horizontal Links of Hadrian#n#1: Upward Links of Hadrian#n#1: Roman_Emperor#n#1 Downward Links of Hadrian#n#1: Horizontal Links of mortise#v#1: Upward Links of mortise#v#1: cut#v#1 Downward Links of mortise#v#1: Horizontal Links of hooded_seal#n#1: Upward Links of hooded_seal#n#1: earless_seal#n#1 Downward Links of hooded_seal#n#1: Cystophora#n#1Horizontal Links of polychrome#v#1: Upward Links of polychrome#v#1: color#v#1 Downward Links of polychrome#v#1: Horizontal Links of stress#v#2: Upward Links of stress#v#2: pronounce#v#1 Downward Links of stress#v#2: Horizontal Links of professional_football#n#1: Upward Links of professional_football#n#1: football#n#1 sport#n#2 Downward Links of professional_football#n#1: Horizontal Links of scat#n#1: Upward Links of scat#n#1: singing#n#1 jazz#n#2 Downward Links of scat#n#1: WordNet-Similarity-2.05/t/keys/0.09/0000755000175000017500000000000011025552366015077 5ustar sidsidWordNet-Similarity-2.05/t/keys/0.09/lesktrace.key0000644000175000017500000000434411015336624017567 0ustar sidsidSynset 1: dog#n#1 Synset 2: cat#n#1 Functions: glos - hype glos : 2 Overlaps: 1 x "of" 1 x "many" Functions: glos - hypo glos : 19 Overlaps: 1 x "member of the genus" 1 x "the" 1 x "in" 1 x "domesticated" Functions: holo glos - glos : 2 Overlaps: 1 x "domestic" 1 x "and" Functions: holo glos - hype glos : 1 Overlaps: 1 x "of" Functions: holo glos - hypo glos : 8 Overlaps: 1 x "genus" 1 x "of the" 1 x "domestic" 1 x "and" 1 x "wild" Functions: hype glos - glos : 1 Overlaps: 1 x "and" Functions: hype glos - hype glos : 15 Overlaps: 1 x "claws" 1 x "fissiped mammals" 1 x "any of various" 1 x "with" Functions: hype glos - hypo glos : 3 Overlaps: 1 x "of" 1 x "any" 1 x "and" Functions: hypo glos - glos : 4 Overlaps: 1 x "to" 1 x "usually" 1 x "and" 1 x "having" Functions: hypo glos - hype glos : 11 Overlaps: 1 x "bodied" 1 x "any of various" 1 x "with" Functions: hypo glos - hypo glos : 16 Overlaps: 1 x "small" 1 x "the" 1 x "of" 2 x "any" 1 x "in the" 1 x "or" 1 x "resembling the" 1 x "in" 1 x "and" Functions: mero glos - hypo glos : 1 Overlaps: 1 x "or" Synset 1: untruly#r#1 Synset 2: expositive#a#1 Synset 1: noncyclic#a#1 Synset 2: indecisive#a#2 Functions: glos - also glos : 1 Overlaps: 1 x "not" Functions: glos - glos : 1 Overlaps: 1 x "not" Synset 1: art#n#3 Synset 2: Hadrian#n#1 Functions: example - glos : 3 Overlaps: 1 x "the" 1 x "of" 1 x "s" Functions: glos - glos : 3 Overlaps: 1 x "a" 2 x "and" Functions: hypo glos - glos : 13 Overlaps: 3 x "the" 2 x "to" 2 x "a" 1 x "in" 2 x "of" 2 x "and" 1 x "s" Functions: hypo glos - hype glos : 2 Overlaps: 1 x "the" 1 x "of" Synset 1: mortise#v#1 Synset 2: Cystophora_cristata#n#1 Functions: glos - hype glos : 2 Overlaps: 2 x "a" Functions: hype glos - glos : 1 Overlaps: 1 x "with" Functions: hype glos - hype glos : 1 Overlaps: 1 x "with" Synset 1: polychromize#v#1 Synset 2: stress#v#2 Functions: glos - glos : 1 Overlaps: 1 x "with" Synset 1: professional_football#n#1 Synset 2: scat#n#1 Functions: glos - glos : 1 Overlaps: 1 x "for" Functions: hype glos - glos : 6 Overlaps: 2 x "the" 1 x "to" 1 x "a" 1 x "of" 1 x "for" Functions: hype glos - hype glos : 5 Overlaps: 1 x "the" 1 x "a" 1 x "in" 2 x "of" WordNet-Similarity-2.05/t/keys/0.09/lchpairs.key0000644000175000017500000000150111015336624017407 0ustar sidsid0.9445 0.4925 1.0186 0.8755 0.6391 0.8755 1.7918 0.8755 1.2809 0.6931 0.6391 0.5878 0.8755 0.6391 0.8755 0.8109 0.8109 1.0986 0.6391 1.0186 0.8755 0.9445 0.9445 0.6931 0.8109 0.7503 1.0986 0.8109 0.9445 0.9445 1.1856 0.8755 0.9445 0.8109 0.8755 1.0186 1.2809 1.1856 1.1856 0.6931 0.9445 0.9445 0.7503 0.7503 0.7503 0.8755 0.8755 0.8755 0.9445 0.9445 1.3863 1.5041 1.0986 1.0986 1.0986 0.8755 0.9445 0.8109 1.1856 0.9445 1.1350 1.3863 1.0296 1.9459 1.2528 1.3863 1.7228 1.1350 1.5404 0.6242 1.5404 1.2528 1.3863 1.1350 1.7228 1.2528 1.1350 1.0296 1.1350 1.7228 1.1350 1.3863 0.7673 1.2528 1.5404 -1000000.0000 0.6391 0.8755 0.7503 -1000000.0000 0.7503 -1000000.0000 0.8109 1.3863 -1000000.0000 0.7503 1.2528 -1000000.0000 1.2809 0.8755 1.2809 -1000000.0000 1.0186 -1000000.0000 0.8755 1.0986 1.0186 -1000000.0000 -1000000.0000 0.9445 WordNet-Similarity-2.05/t/keys/0.09/pathpairs.key0000644000175000017500000000150111015336624017575 0ustar sidsid0.0714 0.0455 0.0769 0.0667 0.0526 0.0667 0.1667 0.0667 0.1000 0.0556 0.0526 0.0500 0.0667 0.0526 0.0667 0.0625 0.0625 0.0833 0.0526 0.0769 0.0667 0.0714 0.0714 0.0556 0.0625 0.0588 0.0833 0.0625 0.0714 0.0714 0.0909 0.0667 0.0714 0.0625 0.0667 0.0769 0.1000 0.0909 0.0909 0.0556 0.0714 0.0714 0.0588 0.0588 0.0588 0.0667 0.0667 0.0667 0.0714 0.0714 0.1111 0.1250 0.0833 0.0833 0.0833 0.0667 0.0714 0.0625 0.0909 0.0714 0.1111 0.1429 0.1000 0.2500 0.1250 0.1429 0.2000 0.1111 0.1667 0.0667 0.1667 0.1250 0.1429 0.1111 0.2000 0.1250 0.1111 0.1000 0.1111 0.2000 0.1111 0.1429 0.0769 0.1250 0.1667 -1000000.0000 0.0526 0.0667 0.0588 -1000000.0000 0.0588 -1000000.0000 0.0625 0.1111 -1000000.0000 0.0588 0.1250 -1000000.0000 0.1000 0.0667 0.1000 -1000000.0000 0.0769 -1000000.0000 0.0667 0.0833 0.0769 -1000000.0000 -1000000.0000 0.0714 WordNet-Similarity-2.05/t/keys/0.09/hsopairs_nr.key0000644000175000017500000000033411015336624020134 0ustar sidsid0 0 0 0 0 0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 WordNet-Similarity-2.05/t/keys/0.09/leskpairs.key0000644000175000017500000000144511015336624017606 0ustar sidsid6.0000 5.0000 3.0000 39.0000 7.0000 5.0000 38.0000 6.0000 7.0000 50.0000 18.0000 14.0000 3.0000 2.0000 7.0000 4.0000 4.0000 11.0000 5.0000 5.0000 4.0000 10.0000 0.0000 4.0000 3.0000 8.0000 5.0000 27.0000 14.0000 15.0000 50.0000 17.0000 13.0000 25.0000 8.0000 7.0000 2.0000 9.0000 12.0000 12.0000 6.0000 8.0000 8.0000 0.0000 4.0000 14.0000 6.0000 15.0000 2.0000 1.0000 11.0000 17.0000 5.0000 5.0000 4.0000 5.0000 9.0000 3.0000 14.0000 15.0000 5.0000 3.0000 1.0000 2.0000 8.0000 7.0000 3.0000 2.0000 8.0000 0.0000 11.0000 0.0000 2.0000 1.0000 16.0000 6.0000 1.0000 5.0000 4.0000 5.0000 1.0000 4.0000 3.0000 5.0000 8.0000 0.0000 16.0000 41.0000 13.0000 3.0000 15.0000 3.0000 6.0000 3.0000 1.0000 2.0000 10.0000 2.0000 12.0000 12.0000 16.0000 1.0000 5.0000 3.0000 25.0000 11.0000 5.0000 8.0000 7.0000 12.0000 WordNet-Similarity-2.05/t/keys/0.09/jcntrace.key0000644000175000017500000000634211015336624017403 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.09/lchtrace.key0000644000175000017500000000660111015336624017375 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (Length=5) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (Length=14) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=19) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (Length=6)HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): activity#n#1 (Length=8) activity#n#1 (Length=10) activity#n#1 (Length=11) activity#n#1 (Length=12) diversion#n#1 (Length=12) activity#n#1 (Length=12) diversion#n#1 (Length=14) activity#n#1 (Length=15) *Root*#n#1 (Length=15) *Root*#n#1 (Length=16) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=20) *Root*#n#1 (Length=20)WordNet-Similarity-2.05/t/keys/0.09/jcnpairs.key0000644000175000017500000000150111015336624017413 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0424 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0408 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0502 0.0774 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0532 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0415 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.09/respairs.key0000644000175000017500000000150111015336624017432 0ustar sidsid0.0000 0.0000 0.8555 0.0000 0.0000 0.0000 4.3095 0.0000 2.5580 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.3709 1.3709 0.0000 0.0000 0.8555 0.8555 1.3709 0.0000 1.3709 0.0000 0.8555 0.0000 0.0000 0.0000 0.8555 0.0000 0.0000 0.8555 0.0000 0.0000 2.5356 2.4549 0.0000 0.0000 2.4596 0.0000 0.0000 0.0000 0.8555 0.0000 0.8555 1.3709 1.7064 0.0000 2.1429 3.7388 0.0000 0.0000 2.5580 0.0000 0.8555 0.8555 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.09/wuptrace.key0000644000175000017500000000606511015336624017446 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumers: carnivore#n#1 (Depth=11) Depth(dog#n#1) = 13 Depth(cat#n#1) = 13 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumers: *Root*#n#1 (Depth=1) Depth(art#n#3) = 6 Depth(Hadrian#n#1) = 9 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumers: *Root*#v#1 (Depth=1) Depth(polychromize#v#1) = 4 Depth(stress#v#2) = 3 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumers: diversion#n#1 (Depth=4) Depth(professional_football#n#1) = 6 Depth(scat#n#1) = 7 WordNet-Similarity-2.05/t/keys/0.09/restrace.key0000644000175000017500000000567711015336624017434 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.09/respairs_nr.key0000644000175000017500000000251611015336624020140 0ustar sidsid-1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 4.3095 -1000000.0000 2.5580 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 1.3709 -1000000.0000 -1000000.0000 0.8555 0.8555 1.3709 -1000000.0000 1.3709 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 2.5356 2.4549 -1000000.0000 -1000000.0000 2.4596 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 0.8555 1.3709 1.7064 -1000000.0000 2.1429 3.7388 -1000000.0000 -1000000.0000 2.5580 -1000000.0000 0.8555 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.09/pathtrace.key0000644000175000017500000000633411015336624017566 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Shortest path: dog#n#1 canine#n#2 carnivore#n#1 feline#n#1 cat#n#1 Path length = 5 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Shortest path: art#n#3 superior_skill#n#1 ability#n#2 cognition#n#1 psychological_feature#n#1 *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Path length = 14 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Shortest path: polychrome#v#1 color#v#1 change#v#2 *Root*#v#1 pronounce#v#1 stress#v#2 Path length = 6HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Shortest path: professional_football#n#1 sport#n#2 occupation#n#1 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 Path length = 8WordNet-Similarity-2.05/t/keys/0.09/linpairs.key0000644000175000017500000000150111015336624017423 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 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0.5000 0.1667 0.1053 0.4762 0.1333 0.1111 0.1111 0.2000 0.1250 0.2222 0.3333 0.2353 0.1333 0.3333 0.5882 0.1538 0.1538 0.4211 0.1250 0.2353 0.2105 0.1667 0.1333 0.2000 0.2500 0.1818 0.4000 0.2222 0.2500 0.3333 0.2000 0.2857 0.1250 0.2857 0.2222 0.2500 0.2000 0.3333 0.2222 0.2000 0.1818 0.2000 0.3333 0.2000 0.2500 0.1429 0.2222 0.2857 -1000000.0000 0.1000 0.1250 0.1111 -1000000.0000 0.1111 -1000000.0000 0.1176 0.4615 -1000000.0000 0.1111 0.2222 -1000000.0000 0.1818 0.1250 0.1818 -1000000.0000 0.1429 -1000000.0000 0.1250 0.3529 0.1429 -1000000.0000 -1000000.0000 0.1333 WordNet-Similarity-2.05/t/keys/0.09/hsopairs.key0000644000175000017500000000140211015336624017432 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 3.0000 0.0000 0.0000 3.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.09/lintrace.key0000644000175000017500000000634211015336624017413 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.09/pathpairs_nr.key0000644000175000017500000000251611015336624020303 0ustar sidsid-1000000.0000 -1000000.0000 0.0769 -1000000.0000 -1000000.0000 -1000000.0000 0.1667 -1000000.0000 0.1000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 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-1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.09/lchpairs_nr.key0000644000175000017500000000251611015336624020115 0ustar sidsid-1000000.0000 -1000000.0000 0.9614 -1000000.0000 -1000000.0000 -1000000.0000 1.7346 -1000000.0000 1.2238 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.7538 1.0415 -1000000.0000 -1000000.0000 0.8183 0.8873 0.8873 -1000000.0000 0.7538 -1000000.0000 1.0415 -1000000.0000 -1000000.0000 -1000000.0000 1.1285 -1000000.0000 -1000000.0000 0.7538 -1000000.0000 -1000000.0000 1.2238 1.1285 -1000000.0000 -1000000.0000 0.8873 -1000000.0000 -1000000.0000 -1000000.0000 0.6931 -1000000.0000 0.8183 0.8183 0.6931 -1000000.0000 0.8938 1.4469 -1000000.0000 -1000000.0000 1.0415 -1000000.0000 0.8873 0.7538 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 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WordNet-Similarity-2.05/t/keys/0.09/hsotrace.key0000644000175000017500000000434511015336624017423 0ustar sidsidHorizontal Links of dog#n#1: Upward Links of dog#n#1: canine#n#2 flag#n#7 Downward Links of dog#n#1: Canis#n#1 pack#n#6 pooch#n#1 cur#n#1 lapdog#n#1 toy_dog#n#1 hunting_dog#n#1 working_dog#n#1 dalmatian#n#2 basenji#n#1 pug#n#1 Leonberg#n#1 Newfoundland#n#1 Great_Pyrenees#n#1 spitz#n#1 griffon#n#1 corgi#n#1 poodle#n#1 Mexican_hairless#n#1 Horizontal Links of cat#n#1: Upward Links of cat#n#1: feline#n#1 Downward Links of cat#n#1: domestic_cat#n#1 wildcat#n#3 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] carnivore#n#1 [D] feline#n#1 [D] cat#n#1 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] paw#n#1 [D] feline#n#1 [D] cat#n#1 Horizontal Links of untruly#r#1: untrue#a#1 Upward Links of untruly#r#1: Downward Links of untruly#r#1: Horizontal Links of expository#a#1: instructive#a#1 Upward Links of expository#a#1: Downward Links of expository#a#1: Horizontal Links of noncyclic#a#1: cyclic#a#4 cyclicity#n#1 Upward Links of noncyclic#a#1: Downward Links of noncyclic#a#1: Horizontal Links of indecisive#a#2: inconclusive#a#1 irresolute#a#1 decisive#a#1 inconclusive#a#2 Upward Links of indecisive#a#2: Downward Links of indecisive#a#2: Horizontal Links of art#n#3: Upward Links of art#n#3: superior_skill#n#1 Downward Links of art#n#3: aviation#n#3 eristic#n#2 falconry#n#1 fortification#n#2 homiletics#n#2 horology#n#1 minstrelsy#n#3 musicianship#n#1 enology#n#1 puppetry#n#1 taxidermy#n#1 ventriloquism#n#1 Horizontal Links of Hadrian#n#1: Upward Links of Hadrian#n#1: Roman_Emperor#n#1 Downward Links of Hadrian#n#1: Horizontal Links of mortise#v#1: Upward Links of mortise#v#1: cut#v#1 Downward Links of mortise#v#1: Horizontal Links of hooded_seal#n#1: Upward Links of hooded_seal#n#1: earless_seal#n#1 Downward Links of hooded_seal#n#1: Cystophora#n#1Horizontal Links of polychrome#v#1: Upward Links of polychrome#v#1: color#v#1 Downward Links of polychrome#v#1: Horizontal Links of stress#v#2: Upward Links of stress#v#2: pronounce#v#1 Downward Links of stress#v#2: Horizontal Links of professional_football#n#1: Upward Links of professional_football#n#1: football#n#1 sport#n#2 Downward Links of professional_football#n#1: Horizontal Links of scat#n#1: Upward Links of scat#n#1: singing#n#1 jazz#n#2 Downward Links of scat#n#1: WordNet-Similarity-2.05/t/keys/linpairs.key0000644000175000017500000000146211015336623017042 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 undef 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.10/0000755000175000017500000000000011025552366015067 5ustar sidsidWordNet-Similarity-2.05/t/keys/0.10/lesktrace.key0000644000175000017500000000434411015336624017557 0ustar sidsidSynset 1: dog#n#1 Synset 2: cat#n#1 Functions: glos - hype glos : 2 Overlaps: 1 x "of" 1 x "many" Functions: glos - hypo glos : 19 Overlaps: 1 x "member of the genus" 1 x "the" 1 x "in" 1 x "domesticated" Functions: holo glos - glos : 2 Overlaps: 1 x "domestic" 1 x "and" Functions: holo glos - hype glos : 1 Overlaps: 1 x "of" Functions: holo glos - hypo glos : 8 Overlaps: 1 x "genus" 1 x "of the" 1 x "domestic" 1 x "and" 1 x "wild" Functions: hype glos - glos : 1 Overlaps: 1 x "and" Functions: hype glos - hype glos : 15 Overlaps: 1 x "claws" 1 x "fissiped mammals" 1 x "any of various" 1 x "with" Functions: hype glos - hypo glos : 3 Overlaps: 1 x "of" 1 x "any" 1 x "and" Functions: hypo glos - glos : 4 Overlaps: 1 x "to" 1 x "usually" 1 x "and" 1 x "having" Functions: hypo glos - hype glos : 11 Overlaps: 1 x "bodied" 1 x "any of various" 1 x "with" Functions: hypo glos - hypo glos : 16 Overlaps: 1 x "small" 1 x "the" 1 x "of" 2 x "any" 1 x "in the" 1 x "or" 1 x "resembling the" 1 x "in" 1 x "and" Functions: mero glos - hypo glos : 1 Overlaps: 1 x "or" Synset 1: untruly#r#1 Synset 2: expositive#a#1 Synset 1: noncyclic#a#1 Synset 2: indecisive#a#2 Functions: glos - also glos : 1 Overlaps: 1 x "not" Functions: glos - glos : 1 Overlaps: 1 x "not" Synset 1: art#n#3 Synset 2: Hadrian#n#1 Functions: example - glos : 3 Overlaps: 1 x "the" 1 x "of" 1 x "s" Functions: glos - glos : 3 Overlaps: 1 x "a" 2 x "and" Functions: hypo glos - glos : 13 Overlaps: 3 x "the" 2 x "to" 2 x "a" 1 x "in" 2 x "of" 2 x "and" 1 x "s" Functions: hypo glos - hype glos : 2 Overlaps: 1 x "the" 1 x "of" Synset 1: mortise#v#1 Synset 2: Cystophora_cristata#n#1 Functions: glos - hype glos : 2 Overlaps: 2 x "a" Functions: hype glos - glos : 1 Overlaps: 1 x "with" Functions: hype glos - hype glos : 1 Overlaps: 1 x "with" Synset 1: polychromize#v#1 Synset 2: stress#v#2 Functions: glos - glos : 1 Overlaps: 1 x "with" Synset 1: professional_football#n#1 Synset 2: scat#n#1 Functions: glos - glos : 1 Overlaps: 1 x "for" Functions: hype glos - glos : 6 Overlaps: 2 x "the" 1 x "to" 1 x "a" 1 x "of" 1 x "for" Functions: hype glos - hype glos : 5 Overlaps: 1 x "the" 1 x "a" 1 x "in" 2 x "of" WordNet-Similarity-2.05/t/keys/0.10/lchpairs.key0000644000175000017500000000150111015336624017377 0ustar sidsid0.9445 0.4925 1.0186 0.8755 0.6391 0.8755 1.7918 0.8755 1.2809 0.6931 0.6391 0.5878 0.8755 0.6391 0.8755 0.8109 0.8109 1.0986 0.6391 1.0186 0.8755 0.9445 0.9445 0.6931 0.8109 0.7503 1.0986 0.8109 0.9445 0.9445 1.1856 0.8755 0.9445 0.8109 0.8755 1.0186 1.2809 1.1856 1.1856 0.6931 0.9445 0.9445 0.7503 0.7503 0.7503 0.8755 0.8755 0.8755 0.9445 0.9445 1.3863 1.5041 1.0986 1.0986 1.0986 0.8755 0.9445 0.8109 1.1856 0.9445 1.1350 1.3863 1.0296 1.9459 1.2528 1.3863 1.7228 1.1350 1.5404 0.6242 1.5404 1.2528 1.3863 1.1350 1.7228 1.2528 1.1350 1.0296 1.1350 1.7228 1.1350 1.3863 0.7673 1.2528 1.5404 -1000000.0000 0.6391 0.8755 0.7503 -1000000.0000 0.7503 -1000000.0000 0.8109 1.3863 -1000000.0000 0.7503 1.2528 -1000000.0000 1.2809 0.8755 1.2809 -1000000.0000 1.0186 -1000000.0000 0.8755 1.0986 1.0186 -1000000.0000 -1000000.0000 0.9445 WordNet-Similarity-2.05/t/keys/0.10/pathpairs.key0000644000175000017500000000150111015336624017565 0ustar sidsid0.0714 0.0455 0.0769 0.0667 0.0526 0.0667 0.1667 0.0667 0.1000 0.0556 0.0526 0.0500 0.0667 0.0526 0.0667 0.0625 0.0625 0.0833 0.0526 0.0769 0.0667 0.0714 0.0714 0.0556 0.0625 0.0588 0.0833 0.0625 0.0714 0.0714 0.0909 0.0667 0.0714 0.0625 0.0667 0.0769 0.1000 0.0909 0.0909 0.0556 0.0714 0.0714 0.0588 0.0588 0.0588 0.0667 0.0667 0.0667 0.0714 0.0714 0.1111 0.1250 0.0833 0.0833 0.0833 0.0667 0.0714 0.0625 0.0909 0.0714 0.1111 0.1429 0.1000 0.2500 0.1250 0.1429 0.2000 0.1111 0.1667 0.0667 0.1667 0.1250 0.1429 0.1111 0.2000 0.1250 0.1111 0.1000 0.1111 0.2000 0.1111 0.1429 0.0769 0.1250 0.1667 -1000000.0000 0.0526 0.0667 0.0588 -1000000.0000 0.0588 -1000000.0000 0.0625 0.1111 -1000000.0000 0.0588 0.1250 -1000000.0000 0.1000 0.0667 0.1000 -1000000.0000 0.0769 -1000000.0000 0.0667 0.0833 0.0769 -1000000.0000 -1000000.0000 0.0714 WordNet-Similarity-2.05/t/keys/0.10/hsopairs_nr.key0000644000175000017500000000033411015336624020124 0ustar sidsid0 0 0 0 0 0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 WordNet-Similarity-2.05/t/keys/0.10/leskpairs.key0000644000175000017500000000144511015336624017576 0ustar sidsid6.0000 5.0000 3.0000 39.0000 7.0000 5.0000 38.0000 6.0000 7.0000 50.0000 18.0000 14.0000 3.0000 2.0000 7.0000 4.0000 4.0000 11.0000 5.0000 5.0000 4.0000 10.0000 0.0000 4.0000 3.0000 8.0000 5.0000 27.0000 14.0000 15.0000 50.0000 17.0000 13.0000 25.0000 8.0000 7.0000 2.0000 9.0000 12.0000 12.0000 6.0000 8.0000 8.0000 0.0000 4.0000 14.0000 6.0000 15.0000 2.0000 1.0000 11.0000 17.0000 5.0000 5.0000 4.0000 5.0000 9.0000 3.0000 14.0000 15.0000 5.0000 3.0000 1.0000 2.0000 8.0000 7.0000 3.0000 2.0000 8.0000 0.0000 11.0000 0.0000 2.0000 1.0000 16.0000 6.0000 1.0000 5.0000 4.0000 5.0000 1.0000 4.0000 3.0000 5.0000 8.0000 0.0000 16.0000 41.0000 13.0000 3.0000 15.0000 3.0000 6.0000 3.0000 1.0000 2.0000 10.0000 2.0000 12.0000 12.0000 16.0000 1.0000 5.0000 3.0000 25.0000 11.0000 5.0000 8.0000 7.0000 12.0000 WordNet-Similarity-2.05/t/keys/0.10/jcntrace.key0000644000175000017500000000634211015336624017373 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.10/lchtrace.key0000644000175000017500000000660111015336624017365 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (Length=5) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (Length=14) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=19) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (Length=6)HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): activity#n#1 (Length=8) activity#n#1 (Length=10) activity#n#1 (Length=11) activity#n#1 (Length=12) diversion#n#1 (Length=12) activity#n#1 (Length=12) diversion#n#1 (Length=14) activity#n#1 (Length=15) *Root*#n#1 (Length=15) *Root*#n#1 (Length=16) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=20) *Root*#n#1 (Length=20)WordNet-Similarity-2.05/t/keys/0.10/jcnpairs.key0000644000175000017500000000150111015336624017403 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0424 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0408 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0502 0.0774 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0532 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0415 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.10/respairs.key0000644000175000017500000000150111015336624017422 0ustar sidsid0.0000 0.0000 0.8555 0.0000 0.0000 0.0000 4.3095 0.0000 2.5580 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.3709 1.3709 0.0000 0.0000 0.8555 0.8555 1.3709 0.0000 1.3709 0.0000 0.8555 0.0000 0.0000 0.0000 0.8555 0.0000 0.0000 0.8555 0.0000 0.0000 2.5356 2.4549 0.0000 0.0000 2.4596 0.0000 0.0000 0.0000 0.8555 0.0000 0.8555 1.3709 1.7064 0.0000 2.1429 3.7388 0.0000 0.0000 2.5580 0.0000 0.8555 0.8555 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.10/wuptrace.key0000644000175000017500000000606511015336624017436 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumers: carnivore#n#1 (Depth=11) Depth(dog#n#1) = 13 Depth(cat#n#1) = 13 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumers: *Root*#n#1 (Depth=1) Depth(art#n#3) = 6 Depth(Hadrian#n#1) = 9 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumers: *Root*#v#1 (Depth=1) Depth(polychromize#v#1) = 4 Depth(stress#v#2) = 3 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumers: diversion#n#1 (Depth=4) Depth(professional_football#n#1) = 6 Depth(scat#n#1) = 7 WordNet-Similarity-2.05/t/keys/0.10/restrace.key0000644000175000017500000000567711015336624017424 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.10/respairs_nr.key0000644000175000017500000000251611015336624020130 0ustar sidsid-1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 4.3095 -1000000.0000 2.5580 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 1.3709 -1000000.0000 -1000000.0000 0.8555 0.8555 1.3709 -1000000.0000 1.3709 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 2.5356 2.4549 -1000000.0000 -1000000.0000 2.4596 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 0.8555 1.3709 1.7064 -1000000.0000 2.1429 3.7388 -1000000.0000 -1000000.0000 2.5580 -1000000.0000 0.8555 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.10/pathtrace.key0000644000175000017500000000633411015336624017556 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Shortest path: dog#n#1 canine#n#2 carnivore#n#1 feline#n#1 cat#n#1 Path length = 5 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Shortest path: art#n#3 superior_skill#n#1 ability#n#2 cognition#n#1 psychological_feature#n#1 *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Path length = 14 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Shortest path: polychrome#v#1 color#v#1 change#v#2 *Root*#v#1 pronounce#v#1 stress#v#2 Path length = 6HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Shortest path: professional_football#n#1 sport#n#2 occupation#n#1 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 Path length = 8WordNet-Similarity-2.05/t/keys/0.10/linpairs.key0000644000175000017500000000150111015336624017413 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 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0.5000 0.1667 0.1053 0.4762 0.1333 0.1111 0.1111 0.2000 0.1250 0.2222 0.3333 0.2353 0.1333 0.3333 0.5882 0.1538 0.1538 0.4211 0.1250 0.2353 0.2105 0.1667 0.1333 0.2000 0.2500 0.1818 0.4000 0.2222 0.2500 0.3333 0.2000 0.2857 0.1250 0.2857 0.2222 0.2500 0.2000 0.3333 0.2222 0.2000 0.1818 0.2000 0.3333 0.2000 0.2500 0.1429 0.2222 0.2857 -1000000.0000 0.1000 0.1250 0.1111 -1000000.0000 0.1111 -1000000.0000 0.1176 0.4615 -1000000.0000 0.1111 0.2222 -1000000.0000 0.1818 0.1250 0.1818 -1000000.0000 0.1429 -1000000.0000 0.1250 0.3529 0.1429 -1000000.0000 -1000000.0000 0.1333 WordNet-Similarity-2.05/t/keys/0.10/hsopairs.key0000644000175000017500000000140211015336624017422 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 3.0000 0.0000 0.0000 3.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.10/lintrace.key0000644000175000017500000000634211015336624017403 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.10/pathpairs_nr.key0000644000175000017500000000251611015336624020273 0ustar sidsid-1000000.0000 -1000000.0000 0.0769 -1000000.0000 -1000000.0000 -1000000.0000 0.1667 -1000000.0000 0.1000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 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-1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.10/lchpairs_nr.key0000644000175000017500000000251611015336624020105 0ustar sidsid-1000000.0000 -1000000.0000 0.9614 -1000000.0000 -1000000.0000 -1000000.0000 1.7346 -1000000.0000 1.2238 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.7538 1.0415 -1000000.0000 -1000000.0000 0.8183 0.8873 0.8873 -1000000.0000 0.7538 -1000000.0000 1.0415 -1000000.0000 -1000000.0000 -1000000.0000 1.1285 -1000000.0000 -1000000.0000 0.7538 -1000000.0000 -1000000.0000 1.2238 1.1285 -1000000.0000 -1000000.0000 0.8873 -1000000.0000 -1000000.0000 -1000000.0000 0.6931 -1000000.0000 0.8183 0.8183 0.6931 -1000000.0000 0.8938 1.4469 -1000000.0000 -1000000.0000 1.0415 -1000000.0000 0.8873 0.7538 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 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WordNet-Similarity-2.05/t/keys/0.10/hsotrace.key0000644000175000017500000000434511015336624017413 0ustar sidsidHorizontal Links of dog#n#1: Upward Links of dog#n#1: canine#n#2 flag#n#7 Downward Links of dog#n#1: Canis#n#1 pack#n#6 pooch#n#1 cur#n#1 lapdog#n#1 toy_dog#n#1 hunting_dog#n#1 working_dog#n#1 dalmatian#n#2 basenji#n#1 pug#n#1 Leonberg#n#1 Newfoundland#n#1 Great_Pyrenees#n#1 spitz#n#1 griffon#n#1 corgi#n#1 poodle#n#1 Mexican_hairless#n#1 Horizontal Links of cat#n#1: Upward Links of cat#n#1: feline#n#1 Downward Links of cat#n#1: domestic_cat#n#1 wildcat#n#3 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] carnivore#n#1 [D] feline#n#1 [D] cat#n#1 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] paw#n#1 [D] feline#n#1 [D] cat#n#1 Horizontal Links of untruly#r#1: untrue#a#1 Upward Links of untruly#r#1: Downward Links of untruly#r#1: Horizontal Links of expository#a#1: instructive#a#1 Upward Links of expository#a#1: Downward Links of expository#a#1: Horizontal Links of noncyclic#a#1: cyclic#a#4 cyclicity#n#1 Upward Links of noncyclic#a#1: Downward Links of noncyclic#a#1: Horizontal Links of indecisive#a#2: inconclusive#a#1 irresolute#a#1 decisive#a#1 inconclusive#a#2 Upward Links of indecisive#a#2: Downward Links of indecisive#a#2: Horizontal Links of art#n#3: Upward Links of art#n#3: superior_skill#n#1 Downward Links of art#n#3: aviation#n#3 eristic#n#2 falconry#n#1 fortification#n#2 homiletics#n#2 horology#n#1 minstrelsy#n#3 musicianship#n#1 enology#n#1 puppetry#n#1 taxidermy#n#1 ventriloquism#n#1 Horizontal Links of Hadrian#n#1: Upward Links of Hadrian#n#1: Roman_Emperor#n#1 Downward Links of Hadrian#n#1: Horizontal Links of mortise#v#1: Upward Links of mortise#v#1: cut#v#1 Downward Links of mortise#v#1: Horizontal Links of hooded_seal#n#1: Upward Links of hooded_seal#n#1: earless_seal#n#1 Downward Links of hooded_seal#n#1: Cystophora#n#1Horizontal Links of polychrome#v#1: Upward Links of polychrome#v#1: color#v#1 Downward Links of polychrome#v#1: Horizontal Links of stress#v#2: Upward Links of stress#v#2: pronounce#v#1 Downward Links of stress#v#2: Horizontal Links of professional_football#n#1: Upward Links of professional_football#n#1: football#n#1 sport#n#2 Downward Links of professional_football#n#1: Horizontal Links of scat#n#1: Upward Links of scat#n#1: singing#n#1 jazz#n#2 Downward Links of scat#n#1: WordNet-Similarity-2.05/t/keys/wndepths.txt0000644000175000017500000001627711015336623017116 0ustar sidsidn 00000000 20 n 00001740 19 v 00000000 14 v 00001740 4 v 00010435 4 v 00014549 2 v 00015713 1 v 00016702 1 v 00017282 2 v 00017865 3 v 00018158 1 v 00020133 2 v 00020449 1 v 00052374 1 v 00057410 1 v 00059559 1 v 00077369 1 v 00077458 1 v 00078760 5 v 00099721 3 v 00104868 5 v 00107943 2 v 00109660 8 v 00118764 1 v 00123170 4 v 00126264 8 v 00173338 5 v 00224295 1 v 00258109 2 v 00306298 1 v 00339934 3 v 00345761 3 v 00348746 4 v 00360092 2 v 00413876 2 v 00415044 2 v 00415743 1 v 00422090 3 v 00425071 3 v 00426958 2 v 00528990 2 v 00549771 1 v 00550117 3 v 00554298 1 v 00560247 1 v 00571061 2 v 00588888 4 v 00591115 3 v 00591755 1 v 00592037 2 v 00594058 1 v 00594337 1 v 00594621 3 v 00595505 1 v 00595630 2 v 00595935 3 v 00596132 2 v 00597915 4 v 00598954 3 v 00599992 3 v 00601378 1 v 00601581 1 v 00607405 2 v 00607780 5 v 00608808 1 v 00609100 2 v 00609683 5 v 00610167 3 v 00610538 3 v 00611256 2 v 00611481 1 v 00613393 1 v 00613683 3 v 00614999 2 v 00616153 2 v 00616857 2 v 00617748 4 v 00628491 9 v 00636888 2 v 00639849 1 v 00643473 2 v 00644583 5 v 00652346 1 v 00665886 5 v 00679389 2 v 00685419 1 v 00697589 5 v 00699815 6 v 00708538 5 v 00720063 3 v 00732552 1 v 00736216 2 v 00770437 5 v 00779601 2 v 00799383 1 v 00801626 1 v 00804802 5 v 00805376 7 v 00811375 3 v 00818974 5 v 00843468 4 v 00859325 3 v 00890590 2 v 00892923 1 v 00905059 1 v 00907147 3 v 00918872 3 v 00920336 2 v 00931852 5 v 00935141 1 v 00935987 2 v 00937619 1 v 00940384 8 v 00950337 1 v 00950431 1 v 00973728 2 v 00978549 3 v 00983824 5 v 00989201 2 v 00989490 1 v 01006810 2 v 01009821 1 v 01011725 6 v 01020731 1 v 01029368 1 v 01030132 5 v 01035530 3 v 01041061 1 v 01066433 1 v 01068380 1 v 01070777 1 v 01072262 6 v 01076615 3 v 01079480 3 v 01082606 2 v 01083044 1 v 01087197 2 v 01087835 1 v 01090335 5 v 01093786 2 v 01099592 2 v 01100145 2 v 01108148 5 v 01111028 4 v 01113620 1 v 01113975 2 v 01116447 3 v 01118081 2 v 01120069 3 v 01133825 4 v 01134238 1 v 01135371 1 v 01154175 1 v 01155090 1 v 01156834 5 v 01158572 3 v 01158872 5 v 01188342 1 v 01189113 1 v 01190948 2 v 01192992 1 v 01196037 3 v 01197750 1 v 01200934 1 v 01205331 1 v 01205696 7 v 01206218 7 v 01207149 1 v 01209678 1 v 01212230 4 v 01213146 1 v 01214265 6 v 01216670 5 v 01217043 3 v 01228524 1 v 01230710 3 v 01237901 2 v 01307896 1 v 01315613 3 v 01323958 4 v 01325536 2 v 01325774 2 v 01328012 1 v 01332730 4 v 01336635 2 v 01339505 2 v 01345109 3 v 01346003 2 v 01354673 6 v 01375295 2 v 01378556 5 v 01380638 4 v 01397210 3 v 01434140 1 v 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v 01857717 2 v 01859221 3 v 01860795 2 v 01864230 2 v 01868899 1 v 01880673 2 v 01899017 1 v 01900760 1 v 01944252 1 v 01965156 1 v 01981279 1 v 01983597 1 v 01989562 2 v 01997512 1 v 01999798 3 v 02005948 4 v 02009200 1 v 02009433 5 v 02016523 6 v 02020590 3 v 02023107 4 v 02031158 4 v 02035315 1 v 02052476 3 v 02067689 1 v 02078591 2 v 02081578 2 v 02086805 1 v 02087472 1 v 02098964 1 v 02105657 1 v 02105810 1 v 02106506 4 v 02108654 1 v 02108791 1 v 02115778 4 v 02118476 2 v 02118933 1 v 02119241 2 v 02121423 1 v 02123903 5 v 02124632 1 v 02125460 1 v 02127853 2 v 02128066 2 v 02128653 2 v 02130017 1 v 02130524 3 v 02131279 3 v 02131963 1 v 02135048 2 v 02136271 2 v 02137132 4 v 02144835 4 v 02145814 2 v 02146790 2 v 02150510 4 v 02151966 2 v 02157100 2 v 02158034 1 v 02166761 1 v 02168555 2 v 02179518 3 v 02190077 1 v 02191766 3 v 02193194 2 v 02194286 2 v 02197091 1 v 02197505 1 v 02198602 1 v 02200686 4 v 02201138 1 v 02203362 8 v 02204692 2 v 02205098 2 v 02205272 5 v 02206619 5 v 02209936 2 v 02210855 5 v 02220461 8 v 02224055 5 v 02227741 3 v 02237338 2 v 02247390 1 v 02248465 1 v 02252755 1 v 02254155 2 v 02260362 4 v 02265231 5 v 02265979 2 v 02266920 2 v 02280018 1 v 02287618 2 v 02287789 2 v 02288155 1 v 02288828 1 v 02289134 1 v 02298632 2 v 02315938 1 v 02316868 3 v 02317094 1 v 02318403 1 v 02350175 3 v 02358034 1 v 02358655 1 v 02358922 1 v 02363742 1 v 02367363 11 v 02371352 1 v 02378330 1 v 02379528 4 v 02384041 3 v 02386266 1 v 02404224 5 v 02404904 2 v 02406585 4 v 02407766 1 v 02409412 3 v 02410855 4 v 02412089 1 v 02413480 3 v 02417504 2 v 02418421 1 v 02419073 3 v 02421374 2 v 02422026 3 v 02423183 2 v 02423650 1 v 02423762 7 v 02424652 2 v 02426171 2 v 02426395 3 v 02427334 2 v 02431320 2 v 02434859 1 v 02434976 4 v 02436140 1 v 02441022 6 v 02445100 1 v 02445925 2 v 02450505 5 v 02452885 4 v 02463510 3 v 02473890 1 v 02475922 6 v 02477755 1 v 02481900 1 v 02486932 4 v 02488168 1 v 02495038 2 v 02497992 1 v 02499629 4 v 02500397 1 v 02500489 1 v 02502916 1 v 02511276 2 v 02522319 1 v 02523221 1 v 02523521 1 v 02524171 4 v 02528380 2 v 02529284 4 v 02532886 1 v 02534062 2 v 02534936 1 v 02535360 1 v 02539788 2 v 02547225 2 v 02551602 1 v 02551832 3 v 02556126 7 v 02560585 3 v 02561332 2 v 02564146 2 v 02566092 2 v 02568672 2 v 02578510 2 v 02578872 2 v 02581477 1 v 02581675 1 v 02590253 1 v 02594469 1 v 02595840 2 v 02596113 1 v 02596275 1 v 02596371 1 v 02597095 1 v 02597762 1 v 02598143 2 v 02598765 1 v 02599528 1 v 02600779 1 v 02603699 6 v 02604760 6 v 02608347 2 v 02609764 3 v 02614181 1 v 02614387 2 v 02614970 1 v 02616386 1 v 02616713 2 v 02617798 1 v 02618149 2 v 02619122 2 v 02619739 1 v 02620587 3 v 02621853 1 v 02622234 4 v 02623529 4 v 02627666 2 v 02627934 2 v 02629256 2 v 02630189 4 v 02632353 2 v 02632838 1 v 02632940 5 v 02637938 2 v 02640440 3 v 02644234 2 v 02653862 1 v 02655135 6 v 02658283 1 v 02662979 2 v 02663848 1 v 02664017 1 v 02664769 7 v 02664992 2 v 02666239 4 v 02668523 2 v 02669477 2 v 02671761 1 v 02673630 1 v 02673965 2 v 02676054 4 v 02678438 1 v 02679227 1 v 02680814 3 v 02681639 1 v 02681795 4 v 02687251 1 v 02687385 1 v 02700666 1 v 02700772 1 v 02703289 2 v 02705832 1 v 02707429 1 v 02708420 3 v 02709906 2 v 02710402 2 v 02711987 3 v 02712443 1 v 02712772 1 v 02713372 3 v 02714057 1 v 02714731 1 v 02716165 2 v 02719016 2 v 02719930 2 v 02720697 1 v 02722449 1 v 02725714 3 v 02731024 2 v 02732798 2 v 02733806 1 v 02734653 1 v 02736259 1 v 02737187 1 v 02740745 1 v 02740859 1 v 02741003 1 v 02741149 2 v 02741682 1 v 02741793 1 v 02742232 1 v 02742482 1 v 02743921 1 v 02744451 1 v 02744651 1 v 02745486 1 v 02749904 1 v 02756821 4 v 02758826 1 v 02761897 2 v 02762468 4 v 02763283 1 v 02763740 3 v 02765464 2 v 02767308 3 v 02769241 2 v 02770170 1 v 02770535 1 v 02771169 1 wnver::eOS9lXC6GvMWznF1wkZofDdtbBU WordNet-Similarity-2.05/t/keys/0.13/0000755000175000017500000000000011025552366015072 5ustar sidsidWordNet-Similarity-2.05/t/keys/0.13/lesktrace.key0000644000175000017500000000427211015336624017562 0ustar sidsidSynset 1: dog#n#1 Synset 2: cat#n#1 Functions: glos - hype glos : 2 Overlaps: 1 x "of" 1 x "many" Functions: glos - hypo glos : 19 Overlaps: 1 x "the" 1 x "member of the genus" 1 x "in" 1 x "domesticated" Functions: holo glos - glos : 2 Overlaps: 1 x "domestic" 1 x "and" Functions: holo glos - hype glos : 1 Overlaps: 1 x "of" Functions: holo glos - hypo glos : 8 Overlaps: 1 x "genus" 1 x "domestic" 1 x "of the" 1 x "and" 1 x "wild" Functions: hype glos - glos : 1 Overlaps: 1 x "and" Functions: hype glos - hype glos : 15 Overlaps: 1 x "claws" 1 x "fissiped mammals" 1 x "with" 1 x "any of various" Functions: hype glos - hypo glos : 3 Overlaps: 1 x "of" 1 x "any" 1 x "and" Functions: hypo glos - glos : 4 Overlaps: 1 x "to" 1 x "usually" 1 x "having" 1 x "and" Functions: hypo glos - hype glos : 10 Overlaps: 1 x "with" 1 x "any of various" Functions: hypo glos - hypo glos : 15 Overlaps: 1 x "the" 1 x "small" 1 x "in the" 1 x "or" 1 x "of" 1 x "resembling the" 2 x "any" 1 x "and" Functions: mero glos - hypo glos : 1 Overlaps: 1 x "or" Synset 1: untruly#r#1 Synset 2: expositive#a#1 Synset 1: noncyclic#a#1 Synset 2: indecisive#a#2 Functions: glos - also glos : 1 Overlaps: 1 x "not" Functions: glos - glos : 1 Overlaps: 1 x "not" Synset 1: art#n#3 Synset 2: Hadrian#n#1 Functions: example - glos : 2 Overlaps: 1 x "the" 1 x "of" Functions: glos - glos : 3 Overlaps: 1 x "a" 2 x "and" Functions: hypo glos - glos : 10 Overlaps: 2 x "the" 2 x "to" 1 x "a" 1 x "in" 2 x "of" 2 x "and" Functions: hypo glos - hype glos : 2 Overlaps: 1 x "the" 1 x "of" Synset 1: mortise#v#1 Synset 2: Cystophora_cristata#n#1 Functions: glos - hype glos : 1 Overlaps: 1 x "a" Functions: hype glos - glos : 1 Overlaps: 1 x "with" Functions: hype glos - hype glos : 1 Overlaps: 1 x "with" Synset 1: polychromize#v#1 Synset 2: stress#v#2 Functions: glos - glos : 1 Overlaps: 1 x "with" Synset 1: professional_football#n#1 Synset 2: scat#n#1 Functions: glos - glos : 1 Overlaps: 1 x "for" Functions: hype glos - glos : 6 Overlaps: 2 x "the" 1 x "to" 1 x "a" 1 x "for" 1 x "of" Functions: hype glos - hype glos : 5 Overlaps: 1 x "the" 1 x "a" 1 x "in" 2 x "of" WordNet-Similarity-2.05/t/keys/0.13/lchpairs.key0000644000175000017500000000150111015336624017402 0ustar sidsid0.9445 0.4925 1.0186 0.8755 0.6391 0.8755 1.7918 0.8755 1.2809 0.6931 0.6391 0.5878 0.8755 0.6391 0.8755 0.8109 0.8109 1.0986 0.6391 1.0186 0.8755 0.9445 0.9445 0.6931 0.8109 0.7503 1.0986 0.8109 0.9445 0.9445 1.1856 0.8755 0.9445 0.8109 0.8755 1.0186 1.2809 1.1856 1.1856 0.6931 0.9445 0.9445 0.7503 0.7503 0.7503 0.8755 0.8755 0.8755 0.9445 0.9445 1.3863 1.5041 1.0986 1.0986 1.0986 0.8755 0.9445 0.8109 1.1856 0.9445 1.1350 1.3863 1.0296 1.9459 1.2528 1.3863 1.7228 1.1350 1.5404 0.6242 1.5404 1.2528 1.3863 1.1350 1.7228 1.2528 1.1350 1.0296 1.1350 1.7228 1.1350 1.3863 0.7673 1.2528 1.5404 -1000000.0000 0.6391 0.8755 0.7503 -1000000.0000 0.7503 -1000000.0000 0.8109 1.3863 -1000000.0000 0.7503 1.2528 -1000000.0000 1.2809 0.8755 1.2809 -1000000.0000 1.0186 -1000000.0000 0.8755 1.0986 1.0186 -1000000.0000 -1000000.0000 0.9445 WordNet-Similarity-2.05/t/keys/0.13/pathpairs.key0000644000175000017500000000150111015336624017570 0ustar sidsid0.0714 0.0455 0.0769 0.0667 0.0526 0.0667 0.1667 0.0667 0.1000 0.0556 0.0526 0.0500 0.0667 0.0526 0.0667 0.0625 0.0625 0.0833 0.0526 0.0769 0.0667 0.0714 0.0714 0.0556 0.0625 0.0588 0.0833 0.0625 0.0714 0.0714 0.0909 0.0667 0.0714 0.0625 0.0667 0.0769 0.1000 0.0909 0.0909 0.0556 0.0714 0.0714 0.0588 0.0588 0.0588 0.0667 0.0667 0.0667 0.0714 0.0714 0.1111 0.1250 0.0833 0.0833 0.0833 0.0667 0.0714 0.0625 0.0909 0.0714 0.1111 0.1429 0.1000 0.2500 0.1250 0.1429 0.2000 0.1111 0.1667 0.0667 0.1667 0.1250 0.1429 0.1111 0.2000 0.1250 0.1111 0.1000 0.1111 0.2000 0.1111 0.1429 0.0769 0.1250 0.1667 -1000000.0000 0.0526 0.0667 0.0588 -1000000.0000 0.0588 -1000000.0000 0.0625 0.1111 -1000000.0000 0.0588 0.1250 -1000000.0000 0.1000 0.0667 0.1000 -1000000.0000 0.0769 -1000000.0000 0.0667 0.0833 0.0769 -1000000.0000 -1000000.0000 0.0714 WordNet-Similarity-2.05/t/keys/0.13/hsopairs_nr.key0000644000175000017500000000033411015336624020127 0ustar sidsid0 0 0 0 0 0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 WordNet-Similarity-2.05/t/keys/0.13/leskpairs.key0000644000175000017500000000144011015336624017574 0ustar sidsid6.0000 5.0000 3.0000 37.0000 7.0000 5.0000 38.0000 6.0000 7.0000 47.0000 13.0000 14.0000 3.0000 2.0000 3.0000 2.0000 4.0000 7.0000 5.0000 5.0000 4.0000 9.0000 0.0000 4.0000 3.0000 7.0000 5.0000 25.0000 14.0000 15.0000 28.0000 15.0000 11.0000 20.0000 7.0000 7.0000 2.0000 8.0000 8.0000 9.0000 6.0000 8.0000 7.0000 0.0000 2.0000 13.0000 6.0000 14.0000 2.0000 1.0000 10.0000 13.0000 3.0000 5.0000 4.0000 5.0000 8.0000 3.0000 11.0000 15.0000 5.0000 3.0000 1.0000 2.0000 8.0000 7.0000 3.0000 1.0000 6.0000 0.0000 11.0000 0.0000 2.0000 1.0000 14.0000 6.0000 1.0000 5.0000 4.0000 4.0000 1.0000 3.0000 3.0000 5.0000 7.0000 0.0000 15.0000 41.0000 13.0000 3.0000 15.0000 3.0000 5.0000 3.0000 1.0000 2.0000 9.0000 2.0000 12.0000 12.0000 16.0000 1.0000 5.0000 2.0000 25.0000 11.0000 5.0000 8.0000 7.0000 10.0000 WordNet-Similarity-2.05/t/keys/0.13/jcntrace.key0000644000175000017500000000634211015336624017376 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.13/lchtrace.key0000644000175000017500000000660111015336624017370 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (Length=5) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (Length=14) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=19) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (Length=6)HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): activity#n#1 (Length=8) activity#n#1 (Length=10) activity#n#1 (Length=11) activity#n#1 (Length=12) diversion#n#1 (Length=12) activity#n#1 (Length=12) diversion#n#1 (Length=14) activity#n#1 (Length=15) *Root*#n#1 (Length=15) *Root*#n#1 (Length=16) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=20) *Root*#n#1 (Length=20)WordNet-Similarity-2.05/t/keys/0.13/jcnpairs.key0000644000175000017500000000150111015336624017406 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0424 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0408 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0502 0.0774 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0532 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0415 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.13/respairs.key0000644000175000017500000000150111015336624017425 0ustar sidsid0.0000 0.0000 0.8555 0.0000 0.0000 0.0000 4.3095 0.0000 2.5580 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.3709 1.3709 0.0000 0.0000 0.8555 0.8555 1.3709 0.0000 1.3709 0.0000 0.8555 0.0000 0.0000 0.0000 0.8555 0.0000 0.0000 0.8555 0.0000 0.0000 2.5356 2.4549 0.0000 0.0000 2.4596 0.0000 0.0000 0.0000 0.8555 0.0000 0.8555 1.3709 1.7064 0.0000 2.1429 3.7388 0.0000 0.0000 2.5580 0.0000 0.8555 0.8555 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.13/wuptrace.key0000644000175000017500000000606511015336624017441 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumers: carnivore#n#1 (Depth=11) Depth(dog#n#1) = 13 Depth(cat#n#1) = 13 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumers: *Root*#n#1 (Depth=1) Depth(art#n#3) = 6 Depth(Hadrian#n#1) = 9 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumers: *Root*#v#1 (Depth=1) Depth(polychromize#v#1) = 4 Depth(stress#v#2) = 3 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumers: diversion#n#1 (Depth=4) Depth(professional_football#n#1) = 8 Depth(scat#n#1) = 9 WordNet-Similarity-2.05/t/keys/0.13/restrace.key0000644000175000017500000000567711015336624017427 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.13/respairs_nr.key0000644000175000017500000000251611015336624020133 0ustar sidsid-1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 4.3095 -1000000.0000 2.5580 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 1.3709 -1000000.0000 -1000000.0000 0.8555 0.8555 1.3709 -1000000.0000 1.3709 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 2.5356 2.4549 -1000000.0000 -1000000.0000 2.4596 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 0.8555 1.3709 1.7064 -1000000.0000 2.1429 3.7388 -1000000.0000 -1000000.0000 2.5580 -1000000.0000 0.8555 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.13/pathtrace.key0000644000175000017500000000633411015336624017561 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Shortest path: dog#n#1 canine#n#2 carnivore#n#1 feline#n#1 cat#n#1 Path length = 5 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Shortest path: art#n#3 superior_skill#n#1 ability#n#2 cognition#n#1 psychological_feature#n#1 *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Path length = 14 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Shortest path: polychrome#v#1 color#v#1 change#v#2 *Root*#v#1 pronounce#v#1 stress#v#2 Path length = 6HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Shortest path: professional_football#n#1 sport#n#2 occupation#n#1 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 Path length = 8WordNet-Similarity-2.05/t/keys/0.13/linpairs.key0000644000175000017500000000150111015336624017416 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 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Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.13/pathpairs_nr.key0000644000175000017500000000251611015336624020276 0ustar sidsid-1000000.0000 -1000000.0000 0.0769 -1000000.0000 -1000000.0000 -1000000.0000 0.1667 -1000000.0000 0.1000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 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WordNet-Similarity-2.05/t/keys/0.13/hsotrace.key0000644000175000017500000000434511015336624017416 0ustar sidsidHorizontal Links of dog#n#1: Upward Links of dog#n#1: canine#n#2 flag#n#7 Downward Links of dog#n#1: Canis#n#1 pack#n#6 pooch#n#1 cur#n#1 lapdog#n#1 toy_dog#n#1 hunting_dog#n#1 working_dog#n#1 dalmatian#n#2 basenji#n#1 pug#n#1 Leonberg#n#1 Newfoundland#n#1 Great_Pyrenees#n#1 spitz#n#1 griffon#n#1 corgi#n#1 poodle#n#1 Mexican_hairless#n#1 Horizontal Links of cat#n#1: Upward Links of cat#n#1: feline#n#1 Downward Links of cat#n#1: domestic_cat#n#1 wildcat#n#3 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] carnivore#n#1 [D] feline#n#1 [D] cat#n#1 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] paw#n#1 [D] feline#n#1 [D] cat#n#1 Horizontal Links of untruly#r#1: untrue#a#1 Upward Links of untruly#r#1: Downward Links of untruly#r#1: Horizontal Links of expository#a#1: instructive#a#1 Upward Links of expository#a#1: Downward Links of expository#a#1: Horizontal Links of noncyclic#a#1: cyclic#a#4 cyclicity#n#1 Upward Links of noncyclic#a#1: Downward Links of noncyclic#a#1: Horizontal Links of indecisive#a#2: inconclusive#a#1 irresolute#a#1 decisive#a#1 inconclusive#a#2 Upward Links of indecisive#a#2: Downward Links of indecisive#a#2: Horizontal Links of art#n#3: Upward Links of art#n#3: superior_skill#n#1 Downward Links of art#n#3: aviation#n#3 eristic#n#2 falconry#n#1 fortification#n#2 homiletics#n#2 horology#n#1 minstrelsy#n#3 musicianship#n#1 enology#n#1 puppetry#n#1 taxidermy#n#1 ventriloquism#n#1 Horizontal Links of Hadrian#n#1: Upward Links of Hadrian#n#1: Roman_Emperor#n#1 Downward Links of Hadrian#n#1: Horizontal Links of mortise#v#1: Upward Links of mortise#v#1: cut#v#1 Downward Links of mortise#v#1: Horizontal Links of hooded_seal#n#1: Upward Links of hooded_seal#n#1: earless_seal#n#1 Downward Links of hooded_seal#n#1: Cystophora#n#1Horizontal Links of polychrome#v#1: Upward Links of polychrome#v#1: color#v#1 Downward Links of polychrome#v#1: Horizontal Links of stress#v#2: Upward Links of stress#v#2: pronounce#v#1 Downward Links of stress#v#2: Horizontal Links of professional_football#n#1: Upward Links of professional_football#n#1: football#n#1 sport#n#2 Downward Links of professional_football#n#1: Horizontal Links of scat#n#1: Upward Links of scat#n#1: singing#n#1 jazz#n#2 Downward Links of scat#n#1: WordNet-Similarity-2.05/t/keys/linpairs_nr.key0000644000175000017500000000174111015336623017541 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 undef -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/wuppairs.key0000644000175000017500000000146211015336623017073 0ustar sidsid0.3333 0.1481 0.3810 0.3333 0.2857 0.1905 0.7059 0.2727 0.5714 0.1739 0.1905 0.1818 0.2857 0.1667 0.2000 0.3158 0.4000 0.4762 0.2609 0.3158 0.2857 0.2857 0.4000 0.1600 0.3200 0.2222 0.3333 0.1905 0.2222 0.2105 0.3750 0.2222 0.2222 0.2727 0.3529 0.2857 0.5263 0.5455 0.4286 0.1600 0.5185 0.1600 0.2222 0.1818 0.2609 0.2105 0.2857 0.4167 0.3529 0.3333 0.6000 0.6667 0.2105 0.2353 0.5455 0.2857 0.3000 0.2609 0.2667 0.2105 0.2222 0.2500 0.1818 0.4000 undef 0.2500 0.2222 0.2000 0.2857 0.1250 0.2857 0.2222 0.2500 0.2000 0.3333 0.2222 0.2000 0.1818 0.2000 0.3333 0.2000 0.2500 0.1429 0.2222 0.2857 -1000000.0000 0.2500 0.2000 0.1818 -1000000.0000 0.1818 -1000000.0000 0.1739 0.5556 -1000000.0000 0.2000 0.2222 -1000000.0000 0.2500 0.3158 0.2857 -1000000.0000 0.2105 0.2500 0.4762 0.2105 -1000000.0000 -1000000.0000 0.2727 WordNet-Similarity-2.05/t/keys/0.07/0000755000175000017500000000000011025552366015075 5ustar sidsidWordNet-Similarity-2.05/t/keys/0.07/lesktrace.key0000644000175000017500000000434411015336623017564 0ustar sidsidSynset 1: dog#n#1 Synset 2: cat#n#1 Functions: glos - hype glos : 2 Overlaps: 1 x "of" 1 x "many" Functions: glos - hypo glos : 19 Overlaps: 1 x "member of the genus" 1 x "the" 1 x "in" 1 x "domesticated" Functions: holo glos - glos : 2 Overlaps: 1 x "domestic" 1 x "and" Functions: holo glos - hype glos : 1 Overlaps: 1 x "of" Functions: holo glos - hypo glos : 8 Overlaps: 1 x "genus" 1 x "of the" 1 x "domestic" 1 x "and" 1 x "wild" Functions: hype glos - glos : 1 Overlaps: 1 x "and" Functions: hype glos - hype glos : 15 Overlaps: 1 x "claws" 1 x "fissiped mammals" 1 x "any of various" 1 x "with" Functions: hype glos - hypo glos : 3 Overlaps: 1 x "of" 1 x "any" 1 x "and" Functions: hypo glos - glos : 4 Overlaps: 1 x "to" 1 x "usually" 1 x "and" 1 x "having" Functions: hypo glos - hype glos : 11 Overlaps: 1 x "bodied" 1 x "any of various" 1 x "with" Functions: hypo glos - hypo glos : 16 Overlaps: 1 x "small" 1 x "the" 1 x "of" 2 x "any" 1 x "in the" 1 x "or" 1 x "resembling the" 1 x "in" 1 x "and" Functions: mero glos - hypo glos : 1 Overlaps: 1 x "or" Synset 1: untruly#r#1 Synset 2: expositive#a#1 Synset 1: noncyclic#a#1 Synset 2: indecisive#a#2 Functions: glos - also glos : 1 Overlaps: 1 x "not" Functions: glos - glos : 1 Overlaps: 1 x "not" Synset 1: art#n#3 Synset 2: Hadrian#n#1 Functions: example - glos : 3 Overlaps: 1 x "the" 1 x "of" 1 x "s" Functions: glos - glos : 3 Overlaps: 1 x "a" 2 x "and" Functions: hypo glos - glos : 13 Overlaps: 3 x "the" 2 x "to" 2 x "a" 1 x "in" 2 x "of" 2 x "and" 1 x "s" Functions: hypo glos - hype glos : 2 Overlaps: 1 x "the" 1 x "of" Synset 1: mortise#v#1 Synset 2: Cystophora_cristata#n#1 Functions: glos - hype glos : 2 Overlaps: 2 x "a" Functions: hype glos - glos : 1 Overlaps: 1 x "with" Functions: hype glos - hype glos : 1 Overlaps: 1 x "with" Synset 1: polychromize#v#1 Synset 2: stress#v#2 Functions: glos - glos : 1 Overlaps: 1 x "with" Synset 1: professional_football#n#1 Synset 2: scat#n#1 Functions: glos - glos : 1 Overlaps: 1 x "for" Functions: hype glos - glos : 6 Overlaps: 2 x "the" 1 x "to" 1 x "a" 1 x "of" 1 x "for" Functions: hype glos - hype glos : 5 Overlaps: 1 x "the" 1 x "a" 1 x "in" 2 x "of" WordNet-Similarity-2.05/t/keys/0.07/lchpairs.key0000644000175000017500000000150111015336623017404 0ustar sidsid0.9445 0.4925 1.0186 0.8755 0.6391 0.8755 1.7918 0.8755 1.2809 0.6931 0.6391 0.5878 0.8755 0.6391 0.8755 0.8109 0.8109 1.0986 0.6391 1.0186 0.8755 0.9445 0.9445 0.6931 0.8109 0.7503 1.0986 0.8109 0.9445 0.9445 1.1856 0.8755 0.9445 0.8109 0.8755 1.0186 1.2809 1.1856 1.1856 0.6931 0.9445 0.9445 0.7503 0.7503 0.7503 0.8755 0.8755 0.8755 0.9445 0.9445 1.3863 1.5041 1.0986 1.0986 1.0986 0.8755 0.9445 0.8109 1.1856 0.9445 1.1350 1.3863 1.0296 1.9459 1.2528 1.3863 1.7228 1.1350 1.5404 0.6242 1.5404 1.2528 1.3863 1.1350 1.7228 1.2528 1.1350 1.0296 1.1350 1.7228 1.1350 1.3863 0.7673 1.2528 1.5404 -1000000.0000 0.6391 0.8755 0.7503 -1000000.0000 0.7503 -1000000.0000 0.8109 1.3863 -1000000.0000 0.7503 1.2528 -1000000.0000 1.2809 0.8755 1.2809 -1000000.0000 1.0186 -1000000.0000 0.8755 1.0986 1.0186 -1000000.0000 -1000000.0000 0.9445 WordNet-Similarity-2.05/t/keys/0.07/pathpairs.key0000644000175000017500000000150111015336623017572 0ustar sidsid0.0714 0.0455 0.0769 0.0667 0.0526 0.0667 0.1667 0.0667 0.1000 0.0556 0.0526 0.0500 0.0667 0.0526 0.0667 0.0625 0.0625 0.0833 0.0526 0.0769 0.0667 0.0714 0.0714 0.0556 0.0625 0.0588 0.0833 0.0625 0.0714 0.0714 0.0909 0.0667 0.0714 0.0625 0.0667 0.0769 0.1000 0.0909 0.0909 0.0556 0.0714 0.0714 0.0588 0.0588 0.0588 0.0667 0.0667 0.0667 0.0714 0.0714 0.1111 0.1250 0.0833 0.0833 0.0833 0.0667 0.0714 0.0625 0.0909 0.0714 0.1111 0.1429 0.1000 0.2500 0.1250 0.1429 0.2000 0.1111 0.1667 0.0667 0.1667 0.1250 0.1429 0.1111 0.2000 0.1250 0.1111 0.1000 0.1111 0.2000 0.1111 0.1429 0.0769 0.1250 0.1667 -1000000.0000 0.0526 0.0667 0.0588 -1000000.0000 0.0588 -1000000.0000 0.0625 0.1111 -1000000.0000 0.0588 0.1250 -1000000.0000 0.1000 0.0667 0.1000 -1000000.0000 0.0769 -1000000.0000 0.0667 0.0833 0.0769 -1000000.0000 -1000000.0000 0.0714 WordNet-Similarity-2.05/t/keys/0.07/hsopairs_nr.key0000644000175000017500000000033411015336623020131 0ustar sidsid0 0 0 0 0 0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 WordNet-Similarity-2.05/t/keys/0.07/leskpairs.key0000644000175000017500000000144511015336623017603 0ustar sidsid6.0000 5.0000 3.0000 39.0000 7.0000 5.0000 38.0000 6.0000 7.0000 50.0000 18.0000 14.0000 3.0000 2.0000 7.0000 4.0000 4.0000 11.0000 5.0000 5.0000 4.0000 10.0000 0.0000 4.0000 3.0000 8.0000 5.0000 27.0000 14.0000 15.0000 50.0000 17.0000 13.0000 25.0000 8.0000 7.0000 2.0000 9.0000 12.0000 12.0000 6.0000 8.0000 8.0000 0.0000 4.0000 14.0000 6.0000 15.0000 2.0000 1.0000 11.0000 17.0000 5.0000 5.0000 4.0000 5.0000 9.0000 3.0000 14.0000 15.0000 5.0000 3.0000 1.0000 2.0000 8.0000 7.0000 3.0000 2.0000 8.0000 0.0000 11.0000 0.0000 2.0000 1.0000 16.0000 6.0000 1.0000 5.0000 4.0000 5.0000 1.0000 4.0000 3.0000 5.0000 8.0000 0.0000 16.0000 41.0000 13.0000 3.0000 15.0000 3.0000 6.0000 3.0000 1.0000 2.0000 10.0000 2.0000 12.0000 12.0000 16.0000 1.0000 5.0000 3.0000 25.0000 11.0000 5.0000 8.0000 7.0000 12.0000 WordNet-Similarity-2.05/t/keys/0.07/jcntrace.key0000644000175000017500000000634211015336623017400 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.07/lchtrace.key0000644000175000017500000000660111015336623017372 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (Length=5) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (Length=14) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=19) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (Length=6)HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): activity#n#1 (Length=8) activity#n#1 (Length=10) activity#n#1 (Length=11) activity#n#1 (Length=12) diversion#n#1 (Length=12) activity#n#1 (Length=12) diversion#n#1 (Length=14) activity#n#1 (Length=15) *Root*#n#1 (Length=15) *Root*#n#1 (Length=16) *Root*#n#1 (Length=16) *Root*#n#1 (Length=17) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=18) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=19) *Root*#n#1 (Length=20) *Root*#n#1 (Length=20)WordNet-Similarity-2.05/t/keys/0.07/jcnpairs.key0000644000175000017500000000150111015336623017410 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0424 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0408 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0502 0.0774 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0532 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0415 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.07/respairs.key0000644000175000017500000000150111015336623017427 0ustar sidsid0.0000 0.0000 0.8555 0.0000 0.0000 0.0000 4.3095 0.0000 2.5580 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.3709 1.3709 0.0000 0.0000 0.8555 0.8555 1.3709 0.0000 1.3709 0.0000 0.8555 0.0000 0.0000 0.0000 0.8555 0.0000 0.0000 0.8555 0.0000 0.0000 2.5356 2.4549 0.0000 0.0000 2.4596 0.0000 0.0000 0.0000 0.8555 0.0000 0.8555 1.3709 1.7064 0.0000 2.1429 3.7388 0.0000 0.0000 2.5580 0.0000 0.8555 0.8555 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 1.3709 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.07/wuptrace.key0000644000175000017500000000606511015336623017443 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumers: carnivore#n#1 (Depth=11) Depth(dog#n#1) = 13 Depth(cat#n#1) = 13 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumers: *Root*#n#1 (Depth=1) Depth(art#n#3) = 6 Depth(Hadrian#n#1) = 9 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumers: *Root*#v#1 (Depth=1) Depth(polychromize#v#1) = 4 Depth(stress#v#2) = 3 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumers: diversion#n#1 (Depth=4) Depth(professional_football#n#1) = 6 Depth(scat#n#1) = 7 WordNet-Similarity-2.05/t/keys/0.07/restrace.key0000644000175000017500000000567711015336623017431 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.07/respairs_nr.key0000644000175000017500000000251611015336623020135 0ustar sidsid-1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 4.3095 -1000000.0000 2.5580 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 1.3709 -1000000.0000 -1000000.0000 0.8555 0.8555 1.3709 -1000000.0000 1.3709 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 -1000000.0000 2.5356 2.4549 -1000000.0000 -1000000.0000 2.4596 -1000000.0000 -1000000.0000 -1000000.0000 0.8555 -1000000.0000 0.8555 1.3709 1.7064 -1000000.0000 2.1429 3.7388 -1000000.0000 -1000000.0000 2.5580 -1000000.0000 0.8555 0.8555 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 1.3709 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.07/pathtrace.key0000644000175000017500000000633411015336623017563 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Shortest path: dog#n#1 canine#n#2 carnivore#n#1 feline#n#1 cat#n#1 Path length = 5 untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Shortest path: art#n#3 superior_skill#n#1 ability#n#2 cognition#n#1 psychological_feature#n#1 *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Path length = 14 mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Shortest path: polychrome#v#1 color#v#1 change#v#2 *Root*#v#1 pronounce#v#1 stress#v#2 Path length = 6HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Shortest path: professional_football#n#1 sport#n#2 occupation#n#1 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 Path length = 8WordNet-Similarity-2.05/t/keys/0.07/linpairs.key0000644000175000017500000000150111015336623017420 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.07/wndepths.txt0000644000175000017500000001631011015336623017466 0ustar sidsidn 00000000 18 n 00001740 17 n 00020333 13 n 00020486 14 n 00024568 12 n 00025950 9 n 00026194 11 n 00026769 11 n 00027371 13 n 00029881 11 v 00000000 14 v 00001740 4 v 00010141 4 v 00014236 2 v 00015400 1 v 00016389 1 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0.5000 0.1667 0.1053 0.4762 0.1333 0.1111 0.1111 0.2000 0.1250 0.2222 0.3333 0.2353 0.1333 0.3333 0.5882 0.1538 0.1538 0.4211 0.1250 0.2353 0.2105 0.1667 0.1333 0.2000 0.2500 0.1818 0.4000 0.2222 0.2500 0.3333 0.2000 0.2857 0.1250 0.2857 0.2222 0.2500 0.2000 0.3333 0.2222 0.2000 0.1818 0.2000 0.3333 0.2000 0.2500 0.1429 0.2222 0.2857 -1000000.0000 0.1000 0.1250 0.1111 -1000000.0000 0.1111 -1000000.0000 0.1176 0.4615 -1000000.0000 0.1111 0.2222 -1000000.0000 0.1818 0.1250 0.1818 -1000000.0000 0.1429 -1000000.0000 0.1250 0.3529 0.1429 -1000000.0000 -1000000.0000 0.1333 WordNet-Similarity-2.05/t/keys/0.07/hsopairs.key0000644000175000017500000000140211015336623017427 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 3.0000 0.0000 0.0000 3.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.07/lintrace.key0000644000175000017500000000634211015336623017410 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=8.097971) Concept1: dog#n#1 (IC=8.608796) Concept2: cat#n#1 (IC=9.419726) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root*#n#1 (IC=0.000000) Concept1: art#n#3 (IC=10.032831) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.328289) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.921957) activity#n#1 (IC=2.965445) *Root*#n#1 (IC=0.000000) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/0.07/pathpairs_nr.key0000644000175000017500000000251611015336623020300 0ustar sidsid-1000000.0000 -1000000.0000 0.0769 -1000000.0000 -1000000.0000 -1000000.0000 0.1667 -1000000.0000 0.1000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 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-1000000.0000 -1000000.0000 -1000000.0000 WordNet-Similarity-2.05/t/keys/0.07/lchpairs_nr.key0000644000175000017500000000251611015336623020112 0ustar sidsid-1000000.0000 -1000000.0000 0.9614 -1000000.0000 -1000000.0000 -1000000.0000 1.7346 -1000000.0000 1.2238 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.7538 1.0415 -1000000.0000 -1000000.0000 0.8183 0.8873 0.8873 -1000000.0000 0.7538 -1000000.0000 1.0415 -1000000.0000 -1000000.0000 -1000000.0000 1.1285 -1000000.0000 -1000000.0000 0.7538 -1000000.0000 -1000000.0000 1.2238 1.1285 -1000000.0000 -1000000.0000 0.8873 -1000000.0000 -1000000.0000 -1000000.0000 0.6931 -1000000.0000 0.8183 0.8183 0.6931 -1000000.0000 0.8938 1.4469 -1000000.0000 -1000000.0000 1.0415 -1000000.0000 0.8873 0.7538 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 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WordNet-Similarity-2.05/t/keys/0.07/hsotrace.key0000644000175000017500000000434511015336623017420 0ustar sidsidHorizontal Links of dog#n#1: Upward Links of dog#n#1: canine#n#2 flag#n#7 Downward Links of dog#n#1: Canis#n#1 pack#n#6 pooch#n#1 cur#n#1 lapdog#n#1 toy_dog#n#1 hunting_dog#n#1 working_dog#n#1 dalmatian#n#2 basenji#n#1 pug#n#1 Leonberg#n#1 Newfoundland#n#1 Great_Pyrenees#n#1 spitz#n#1 griffon#n#1 corgi#n#1 poodle#n#1 Mexican_hairless#n#1 Horizontal Links of cat#n#1: Upward Links of cat#n#1: feline#n#1 Downward Links of cat#n#1: domestic_cat#n#1 wildcat#n#3 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] carnivore#n#1 [D] feline#n#1 [D] cat#n#1 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] paw#n#1 [D] feline#n#1 [D] cat#n#1 Horizontal Links of untruly#r#1: untrue#a#1 Upward Links of untruly#r#1: Downward Links of untruly#r#1: Horizontal Links of expository#a#1: instructive#a#1 Upward Links of expository#a#1: Downward Links of expository#a#1: Horizontal Links of noncyclic#a#1: cyclic#a#4 cyclicity#n#1 Upward Links of noncyclic#a#1: Downward Links of noncyclic#a#1: Horizontal Links of indecisive#a#2: inconclusive#a#1 irresolute#a#1 decisive#a#1 inconclusive#a#2 Upward Links of indecisive#a#2: Downward Links of indecisive#a#2: Horizontal Links of art#n#3: Upward Links of art#n#3: superior_skill#n#1 Downward Links of art#n#3: aviation#n#3 eristic#n#2 falconry#n#1 fortification#n#2 homiletics#n#2 horology#n#1 minstrelsy#n#3 musicianship#n#1 enology#n#1 puppetry#n#1 taxidermy#n#1 ventriloquism#n#1 Horizontal Links of Hadrian#n#1: Upward Links of Hadrian#n#1: Roman_Emperor#n#1 Downward Links of Hadrian#n#1: Horizontal Links of mortise#v#1: Upward Links of mortise#v#1: cut#v#1 Downward Links of mortise#v#1: Horizontal Links of hooded_seal#n#1: Upward Links of hooded_seal#n#1: earless_seal#n#1 Downward Links of hooded_seal#n#1: Cystophora#n#1Horizontal Links of polychrome#v#1: Upward Links of polychrome#v#1: color#v#1 Downward Links of polychrome#v#1: Horizontal Links of stress#v#2: Upward Links of stress#v#2: pronounce#v#1 Downward Links of stress#v#2: Horizontal Links of professional_football#n#1: Upward Links of professional_football#n#1: football#n#1 sport#n#2 Downward Links of professional_football#n#1: Horizontal Links of scat#n#1: Upward Links of scat#n#1: singing#n#1 jazz#n#2 Downward Links of scat#n#1: WordNet-Similarity-2.05/t/keys/hsopairs.key0000644000175000017500000000137211015336623017051 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 3.0000 0.0000 0.0000 3.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 undef 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 WordNet-Similarity-2.05/t/keys/diff/0000755000175000017500000000000011025552366015421 5ustar sidsidWordNet-Similarity-2.05/t/keys/diff/pathpairs.diff0000644000175000017500000000047711015336624020252 0ustar sidsid86c86 < -1000000.0000 --- > 0.0000 90c90 < -1000000.0000 --- > 0.0000 92c92 < -1000000.0000 --- > 0.0000 95c95 < -1000000.0000 --- > 0.0000 98c98 < -1000000.0000 --- > 0.0000 102c102 < -1000000.0000 --- > 0.0000 104c104 < -1000000.0000 --- > 0.0000 108,109c108,109 < -1000000.0000 < -1000000.0000 --- > 0.0000 > 0.0000 WordNet-Similarity-2.05/t/keys/diff/respairs.diff0000644000175000017500000000351211015336624020100 0ustar sidsid1,2c1,2 < 0.0000 < 0.0000 --- > -0.0000 > -0.0000 4,6c4,6 < 0.0000 < 0.0000 < 0.0000 --- > -0.0000 > -0.0000 > -0.0000 8c8 < 0.0000 --- > -0.0000 10,16c10,16 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 --- > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 19,20c19,20 < 0.0000 < 0.0000 --- > -0.0000 > -0.0000 24c24 < 0.0000 --- > -0.0000 26c26 < 0.0000 --- > -0.0000 28,30c28,30 < 0.0000 < 0.0000 < 0.0000 --- > -0.0000 > -0.0000 > -0.0000 32,33c32,33 < 0.0000 < 0.0000 --- > -0.0000 > -0.0000 35,36c35,36 < 0.0000 < 0.0000 --- > -0.0000 > -0.0000 39,40c39,40 < 0.0000 < 0.0000 --- > -0.0000 > -0.0000 42,44c42,44 < 0.0000 < 0.0000 < 0.0000 --- > -0.0000 > -0.0000 > -0.0000 46c46 < 0.0000 --- > -0.0000 50c50 < 0.0000 --- > -0.0000 53,54c53,54 < 0.0000 < 0.0000 --- > -0.0000 > -0.0000 56c56 < 0.0000 --- > -0.0000 58a59,101 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > -0.0000 > 0.0000 > -0.0000 > -0.0000 > -0.0000 > 0.0000 > -0.0000 > 0.0000 > -0.0000 > 1.3709 > 0.0000 > -0.0000 > -0.0000 > 0.0000 > -0.0000 > -0.0000 > -0.0000 59a103 > -0.0000 61,105c105 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < 0.0000 < -1000000.0000 < 0.0000 < 0.0000 < 0.0000 < -1000000.0000 < 0.0000 < -1000000.0000 < 0.0000 < 1.3709 < -1000000.0000 < 0.0000 < 0.0000 < -1000000.0000 < 0.0000 < 0.0000 < 0.0000 < -1000000.0000 < 0.0000 < -1000000.0000 < 0.0000 --- > -0.0000 106a107 > -0.0000 108,109d108 < -1000000.0000 < -1000000.0000 110a110 > -0.0000 WordNet-Similarity-2.05/t/keys/diff/wuppairs.diff0000644000175000017500000000367211015336624020131 0ustar sidsid1,19c1,2 < 0.1333 < 0.0870 < 0.2500 < 0.1250 < 0.1000 < 0.1250 < 0.6667 < 0.1250 < 0.4706 < 0.1053 < 0.1000 < 0.0952 < 0.1250 < 0.1000 < 0.1250 < 0.1176 < 0.2857 < 0.4000 < 0.1000 --- > 0.0000 > 0.0000 21,33c4,16 < 0.2222 < 0.2353 < 0.3529 < 0.1053 < 0.2857 < 0.1111 < 0.2667 < 0.1176 < 0.1333 < 0.1333 < 0.2857 < 0.1250 < 0.1333 --- > 0.0000 > 0.0000 > 0.0000 > 0.6154 > 0.0000 > 0.4000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 34a18,20 > 0.1538 > 0.0000 > 0.0000 36,41d21 < 0.1429 < 0.4706 < 0.5000 < 0.1667 < 0.1053 < 0.4762 43,49d22 < 0.1111 < 0.1111 < 0.2000 < 0.1250 < 0.2222 < 0.3333 < 0.2353 51,57c24 < 0.3333 < 0.5882 < 0.1538 < 0.1538 < 0.4211 < 0.1250 < 0.2353 --- > 0.0000 58a26,30 > 0.0000 > 0.1538 > 0.0000 > 0.0000 > 0.0000 60,92c32,33 < 0.1333 < 0.2000 < 0.2500 < 0.1818 < 0.4000 < 0.2222 < 0.2500 < 0.3333 < 0.2000 < 0.2857 < 0.1250 < 0.2857 < 0.2222 < 0.2500 < 0.2000 < 0.3333 < 0.2222 < 0.2000 < 0.1818 < 0.2000 < 0.3333 < 0.2000 < 0.2500 < 0.1429 < 0.2222 < 0.2857 < -1000000.0000 < 0.1000 < 0.1250 < 0.1111 < -1000000.0000 < 0.1111 < -1000000.0000 --- > 0.0000 > 0.0000 94,95c35,44 < 0.4615 < -1000000.0000 --- > 0.0000 > 0.0000 > 0.4000 > 0.4286 > 0.0000 > 0.0000 > 0.4211 > 0.0000 > 0.0000 > 0.0000 97,99c46 < 0.2222 < -1000000.0000 < 0.1818 --- > 0.0000 101,104d47 < 0.1818 < -1000000.0000 < 0.1429 < -1000000.0000 105a49,54 > 0.1333 > 0.0000 > 0.2000 > 0.5333 > 0.0000 > 0.0000 107,109c56 < 0.1429 < -1000000.0000 < -1000000.0000 --- > 0.0000 110a58,110 > 0.1176 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.3636 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.2667 > 0.0000 > 0.0000 > 0.0000 > 0.0000 WordNet-Similarity-2.05/t/keys/diff/leskpairs.diff0000644000175000017500000000000011015336624020232 0ustar sidsidWordNet-Similarity-2.05/t/keys/diff/linpairs.diff0000644000175000017500000000044111015336624020067 0ustar sidsid86d85 < -1000000.0000 90d88 < -1000000.0000 92d89 < -1000000.0000 95d91 < -1000000.0000 98d93 < -1000000.0000 102d96 < -1000000.0000 104d97 < -1000000.0000 108,109c101,109 < -1000000.0000 < -1000000.0000 --- > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 WordNet-Similarity-2.05/t/keys/diff/jcnpairs.diff0000644000175000017500000000045211015336624020061 0ustar sidsid86d85 < -1000000.0000 90d88 < -1000000.0000 92d89 < -1000000.0000 95d91 < -1000000.0000 98c94,98 < -1000000.0000 --- > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 102d101 < -1000000.0000 104d102 < -1000000.0000 108,109c106,109 < -1000000.0000 < -1000000.0000 --- > 0.0000 > 0.0000 > 0.0000 > 0.0000 WordNet-Similarity-2.05/t/keys/diff/wuppairs_nr.diff0000644000175000017500000000502111015336624020616 0ustar sidsid1,17c1,8 < -1000000.0000 < -1000000.0000 < 0.1429 < -1000000.0000 < -1000000.0000 < -1000000.0000 < 0.6154 < -1000000.0000 < 0.4000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < 0.2105 --- > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.5455 > 0.0000 19,33c10,16 < -1000000.0000 < -1000000.0000 < 0.1250 < 0.1333 < 0.2667 < -1000000.0000 < 0.2105 < -1000000.0000 < 0.1538 < -1000000.0000 < -1000000.0000 < -1000000.0000 < 0.1667 < -1000000.0000 < -1000000.0000 --- > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 35,57c18,24 < -1000000.0000 < -1000000.0000 < 0.4000 < 0.4286 < -1000000.0000 < -1000000.0000 < 0.4211 < -1000000.0000 < -1000000.0000 < -1000000.0000 < 0.1111 < -1000000.0000 < 0.1250 < 0.2500 < 0.1333 < -1000000.0000 < 0.2000 < 0.5333 < -1000000.0000 < -1000000.0000 < 0.3529 < -1000000.0000 < 0.1333 --- > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 59,105c26,54 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < 0.3636 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 --- > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.3077 > 0.3333 > 0.0000 > 0.0000 > 0.3529 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.4615 > 0.0000 > 0.0000 107,110c56,110 < -1000000.0000 < -1000000.0000 < -1000000.0000 < -1000000.0000 --- > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.2222 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.0000 > 0.1538 > 0.0000 > 0.0000 > 0.0000 > 0.0000 WordNet-Similarity-2.05/t/keys/diff/lchpairs.diff0000644000175000017500000000401311015336624020052 0ustar sidsid1,110c1,110 < 0.9445 < 0.4925 < 1.0186 < 0.8755 < 0.6391 < 0.8755 < 1.7918 < 0.8755 < 1.2809 < 0.6931 < 0.6391 < 0.5878 < 0.8755 < 0.6391 < 0.8755 < 0.8109 < 0.8109 < 1.0986 < 0.6391 < 1.0186 < 0.8755 < 0.9445 < 0.9445 < 0.6931 < 0.8109 < 0.7503 < 1.0986 < 0.8109 < 0.9445 < 0.9445 < 1.1856 < 0.8755 < 0.9445 < 0.8109 < 0.8755 < 1.0186 < 1.2809 < 1.1856 < 1.1856 < 0.6931 < 0.9445 < 0.9445 < 0.7503 < 0.7503 < 0.7503 < 0.8755 < 0.8755 < 0.8755 < 0.9445 < 0.9445 < 1.3863 < 1.5041 < 1.0986 < 1.0986 < 1.0986 < 0.8755 < 0.9445 < 0.8109 < 1.1856 < 0.9445 < 1.1350 < 1.3863 < 1.0296 < 1.9459 < 1.2528 < 1.3863 < 1.7228 < 1.1350 < 1.5404 < 0.6242 < 1.5404 < 1.2528 < 1.3863 < 1.1350 < 1.7228 < 1.2528 < 1.1350 < 1.0296 < 1.1350 < 1.7228 < 1.1350 < 1.3863 < 0.7673 < 1.2528 < 1.5404 < -1000000.0000 < 0.6391 < 0.8755 < 0.7503 < -1000000.0000 < 0.7503 < -1000000.0000 < 0.8109 < 1.3863 < -1000000.0000 < 0.7503 < 1.2528 < -1000000.0000 < 1.2809 < 0.8755 < 1.2809 < -1000000.0000 < 1.0186 < -1000000.0000 < 0.8755 < 1.0986 < 1.0186 < -1000000.0000 < -1000000.0000 < 0.9445 --- > 0.8267 > 0.3747 > 0.9008 > 0.7577 > 0.5213 > 0.7577 > 1.6740 > 0.7577 > 1.1632 > 0.5754 > 0.5213 > 0.4700 > 0.7577 > 0.5213 > 0.7577 > 0.6931 > 0.6931 > 0.9808 > 0.5213 > 0.9008 > 0.7577 > 0.8267 > 0.8267 > 0.5754 > 0.6931 > 0.6325 > 0.9808 > 0.6931 > 0.8267 > 0.8267 > 1.0678 > 0.7577 > 0.8267 > 0.6931 > 0.7577 > 0.9008 > 1.1632 > 1.0678 > 1.0678 > 0.5754 > 0.8267 > 0.8267 > 0.6325 > 0.6325 > 0.6325 > 0.7577 > 0.7577 > 0.7577 > 0.8267 > 0.8267 > 1.2685 > 1.3863 > 0.9808 > 0.9808 > 0.9808 > 0.7577 > 0.8267 > 0.6931 > 1.0678 > 0.8267 > 1.2685 > 1.5198 > 1.1632 > 2.0794 > 1.3863 > 1.5198 > 1.8563 > 1.2685 > 1.6740 > 0.7577 > 1.6740 > 1.3863 > 1.5198 > 1.2685 > 1.8563 > 1.3863 > 1.2685 > 1.1632 > 1.2685 > 1.8563 > 1.2685 > 1.5198 > 0.9008 > 1.3863 > 1.6740 > 0.0000 > 0.5213 > 0.7577 > 0.6325 > 0.0000 > 0.6325 > 0.0000 > 0.6931 > 1.2685 > 0.0000 > 0.6325 > 1.3863 > 0.0000 > 1.1632 > 0.7577 > 1.1632 > 0.0000 > 0.9008 > 0.0000 > 0.7577 > 0.9808 > 0.9008 > 0.0000 > 0.0000 > 0.8267 WordNet-Similarity-2.05/t/keys/diff/hsopairs.diff0000644000175000017500000000000011015336624020065 0ustar sidsidWordNet-Similarity-2.05/t/keys/lintrace.key0000644000175000017500000000773211015336623017030 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 domestic_animal#n#1 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (IC=7.254900) animal#n#1 (IC=4.741111) Concept1: dog#n#1 (IC=7.740408) Concept2: cat#n#1 (IC=8.630266) untruly#r#1 and expository#a#1 belong to different parts of speech.Only verbs and nouns have hypernym trees (noncyclic#a, indecisive#a). HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 person#n#1 ruler#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 ruler#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 object#n#1 whole#n#2 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 physical_entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): entity#n#1 (IC=0.000000) Concept1: art#n#3 (IC=9.200810) Concept2: Hadrian#n#1 (IC=0.000000) mortise#v#1 and hooded_seal#n#1 belong to different parts of speech.HyperTree: *Root*#v#1 change#v#1 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root*#v#1 (IC=0.000000) Concept1: polychrome#v#1 (IC=0.000000) Concept2: stress#v#2 (IC=10.085132) HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 game#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#2 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 event#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#2 performance#n#2 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 entity#n#1 abstraction#n#6 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): diversion#n#1 (IC=5.885227) activity#n#1 (IC=3.382555) psychological_feature#n#1 (IC=1.779817) abstraction#n#6 (IC=0.779417) Concept1: professional_football#n#1 (IC=0.000000) Concept2: scat#n#1 (IC=0.000000) WordNet-Similarity-2.05/t/keys/pathpairs_nr.key0000644000175000017500000000174111015336623017713 0ustar sidsid0.0769 0.0417 0.0714 0.0769 0.0625 0.0556 0.1667 0.0588 0.1000 0.0500 0.0556 0.0526 0.0625 0.0476 0.0588 0.0714 0.0625 0.0833 0.0556 0.0714 0.0625 0.0625 0.0714 0.0455 0.0556 0.0667 0.0769 0.0556 0.0667 0.0625 0.0909 0.0667 0.0667 0.0588 0.0833 0.0625 0.1000 0.0909 0.1111 0.0455 0.0714 0.0455 0.0667 0.0526 0.0556 0.0625 0.0625 0.0667 0.0833 0.0769 0.1111 0.1250 0.0625 0.0714 0.0909 0.0625 0.0667 0.0556 0.0833 0.0625 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 undef -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0526 0.0588 0.0526 -1000000.0000 0.0526 -1000000.0000 0.0500 0.1111 -1000000.0000 0.0588 -1000000.0000 -1000000.0000 0.0769 0.0714 0.0909 -1000000.0000 0.0625 0.0769 0.0833 0.0625 -1000000.0000 -1000000.0000 0.0588 WordNet-Similarity-2.05/t/keys/leskpairs_nr.key0000644000175000017500000000037711015336623017721 0ustar sidsid6 5 3 39 7 5 38 6 7 50 18 14 3 2 7 4 4 11 5 5 4 10 0 4 3 8 5 27 14 15 50 17 13 25 8 7 2 9 12 12 6 8 8 0 4 14 6 15 2 1 11 17 5 5 4 5 9 3 14 15 5 3 1 2 8 7 3 2 8 0 11 0 2 1 16 6 1 5 4 5 1 4 3 5 8 0 16 41 13 3 15 3 6 3 1 2 10 2 12 12 16 1 5 3 25 11 5 8 7 12 WordNet-Similarity-2.05/t/keys/wuppairs_nr.key0000644000175000017500000000174111015336623017572 0ustar sidsid0.2500 0.0800 0.3158 0.2500 0.2105 0.1053 0.6667 0.2000 0.5263 0.0952 0.1053 0.1000 0.2105 0.0909 0.1111 0.2353 0.3478 0.4211 0.1905 0.2353 0.2105 0.2105 0.3478 0.0870 0.2609 0.1250 0.2500 0.1053 0.1250 0.1176 0.2857 0.1250 0.1250 0.2000 0.2667 0.2105 0.4706 0.5000 0.3333 0.0870 0.4800 0.0870 0.1250 0.1000 0.1905 0.1176 0.2105 0.3636 0.2667 0.2500 0.5556 0.6316 0.1176 0.1333 0.5000 0.2105 0.2222 0.1905 0.1538 0.1176 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 undef -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.1818 0.1111 0.1000 -1000000.0000 0.1000 -1000000.0000 0.0952 0.5000 -1000000.0000 0.1111 -1000000.0000 -1000000.0000 0.1429 0.2353 0.1667 -1000000.0000 0.1176 0.1429 0.4211 0.1176 -1000000.0000 -1000000.0000 0.2000 WordNet-Similarity-2.05/t/keys/lchpairs_nr.key0000644000175000017500000000174111015336623017525 0ustar sidsid1.0726 0.4595 0.9985 1.0726 0.8650 0.7472 1.8458 0.8044 1.3350 0.6419 0.7472 0.6931 0.8650 0.5931 0.8044 0.9985 0.8650 1.1527 0.7472 0.9985 0.8650 0.8650 0.9985 0.5465 0.7472 0.9295 1.0726 0.7472 0.9295 0.8650 1.2397 0.9295 0.9295 0.8044 1.1527 0.8650 1.3350 1.2397 1.4404 0.5465 0.9985 0.5465 0.9295 0.6931 0.7472 0.8650 0.8650 0.9295 1.1527 1.0726 1.4404 1.5581 0.8650 0.9985 1.2397 0.8650 0.9295 0.7472 1.1527 0.8650 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 undef -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.6931 0.8044 0.6931 -1000000.0000 0.6931 -1000000.0000 0.6419 1.4404 -1000000.0000 0.8044 -1000000.0000 -1000000.0000 1.0726 0.9985 1.2397 -1000000.0000 0.8650 1.0726 1.1527 0.8650 -1000000.0000 -1000000.0000 0.8044 WordNet-Similarity-2.05/t/keys/jcnpairs_nr.key0000644000175000017500000000174111015336623017531 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0456 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0438 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 undef -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 -1000000.0000 0.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 0.0000 0.0000 -1000000.0000 0.0000 -1000000.0000 -1000000.0000 0.0446 0.0000 0.0000 -1000000.0000 0.0000 0.0000 0.0000 0.0000 -1000000.0000 -1000000.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.06/0000755000175000017500000000000011025552366015074 5ustar sidsidWordNet-Similarity-2.05/t/keys/0.06/lesktrace.key0000644000175000017500000000434411015336623017563 0ustar sidsidSynset 1: dog#n#1 Synset 2: cat#n#1 Functions: glos - hype glos : 2 Overlaps: 1 x "of" 1 x "many" Functions: glos - hypo glos : 19 Overlaps: 1 x "member of the genus" 1 x "the" 1 x "in" 1 x "domesticated" Functions: holo glos - glos : 2 Overlaps: 1 x "domestic" 1 x "and" Functions: holo glos - hype glos : 1 Overlaps: 1 x "of" Functions: holo glos - hypo glos : 8 Overlaps: 1 x "genus" 1 x "of the" 1 x "domestic" 1 x "and" 1 x "wild" Functions: hype glos - glos : 1 Overlaps: 1 x "and" Functions: hype glos - hype glos : 15 Overlaps: 1 x "claws" 1 x "fissiped mammals" 1 x "any of various" 1 x "with" Functions: hype glos - hypo glos : 3 Overlaps: 1 x "of" 1 x "any" 1 x "and" Functions: hypo glos - glos : 4 Overlaps: 1 x "to" 1 x "usually" 1 x "and" 1 x "having" Functions: hypo glos - hype glos : 11 Overlaps: 1 x "bodied" 1 x "any of various" 1 x "with" Functions: hypo glos - hypo glos : 16 Overlaps: 1 x "small" 1 x "the" 1 x "of" 2 x "any" 1 x "in the" 1 x "or" 1 x "resembling the" 1 x "in" 1 x "and" Functions: mero glos - hypo glos : 1 Overlaps: 1 x "or" Synset 1: untruly#r#1 Synset 2: expositive#a#1 Synset 1: noncyclic#a#1 Synset 2: indecisive#a#2 Functions: glos - also glos : 1 Overlaps: 1 x "not" Functions: glos - glos : 1 Overlaps: 1 x "not" Synset 1: art#n#3 Synset 2: Hadrian#n#1 Functions: example - glos : 3 Overlaps: 1 x "the" 1 x "of" 1 x "s" Functions: glos - glos : 3 Overlaps: 1 x "a" 2 x "and" Functions: hypo glos - glos : 13 Overlaps: 3 x "the" 2 x "to" 2 x "a" 1 x "in" 2 x "of" 2 x "and" 1 x "s" Functions: hypo glos - hype glos : 2 Overlaps: 1 x "the" 1 x "of" Synset 1: mortise#v#1 Synset 2: Cystophora_cristata#n#1 Functions: glos - hype glos : 2 Overlaps: 2 x "a" Functions: hype glos - glos : 1 Overlaps: 1 x "with" Functions: hype glos - hype glos : 1 Overlaps: 1 x "with" Synset 1: polychromize#v#1 Synset 2: stress#v#2 Functions: glos - glos : 1 Overlaps: 1 x "with" Synset 1: professional_football#n#1 Synset 2: scat#n#1 Functions: glos - glos : 1 Overlaps: 1 x "for" Functions: hype glos - glos : 6 Overlaps: 2 x "the" 1 x "to" 1 x "a" 1 x "of" 1 x "for" Functions: hype glos - hype glos : 5 Overlaps: 1 x "the" 1 x "a" 1 x "in" 2 x "of" WordNet-Similarity-2.05/t/keys/0.06/lchpairs.key0000644000175000017500000000140211015336623017403 0ustar sidsid0.8267 0.3747 0.9008 0.7577 0.5213 0.7577 1.6740 0.7577 1.1632 0.5754 0.5213 0.4700 0.7577 0.5213 0.7577 0.6931 0.6931 0.9808 0.5213 0.9008 0.7577 0.8267 0.8267 0.5754 0.6931 0.6325 0.9808 0.6931 0.8267 0.8267 1.0678 0.7577 0.8267 0.6931 0.7577 0.9008 1.1632 1.0678 1.0678 0.5754 0.8267 0.8267 0.6325 0.6325 0.6325 0.7577 0.7577 0.7577 0.8267 0.8267 1.2685 1.3863 0.9808 0.9808 0.9808 0.7577 0.8267 0.6931 1.0678 0.8267 1.2685 1.5198 1.1632 2.0794 1.3863 1.5198 1.8563 1.2685 1.6740 0.7577 1.6740 1.3863 1.5198 1.2685 1.8563 1.3863 1.2685 1.1632 1.2685 1.8563 1.2685 1.5198 0.9008 1.3863 1.6740 0.0000 0.5213 0.7577 0.6325 0.0000 0.6325 0.0000 0.6931 1.2685 0.0000 0.6325 1.3863 0.0000 1.1632 0.7577 1.1632 0.0000 0.9008 0.0000 0.7577 0.9808 0.9008 0.0000 0.0000 0.8267 WordNet-Similarity-2.05/t/keys/0.06/leskpairs.key0000644000175000017500000000144511015336623017602 0ustar sidsid6.0000 5.0000 3.0000 39.0000 7.0000 5.0000 38.0000 6.0000 7.0000 50.0000 18.0000 14.0000 3.0000 2.0000 7.0000 4.0000 4.0000 11.0000 5.0000 5.0000 4.0000 10.0000 0.0000 4.0000 3.0000 8.0000 5.0000 27.0000 14.0000 15.0000 50.0000 17.0000 13.0000 25.0000 8.0000 7.0000 2.0000 9.0000 12.0000 12.0000 6.0000 8.0000 8.0000 0.0000 4.0000 14.0000 6.0000 15.0000 2.0000 1.0000 11.0000 17.0000 5.0000 5.0000 4.0000 5.0000 9.0000 3.0000 14.0000 15.0000 5.0000 3.0000 1.0000 2.0000 8.0000 7.0000 3.0000 2.0000 8.0000 0.0000 11.0000 0.0000 2.0000 1.0000 16.0000 6.0000 1.0000 5.0000 4.0000 5.0000 1.0000 4.0000 3.0000 5.0000 8.0000 0.0000 16.0000 41.0000 13.0000 3.0000 15.0000 3.0000 6.0000 3.0000 1.0000 2.0000 10.0000 2.0000 12.0000 12.0000 16.0000 1.0000 5.0000 3.0000 25.0000 11.0000 5.0000 8.0000 7.0000 12.0000 WordNet-Similarity-2.05/t/keys/0.06/jcntrace.key0000644000175000017500000000627311015336623017402 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (Freq=90) Concept1: dog#n#1 (Freq=54) Concept2: cat#n#1 (Freq=24) Relatedness 0 across parts of speech (untruly#r#1, expositive#a#1). Only verbs and nouns have hypernym trees (noncyclic#a#1, indecisive#a#2). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root* (Freq=295901) Concept1: art#n#3 (Freq=13) Concept2: Hadrian#n#1 (Freq=0) Relatedness 0 across parts of speech (mortise#v#1, Cystophora_cristata#n#1). HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root* (Freq=122343) Concept1: polychromize#v#1 (Freq=0) Concept2: stress#v#2 (Freq=4) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): *Root* (Freq=295901) diversion#n#1 (Freq=793) activity#n#1 (Freq=15250) Concept1: professional_football#n#1 (Freq=0) Concept2: scat#n#1 (Freq=0) WordNet-Similarity-2.05/t/keys/0.06/lchtrace.key0000644000175000017500000000550711015336623017375 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 LCS: carnivore#n#1 Path length: 5. Relatedness 0 across parts of speech (untruly#r#1, expositive#a#1). Only verbs and nouns have hypernym trees (noncyclic#a#1, indecisive#a#2). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 LCS: *Root*#n#1 Path length: 14. Relatedness 0 across parts of speech (mortise#v#1, Cystophora_cristata#n#1). HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 LCS: *Root*#v#1 Path length: 6. HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 LCS: activity#n#1 Path length: 8. WordNet-Similarity-2.05/t/keys/0.06/jcnpairs.key0000644000175000017500000000140211015336623017407 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0424 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0408 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0502 0.0774 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0532 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0415 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.06/respairs.key0000644000175000017500000000151511015336623017433 0ustar sidsid-0.0000 -0.0000 0.8555 -0.0000 -0.0000 -0.0000 4.3095 -0.0000 2.5580 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 1.3709 1.3709 -0.0000 -0.0000 0.8555 0.8555 1.3709 -0.0000 1.3709 -0.0000 0.8555 -0.0000 -0.0000 -0.0000 0.8555 -0.0000 -0.0000 0.8555 -0.0000 -0.0000 2.5356 2.4549 -0.0000 -0.0000 2.4596 -0.0000 -0.0000 -0.0000 0.8555 -0.0000 0.8555 1.3709 1.7064 -0.0000 2.1429 3.7388 -0.0000 -0.0000 2.5580 -0.0000 0.8555 0.8555 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 0.0000 -0.0000 1.3709 0.0000 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 0.0000 -0.0000 1.3709 -0.0000 0.0000 0.0000 -0.0000 WordNet-Similarity-2.05/t/keys/0.06/wuptrace.key0000644000175000017500000000455311015336623017442 0ustar sidsidWordNet::Similarity::wup object created: trace :: 1 cache :: 1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 LCS: carnivore#n#1 depth(dog#n#1) = 12 depth(cat#n#1) = 12 depth(carnivore#n#1) = 10 Relatedness 0 across parts of speech.HyperTree: *Root*#a#1 noncyclic#a#1 HyperTree: *Root*#a#1 indecisive#a#2 LCS: *Root*#v#1 depth(noncyclic#a#1) = depth(indecisive#a#2) = depth(*Root*#v#1) = 0 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 entity#n#1 entity#n#1 organism#n#1 causal_agent#n#1 object#n#1 person#n#1 living_thing#n#1 entity#n#1 communicator#n#1 organism#n#1 causal_agent#n#1 negotiator#n#1 person#n#1 representative#n#1 ruler#n#2 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 LCS: *Root*#n#1 depth(art#n#3) = 5 depth(Hadrian#n#1) = 8 depth(*Root*#n#1) = 0 Relatedness 0 across parts of speech.HyperTree: *Root*#v#1 change#v#2 color#v#1 polychromize#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 LCS: *Root*#v#1 depth(polychromize#v#1) = 3 depth(stress#v#2) = 2 depth(*Root*#v#1) = 0 HyperTree: *Root*#n#1 act#n#2 activity#n#1 act#n#2 diversion#n#1 activity#n#1 act#n#2 sport#n#1 game#n#2 activity#n#1 athletic_game#n#1 diversion#n#1 act#n#2 outdoor_game#n#1 sport#n#1 activity#n#1 field_game#n#1 contact_sport#n#1 occupation#n#1 football#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 psychological_feature#n#1 abstraction#n#6 activity#n#1 cognition#n#1 abstraction#n#6 relation#n#1 diversion#n#1 content#n#5 relation#n#1 social_relation#n#1 entertainment#n#1 knowledge_domain#n#1 social_relation#n#1 communication#n#2 act#n#2 show#n#1 discipline#n#1 communication#n#2 auditory_communication#n#1 activity#n#1 presentation#n#3 humanistic_discipline#n#1 expressive_style#n#1 music#n#1 music#n#3 performance#n#3 performing_arts#n#1 music_genre#n#1 vocal_music#n#1 musical_performance#n#1 popular_music#n#1 singing#n#1 jazz#n#2 scat#n#1 LCS: activity#n#1 depth(professional_football#n#1) = 5 depth(scat#n#1) = 6 depth(activity#n#1) = 2 WordNet-Similarity-2.05/t/keys/0.06/restrace.key0000644000175000017500000000567511015336623017426 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (Freq=90) Relatedness 0 across parts of speech (untruly#r#1, expositive#a#1). Only verbs and nouns have hypernym trees (noncyclic#a#1, indecisive#a#2). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root* (Freq=295901) Relatedness 0 across parts of speech (mortise#v#1, Cystophora_cristata#n#1). HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root* (Freq=122343) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): *Root* (Freq=295901) diversion#n#1 (Freq=793) activity#n#1 (Freq=15250) WordNet-Similarity-2.05/t/keys/0.06/edgepairs.key0000644000175000017500000000140211015336623017541 0ustar sidsid0.0714 0.0455 0.0769 0.0667 0.0526 0.0667 0.1667 0.0667 0.1000 0.0556 0.0526 0.0500 0.0667 0.0526 0.0667 0.0625 0.0625 0.0833 0.0526 0.0769 0.0667 0.0714 0.0714 0.0556 0.0625 0.0588 0.0833 0.0625 0.0714 0.0714 0.0909 0.0667 0.0714 0.0625 0.0667 0.0769 0.1000 0.0909 0.0909 0.0556 0.0714 0.0714 0.0588 0.0588 0.0588 0.0667 0.0667 0.0667 0.0714 0.0714 0.1111 0.1250 0.0833 0.0833 0.0833 0.0667 0.0714 0.0625 0.0909 0.0714 0.1111 0.1429 0.1000 0.2500 0.1250 0.1429 0.2000 0.1111 0.1667 0.0667 0.1667 0.1250 0.1429 0.1111 0.2000 0.1250 0.1111 0.1000 0.1111 0.2000 0.1111 0.1429 0.0769 0.1250 0.1667 0.0000 0.0526 0.0667 0.0588 0.0000 0.0588 0.0000 0.0625 0.1111 0.0000 0.0588 0.1250 0.0000 0.1000 0.0667 0.1000 0.0000 0.0769 0.0000 0.0667 0.0833 0.0769 0.0000 0.0000 0.0714 WordNet-Similarity-2.05/t/keys/0.06/linpairs.key0000644000175000017500000000140211015336623017417 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.06/wuppairs.key0000644000175000017500000000140211015336623017450 0ustar sidsid0.0000 0.0000 0.1429 0.0000 0.0000 0.0000 0.6154 0.0000 0.4000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.2105 0.1538 0.0000 0.0000 0.1250 0.1333 0.1333 0.0000 0.2105 0.0000 0.1538 0.0000 0.0000 0.0000 0.1667 0.0000 0.0000 0.1176 0.0000 0.0000 0.4000 0.4286 0.0000 0.0000 0.4211 0.0000 0.0000 0.0000 0.1111 0.0000 0.1250 0.1250 0.1333 0.0000 0.2000 0.5333 0.0000 0.0000 0.3529 0.0000 0.1333 0.1176 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.3636 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.2667 0.0000 0.0000 0.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.06/hsopairs.key0000644000175000017500000000140211015336623017426 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 3.0000 0.0000 0.0000 3.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.06/edgetrace.key0000644000175000017500000000550711015336623017533 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 LCS: carnivore#n#1 Path length: 5. Relatedness 0 across parts of speech (untruly#r#1, expositive#a#1). Only verbs and nouns have hypernym trees (noncyclic#a#1, indecisive#a#2). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 LCS: *Root*#n#1 Path length: 14. Relatedness 0 across parts of speech (mortise#v#1, Cystophora_cristata#n#1). HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 LCS: *Root*#v#1 Path length: 6. HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 LCS: activity#n#1 Path length: 8. WordNet-Similarity-2.05/t/keys/0.06/lintrace.key0000644000175000017500000000627311015336623017412 0ustar sidsidHyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 canine#n#2 dog#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 animal#n#1 chordate#n#1 vertebrate#n#1 mammal#n#1 placental#n#1 carnivore#n#1 feline#n#1 cat#n#1 Lowest Common Subsumer(s): carnivore#n#1 (Freq=90) Concept1: dog#n#1 (Freq=54) Concept2: cat#n#1 (Freq=24) Relatedness 0 across parts of speech (untruly#r#1, expositive#a#1). Only verbs and nouns have hypernym trees (noncyclic#a#1, indecisive#a#2). HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 ability#n#2 superior_skill#n#1 art#n#3 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 ruler#n#2 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 object#n#1 living_thing#n#1 organism#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 HyperTree: *Root*#n#1 entity#n#1 causal_agent#n#1 person#n#1 communicator#n#1 negotiator#n#1 representative#n#1 head_of_state#n#1 sovereign#n#1 Emperor#n#1 Roman_Emperor#n#1 Hadrian#n#1 Lowest Common Subsumer(s): *Root* (Freq=295901) Concept1: art#n#3 (Freq=13) Concept2: Hadrian#n#1 (Freq=0) Relatedness 0 across parts of speech (mortise#v#1, Cystophora_cristata#n#1). HyperTree: *Root*#v#1 change#v#2 color#v#1 polychrome#v#1 HyperTree: *Root*#v#1 pronounce#v#1 stress#v#2 Lowest Common Subsumer(s): *Root* (Freq=122343) Concept1: polychromize#v#1 (Freq=0) Concept2: stress#v#2 (Freq=4) HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 game#n#2 athletic_game#n#1 outdoor_game#n#1 field_game#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 sport#n#1 contact_sport#n#1 football#n#1 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 occupation#n#1 sport#n#2 professional_football#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 music#n#3 vocal_music#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 act#n#2 activity#n#1 diversion#n#1 entertainment#n#1 show#n#1 presentation#n#3 performance#n#3 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 psychological_feature#n#1 cognition#n#1 content#n#5 knowledge_domain#n#1 discipline#n#1 humanistic_discipline#n#1 performing_arts#n#1 musical_performance#n#1 singing#n#1 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 expressive_style#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 HyperTree: *Root*#n#1 abstraction#n#6 relation#n#1 social_relation#n#1 communication#n#2 auditory_communication#n#1 music#n#1 music_genre#n#1 popular_music#n#1 jazz#n#2 scat#n#1 Lowest Common Subsumer(s): *Root* (Freq=295901) diversion#n#1 (Freq=793) activity#n#1 (Freq=15250) Concept1: professional_football#n#1 (Freq=0) Concept2: scat#n#1 (Freq=0) WordNet-Similarity-2.05/t/keys/0.06/wuppairs_nr.key0000644000175000017500000000140211015336623020147 0ustar sidsid0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.5455 0.0000 0.3077 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.1176 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.1176 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.3077 0.3333 0.0000 0.0000 0.3529 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.4615 0.0000 0.0000 0.2667 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.2222 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.1538 0.0000 0.0000 0.0000 0.0000 WordNet-Similarity-2.05/t/keys/0.06/hsotrace.key0000644000175000017500000000435311015336623017416 0ustar sidsidHorizontal Links of dog#n#1: Upward Links of dog#n#1: canine#n#2 flag#n#7 Downward Links of dog#n#1: Canis#n#1 pack#n#6 pooch#n#1 cur#n#1 lapdog#n#1 toy_dog#n#1 hunting_dog#n#1 working_dog#n#1 dalmatian#n#2 basenji#n#1 pug#n#1 Leonberg#n#1 Newfoundland#n#1 Great_Pyrenees#n#1 spitz#n#1 griffon#n#1 corgi#n#1 poodle#n#1 Mexican_hairless#n#1 Horizontal Links of cat#n#1: Upward Links of cat#n#1: feline#n#1 Downward Links of cat#n#1: domestic_cat#n#1 wildcat#n#3 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] carnivore#n#1 [D] feline#n#1 [D] cat#n#1 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] paw#n#1 [D] feline#n#1 [D] cat#n#1 Horizontal Links of untruly#r#1: untrue#a#1 Upward Links of untruly#r#1: Downward Links of untruly#r#1: Horizontal Links of expository#a#1: instructive#a#1 Upward Links of expository#a#1: Downward Links of expository#a#1: Horizontal Links of noncyclic#a#1: cyclic#a#4 cyclicity#n#1 Upward Links of noncyclic#a#1: Downward Links of noncyclic#a#1: Horizontal Links of indecisive#a#2: inconclusive#a#1 irresolute#a#1 decisive#a#1 inconclusive#a#2 Upward Links of indecisive#a#2: Downward Links of indecisive#a#2: Horizontal Links of art#n#3: Upward Links of art#n#3: superior_skill#n#1 Downward Links of art#n#3: aviation#n#3 eristic#n#2 falconry#n#1 fortification#n#2 homiletics#n#2 horology#n#1 minstrelsy#n#3 musicianship#n#1 enology#n#1 puppetry#n#1 taxidermy#n#1 ventriloquism#n#1 Horizontal Links of Hadrian#n#1: Upward Links of Hadrian#n#1: Roman_Emperor#n#1 Downward Links of Hadrian#n#1: Horizontal Links of mortise#v#1: Upward Links of mortise#v#1: cut#v#1 Downward Links of mortise#v#1: Horizontal Links of hooded_seal#n#1: Upward Links of hooded_seal#n#1: earless_seal#n#1 Downward Links of hooded_seal#n#1: Cystophora#n#1 Horizontal Links of polychrome#v#1: Upward Links of polychrome#v#1: color#v#1 Downward Links of polychrome#v#1: Horizontal Links of stress#v#2: Upward Links of stress#v#2: pronounce#v#1 Downward Links of stress#v#2: Horizontal Links of professional_football#n#1: Upward Links of professional_football#n#1: football#n#1 sport#n#2 Downward Links of professional_football#n#1: Horizontal Links of scat#n#1: Upward Links of scat#n#1: singing#n#1 jazz#n#2 Downward Links of scat#n#1: WordNet-Similarity-2.05/t/keys/hsotrace.key0000644000175000017500000000440011015336623017024 0ustar sidsidHorizontal Links of dog#n#1: Upward Links of dog#n#1: canine#n#2 domestic_animal#n#1 flag#n#7 Downward Links of dog#n#1: Canis#n#1 pack#n#6 puppy#n#1 pooch#n#1 cur#n#1 lapdog#n#1 toy_dog#n#1 hunting_dog#n#1 working_dog#n#1 dalmatian#n#2 basenji#n#1 pug#n#1 Leonberg#n#1 Newfoundland#n#1 Great_Pyrenees#n#1 spitz#n#1 griffon#n#2 corgi#n#1 poodle#n#1 Mexican_hairless#n#1 Horizontal Links of cat#n#1: Upward Links of cat#n#1: feline#n#1 Downward Links of cat#n#1: domestic_cat#n#1 wildcat#n#3 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] carnivore#n#1 [D] feline#n#1 [D] cat#n#1 MedStrong relation path... dog#n#1 [U] canine#n#2 [U] paw#n#1 [D] feline#n#1 [D] cat#n#1 Horizontal Links of untruly#r#1: untrue#a#1 Upward Links of untruly#r#1: Downward Links of untruly#r#1: Horizontal Links of expository#a#1: instructive#a#1 Upward Links of expository#a#1: Downward Links of expository#a#1: Horizontal Links of noncyclic#a#1: cyclic#a#4 cyclicity#n#1 Upward Links of noncyclic#a#1: Downward Links of noncyclic#a#1: Horizontal Links of indecisive#a#2: inconclusive#a#1 irresolute#a#1 decisive#a#1 Upward Links of indecisive#a#2: Downward Links of indecisive#a#2: Horizontal Links of art#n#3: Upward Links of art#n#3: superior_skill#n#1 Downward Links of art#n#3: aviation#n#3 eristic#n#2 falconry#n#1 fortification#n#2 homiletics#n#2 horology#n#1 minstrelsy#n#3 musicianship#n#1 enology#n#1 puppetry#n#1 taxidermy#n#1 telescopy#n#1 ventriloquism#n#1 Horizontal Links of Hadrian#n#1: Upward Links of Hadrian#n#1: Roman_Emperor#n#1 Downward Links of Hadrian#n#1: Horizontal Links of mortise#v#1: Upward Links of mortise#v#1: cut#v#1 Downward Links of mortise#v#1: Horizontal Links of hooded_seal#n#1: Upward Links of hooded_seal#n#1: earless_seal#n#1 Downward Links of hooded_seal#n#1: Cystophora#n#1Horizontal Links of polychrome#v#1: Upward Links of polychrome#v#1: color#v#1 Downward Links of polychrome#v#1: Horizontal Links of stress#v#2: Upward Links of stress#v#2: pronounce#v#1 Downward Links of stress#v#2: Horizontal Links of professional_football#n#1: Upward Links of professional_football#n#1: football#n#1 sport#n#2 Downward Links of professional_football#n#1: Horizontal Links of scat#n#1: Upward Links of scat#n#1: singing#n#1 jazz#n#2 Downward Links of scat#n#1: WordNet-Similarity-2.05/t/README0000644000175000017500000000057311015336623014416 0ustar sidsid The following scripts require "keys" for the tests to run correctly: pairs.t pairs-noroot.t tract.t utils/wnDepths.t These scripts support two command-line options: 1) the --key option can be used to generate a key, in which case no actual tests are run. 2) the --keydir option can be used to specify where the key is or should be stored. The default value is t/keys. WordNet-Similarity-2.05/t/pairs.txt0000644000175000017500000000613711015336623015417 0ustar sidsidTylenchus#n#1 s.t.p.#n#1 genus_Jabiru#n#1 kiang#n#1 Puebla_de_Zaragoza#n#1 abuser#n#1 Bhagavadgita#n#1 stake_race#n#1 major_key#n#1 Baldr#n#1 anopia#n#1 nerd#n#1 tensor#n#2 cheek#n#3 Merlot#n#2 genus_Zaglossus#n#1 thriftshop#n#1 take-up#n#1 Haematoxylum_campechianum#n#1 Aegiceras#n#1 Hippo_Regius#n#1 Zaar#n#1 Katmandu#n#1 Southern_Tai#n#1 animalization#n#2 Papal_infallibility#n#1 European_gallinule#n#1 obstruction#n#3 bayonet#n#1 estrangement#n#1 jackpot#n#2 Western_Malayo-Polynesian#n#1 bear_cub#n#1 battlesight#n#1 detective#n#2 Queensboro_Bridge#n#1 walnut_blight#n#1 etymology#n#1 parallelism#n#1 double_Dutch#n#2 Cardiospermum_grandiflorum#n#1 Acapulco_gold#n#1 trichloromethane#n#1 rood-tree#n#1 Samson#n#1 seeded_raisin#n#1 leveling#n#2 sister_ship#n#1 Pennines#n#1 female_horse#n#1 vicereine#n#1 chronology#n#2 music_school#n#2 spawl#n#1 meningism#n#1 scope#n#3 ear#n#5 unalterability#n#2 join#n#2 Japanese_wistaria#n#1 veggie#n#1 Spaniard#n#1 volunteer#n#1 la#n#3 anesthyl#n#1 IR#n#2 labyrinth#n#2 green_alder#n#1 Haemoproteidae#n#1 credit_application#n#1 ether#n#4 cryoscope#n#1 book_agent#n#1 Arius#n#1 egg#n#1 Vasco_Nunez_de_Balboa#n#1 lady_killer#n#1 concretion#n#1 epidemic_roseola#n#1 tenia#n#1 Emberiza_aureola#n#1 begum#n#1 preferment#n#1 yard_grass#n#1 screen_actor#n#1 blind#n#4 repeating_firearm#n#1 genus_Lophosoria#n#1 saddlebill#n#1 methadone_hydrochloride#n#1 corn#n#3 Chinaman#n#2 bocconia#n#2 parafovea#n#1 redcap#n#2 X-ray_machine#n#1 pablum#n#2 spread#n#8 valuation#n#2 virtuosity#n#1 workshop#n#2 truckling#n#1 reading_lamp#n#1 rig#n#1 sire#n#3 comparative_negligence#n#1 saver#n#2 venture#n#3 angiocardiogram#n#1 jewel_casket#n#1 Melampodium_leucanthum#n#1 silver_storm#n#1 row_of_bricks#n#1 pencil_cedar#n#2 atomic_warhead#n#1 Ligustrum_obtusifolium#n#1 thing#n#12 elephant#n#2 organon#n#1 Baku#n#1 territorialize#v#3 break_down#v#5 lift#v#19 stoop#v#5 stuff#v#2 shear#v#1 be#v#2 breathe#v#9 festoon#v#1 put_to_sleep#v#3 immigrate#v#1 dress#v#10 clear_up#v#3 ply#v#5 bring_in#v#4 etch#v#1 play#v#7 advect#v#1 romance#v#4 champ#v#1 retreat#v#2 become#v#2 regret#v#2 prelude#v#2 swage#v#1 play_out#v#4 drool_over#v#1 drive#v#16 pull#v#9 urbanize#v#2 take_back#v#6 wash_one's_hands#v#1 fly_blind#v#1 knock_out#v#3 pay_off#v#5 take_a_bow#v#2 disinter#v#1 pace#v#4 dig_in#v#1 trek#v#2 sluice#v#1 medicate#v#1 rule#v#2 break_short#v#1 palaver#v#3 subject#v#1 smite#v#1 aquaplane#v#2 bach#v#1 hop#v#3 bowstring_hemp#n#1 lipread#v#1 Epistle_to_the_Colossians#n#1 greenmail#n#1 bugbane#n#2 central_bank#n#1 Gleditsia#n#1 capital_of_Idaho#n#1 come#v#2 Trichostema_lanceolatum#n#1 order_Aphyllophorales#n#1 tallith#n#1 scrambled#a#1 in_vivo#r#1 constitutional#n#1 rolling_pin#n#1 retrovirus#n#1 cone#n#1 knickknack#n#2 blow#v#2 carry-forward#n#1 line_officer#n#1 nark#v#2 matter_to#v#1 net_fish#v#1 realistically#r#2 desorption#n#1 song#n#3 mammothermography#n#1 Khoikhoin#n#1 Gorgoniacea#n#1 portion#n#2 Medicaid#n#1 put_back#v#2 concertina#n#2 quietus#n#1 aleph#n#1 waist#n#2 hull#n#1 chronoscope#n#1 written_assignment#n#1 Marcel_Lajos_Breuer#n#1 discredit#v#2 uncreativeness#n#1 dreamed#a#1 James_I#n#1 IW#n#1 involutional_depression#n#1 WordNet-Similarity-2.05/t/trace.t0000755000175000017500000001250211020104750015005 0ustar sidsid#! /usr/bin/perl -w # trace.t version 2.05 # ($Id: trace.t,v 1.9 2008/05/30 23:12:40 sidz1979 Exp $) # # Copyright (C) 2004 # # Jason Michelizzi, University of Minnesota Duluth # mich0212 at d.umn.edu # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # Before 'make install' is performed this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/trace.t' # A script to tracing in the relatedness modules. The following tests are # run: # * A fixed set of word pairs is used to generate trace strings which are # compared to a "key" (stored in t/keys). # This script supports two options: # 1) the --key option can be used to generate a key, in which case no actual # tests are run. # 2) the --keydir option can be used to specify where the key is or should be # stored. The default value is t/keys. use strict; use warnings; my @measures; my $num_tests; my @wordpairs; sub diff ($$); BEGIN { # the names of the measures we're going to test. Testing random # doesn't seem to make much since, and the vector module is still # experimental. @measures = qw/hso jcn lch lesk lin path res wup/; # the set of words for which we are going to calculate relatedness # values (the testing is being done on the trace strings generated # while calculating relatedness) @wordpairs = (['dog#n#1', 'cat#n#1'], ['untruly#r#1', 'expositive#a#1'], ['noncyclic#a#1', 'indecisive#a#2'], ['art#n#3','Hadrian#n#1'], ['mortise#v#1', 'Cystophora_cristata#n#1'], ['polychromize#v#1', 'stress#v#2'], # ['ambrose#n#1', 'john_chrysostom#n#1'], ['professional_football#n#1', 'scat#n#1'] ); $num_tests = 20 + 7 * (scalar @measures) + scalar (@measures) * scalar (@wordpairs); } use Test::More tests => $num_tests; BEGIN {use_ok 'Getopt::Long'} our ($opt_key, $opt_keydir); my $result = GetOptions ("key", "keydir=s"); ok ($result); BEGIN {use_ok 'WordNet::QueryData'} BEGIN {use_ok 'WordNet::Tools'} BEGIN {use_ok 'WordNet::Similarity::hso'} BEGIN {use_ok 'WordNet::Similarity::jcn'} BEGIN {use_ok 'WordNet::Similarity::lch'} BEGIN {use_ok 'WordNet::Similarity::lesk'} BEGIN {use_ok 'WordNet::Similarity::lin'} BEGIN {use_ok 'WordNet::Similarity::path'} #BEGIN {use_ok 'WordNet::Similarity::random'} BEGIN {use_ok 'WordNet::Similarity::res'} #BEGIN {use_ok 'WordNet::Similarity::vector'} BEGIN {use_ok 'WordNet::Similarity::wup'} BEGIN {use_ok 'File::Spec'} BEGIN {use_ok 'File::Copy'} my $wn = WordNet::QueryData->new; ok ($wn) or diag "Could not create WordNet::QueryData object"; my $wntools = WordNet::Tools->new($wn); ok ($wntools); my $wnHash = $wntools->hashCode(); ok ($wnHash); my $config = "trace$$.cfg"; my $tempfile = "trace$$.tmp"; my $keydir = File::Spec->catdir ('t', 'keys'); $keydir = $opt_keydir if ($opt_keydir); my $wnkey = File::Spec->catfile ($keydir, "wnver.key"); ok (open WNF, $wnkey); my $wnver = ; ok (defined $wnver); $wnver = "" if(!defined($wnver)); $wnver =~ s/[\r\f\n]+//g; $wnver =~ s/^\s+//; $wnver =~ s/\s+$//; ok (close WNF); SKIP: { skip "Hash-code of key file(s) does not match installed WordNet", 112 if($wnver ne $wnHash); foreach my $measure (@measures) { ok (open CFH, '>', $config) or diag "Could not open $config for writing: $!"; print CFH "WordNet::Similarity::$measure\ntrace::1\n"; ok (close CFH) or diag "Could not close $config: $!"; my $module = "WordNet::Similarity::$measure"->new ($wn, $config); # my $module = "WordNet::Similarity::$measure"->new ($wn); ok ($module) or diag "Failed to create $measure module"; my ($err, $errstr) = $module->getError (); is ($err, 0) or diag $errstr; # turn on tracing $module->{trace} = 1; ok (open TFH, '>', $tempfile) or diag "Could not open $tempfile: $!"; foreach my $pair (@wordpairs) { my ($wps1, $wps2) = @$pair; my $score = $module->getRelatedness ($wps1, $wps2); my $tracestr = $module->getTraceString (); $module->getError (); # clear errors ok ($tracestr) or diag "$measure ($wps1, $wps2) has no trace"; print TFH $tracestr; } my $keyfile = File::Spec->catfile ($keydir, "${measure}trace.key"); unless ($opt_key) { ok (close TFH) or diag "Could not close $tempfile: $!"; my ($diff, $msg) = diff ($tempfile, $keyfile); is ($diff, 0) or diag "$measure: $msg"; } else { ok (close TFH) or diag "Could not close $tempfile: $!"; ok (copy $tempfile, $keyfile) or diag "Could not copy file $tempfile to $keyfile: $!"; } } } END { unlink $config; unlink $tempfile; } sub diff ($$) { my ($file1, $file2) = @_; open FH1, $file1 or return (1, "Couldn't open '$file1': $!"); open FH2, $file2 or return (2, "Couldn't open '$file2': $!"); # chomp'ing the lines might not be necessary, but it might help # if one file were generated on DOS and the other on Unix. On # the other hand, it might not, but in any case, I don't think that # it'd hurt. my @lines1 = map {chomp; $_} ; my @lines2 = map {chomp; $_} ; unless (scalar (@lines1) == scalar (@lines2)) { my $got = scalar @lines1; my $want = scalar @lines2; return (3, "Wrong number of lines: got $got expected $want"); } for (0..$#lines1) { if ($lines1[$_] ne $lines2[$_]) { return (4, "The lines '$lines1[$_]' and '$lines2[$_]' differ"); } } return 0; } WordNet-Similarity-2.05/t/pod.t0000755000175000017500000000044011020101570014464 0ustar sidsid#! /usr/bin/perl -w # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/pod.t' use Test::More; eval "use Test::Pod 1.00"; plan skip_all => "Test::Pod 1.00 required for testing POD" if $@; all_pod_files_ok(); WordNet-Similarity-2.05/t/res.t0000755000175000017500000000553711020101624014507 0ustar sidsid#! /usr/bin/perl -w # Before `make install' is performed this script should be runnable with # `make test'. After `make install' it should work as `perl t/res.t' # A script to test the Resnik (res.pm) measure. This script runs the # following tests: # # 1) tests whether the modules are constructed correctly # 2) tests whether an error is given when no WordNet::QueryData object # is supplied # 3) simply getRelatedness queries are performed on valid words, invalid # words, and words from different parts of speech ##################### We start with some black magic to print on failure. BEGIN { $| = 1; print "1..8\n"; } END {print "not ok 1\n" unless $loaded;} use WordNet::Similarity; use WordNet::QueryData; use WordNet::Similarity::res; $loaded = 1; print "ok 1\n"; ######################### End of black magic. # Insert your test code below (better if it prints "ok 13" # (correspondingly "not ok 13") depending on the success of chunk 13 # of the test code): ############ Load QueryData $wn = WordNet::QueryData->new(); if($wn) { print "ok 2\n"; } else { print "not ok 2\n"; } ############ Load res $m = WordNet::Similarity::res->new($wn); if($m) { ($err, $errString) = $m->getError(); if($err) { print "not ok 3\n"; } else { print "ok 3\n"; } } else { print "not ok 3\n"; } ############ Load res with undef QueryData. $bad = WordNet::Similarity::res->new(undef); if($bad) { ($err, $errString) = $bad->getError(); if($err < 2) { print "not ok 4\n"; } elsif($err == 2) { if($errString =~ /A WordNet::QueryData object is required/) { print "ok 4\n"; } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } } else { print "not ok 4\n"; } ############ GetRelatedness of same synset. $value = $m->getRelatedness("object#n#1", "object#n#1"); if($value && $value =~ /[0-9]+(\.[0-9]+)?/) { if($value > 0) { print "ok 5\n"; } else { print "not ok 5\n"; } } else { print "not ok 5\n"; } ############ getRelatedness of badly formed synset. ## (Tried getRelatedness of unknown synsets... "hjxlq#n#1", "pynbr#n#2"... ## QueryData complains... can't trap that error myself.) if(defined $m->getRelatedness("hjxlq#n", "pynbr#n")) { print "not ok 6\n"; } else { ($err, $errString) = $m->getError(); if($err == 1) { print "ok 6\n"; } else { print "not ok 6\n"; } } ############ Relatedness across parts of speech. $m->{'trace'} = 1; if($m->getRelatedness("object#n#1", "run#v#1") >= 0) { print "not ok 7\n"; } else { print "ok 7\n"; } ############ Test traces. # JM 1-6-04 # we changed how words from different parts of speech are handled #if($m->getTraceString() !~ /Relatedness 0 across parts of speech/) if (($m->getError())[0] != 1) { print "not ok 8\n"; } else { print "ok 8\n"; } WordNet-Similarity-2.05/t/offsetwps.t0000755000175000017500000000464111020101243015726 0ustar sidsid#! /usr/bin/perl -w # offsetwps.t version 2.01 # (Updated 2/10/2004 -- Jason) # # Copyright (C) 2004 # # Jason Michelizzi, University of Minnesota Duluth # mich0212 at d.umn.edu # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # Before 'make install' is run this script should be runnable with # 'make test'. After 'make install' it should work as 'perl t/offsetwps.t' # A script to test whether the offset method of finding paths and LCSs # is equivalent to the wps method. The tests are conducted as follows: # # * The LCS for a pair of word senses is found. The offsets for those # words are found, and then the LCS is found for those offsets. A # test succeeds if the offset of the LCS using wps is the same as the # LCS using offsets. use strict; use warnings; my $num_tests; my @cases; BEGIN { # test cases: the first two elements are wps strings whose LCS will # be found. The third element is the part of speech (n, v, a, or r). @cases = (['dog#n#1', 'cat#n#1', 'n'], ['tree#n#1', 'bush#n#1', 'n'], ['adore#v#1', 'worship#v#1', 'v'], # ['shout#v#2', 'cry#v#1', 'v'], # ['car#n#1', 'auto#n#1', 'n'], ['poet#n#1', 'poet#n#1', 'n']); $num_tests = 5 + 2 * scalar @cases; } use Test::More tests => $num_tests; BEGIN {use_ok 'WordNet::QueryData'} BEGIN {use_ok 'WordNet::Similarity::PathFinder'} my $wn = WordNet::QueryData->new; ok ($wn) or diag "Failed to load WordNet::QueryData"; my $lf = WordNet::Similarity::PathFinder->new ($wn); ok ($lf) or diag "Failed to create WordNet::Similarity::PathFinder module"; my ($err, $errstr) = $lf->getError (); is ($err, 0) or diag ($errstr); foreach my $case (@cases) { my ($wps1, $wps2, $pos) = @$case; my @wps_paths = $lf->getShortestPath ($wps1, $wps2, $pos, 'wps'); my $wps_length = $wps_paths[0]->[0]; my $offset1 = $wn->offset ($wps1, $pos); my $offset2 = $wn->offset ($wps2, $pos); my @off_paths = $lf->getShortestPath ($offset1, $offset2, $pos, 'offset'); my $off_length = $off_paths[0]->[0]; is ($off_length, $wps_length, "testing length between $wps1 and $wps2"); my @wps_lcss = $lf->getLCSbyPath ($wps1, $wps2, $pos, 'wps'); my $wps_lcs = $wps_lcss[0]->[0]; my @off_lcss = $lf->getLCSbyPath ($offset1, $offset2, $pos, 'offset'); my $off_lcs = $off_lcss[0]->[0]; my $offset = $wps_lcs =~ /\Q*Root*\E/i ? 0 : $wn->offset ($wps_lcs); is ($off_lcs, $offset, "testing LCS of $wps1 and $wps2"); } WordNet-Similarity-2.05/GPL.txt0000644000175000017500000004365511015336623014466 0ustar sidsid GNU GENERAL PUBLIC LICENSE Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc. 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The licenses for most software are designed to take away your freedom to share and change it. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change free software--to make sure the software is free for all its users. This General Public License applies to most of the Free Software Foundation's software and to any other program whose authors commit to using it. (Some other Free Software Foundation software is covered by the GNU Library General Public License instead.) You can apply it to your programs, too. When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for this service if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs; and that you know you can do these things. To protect your rights, we need to make restrictions that forbid anyone to deny you these rights or to ask you to surrender the rights. These restrictions translate to certain responsibilities for you if you distribute copies of the software, or if you modify it. For example, if you distribute copies of such a program, whether gratis or for a fee, you must give the recipients all the rights that you have. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights. We protect your rights with two steps: (1) copyright the software, and (2) offer you this license which gives you legal permission to copy, distribute and/or modify the software. Also, for each author's protection and ours, we want to make certain that everyone understands that there is no warranty for this free software. If the software is modified by someone else and passed on, we want its recipients to know that what they have is not the original, so that any problems introduced by others will not reflect on the original authors' reputations. Finally, any free program is threatened constantly by software patents. We wish to avoid the danger that redistributors of a free program will individually obtain patent licenses, in effect making the program proprietary. To prevent this, we have made it clear that any patent must be licensed for everyone's free use or not licensed at all. The precise terms and conditions for copying, distribution and modification follow. GNU GENERAL PUBLIC LICENSE TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION 0. This License applies to any program or other work which contains a notice placed by the copyright holder saying it may be distributed under the terms of this General Public License. The "Program", below, refers to any such program or work, and a "work based on the Program" means either the Program or any derivative work under copyright law: that is to say, a work containing the Program or a portion of it, either verbatim or with modifications and/or translated into another language. (Hereinafter, translation is included without limitation in the term "modification".) Each licensee is addressed as "you". Activities other than copying, distribution and modification are not covered by this License; they are outside its scope. The act of running the Program is not restricted, and the output from the Program is covered only if its contents constitute a work based on the Program (independent of having been made by running the Program). Whether that is true depends on what the Program does. 1. You may copy and distribute verbatim copies of the Program's source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice and disclaimer of warranty; keep intact all the notices that refer to this License and to the absence of any warranty; and give any other recipients of the Program a copy of this License along with the Program. You may charge a fee for the physical act of transferring a copy, and you may at your option offer warranty protection in exchange for a fee. 2. You may modify your copy or copies of the Program or any portion of it, thus forming a work based on the Program, and copy and distribute such modifications or work under the terms of Section 1 above, provided that you also meet all of these conditions: a) You must cause the modified files to carry prominent notices stating that you changed the files and the date of any change. b) You must cause any work that you distribute or publish, that in whole or in part contains or is derived from the Program or any part thereof, to be licensed as a whole at no charge to all third parties under the terms of this License. c) If the modified program normally reads commands interactively when run, you must cause it, when started running for such interactive use in the most ordinary way, to print or display an announcement including an appropriate copyright notice and a notice that there is no warranty (or else, saying that you provide a warranty) and that users may redistribute the program under these conditions, and telling the user how to view a copy of this License. (Exception: if the Program itself is interactive but does not normally print such an announcement, your work based on the Program is not required to print an announcement.) These requirements apply to the modified work as a whole. If identifiable sections of that work are not derived from the Program, and can be reasonably considered independent and separate works in themselves, then this License, and its terms, do not apply to those sections when you distribute them as separate works. But when you distribute the same sections as part of a whole which is a work based on the Program, the distribution of the whole must be on the terms of this License, whose permissions for other licensees extend to the entire whole, and thus to each and every part regardless of who wrote it. Thus, it is not the intent of this section to claim rights or contest your rights to work written entirely by you; rather, the intent is to exercise the right to control the distribution of derivative or collective works based on the Program. In addition, mere aggregation of another work not based on the Program with the Program (or with a work based on the Program) on a volume of a storage or distribution medium does not bring the other work under the scope of this License. 3. You may copy and distribute the Program (or a work based on it, under Section 2) in object code or executable form under the terms of Sections 1 and 2 above provided that you also do one of the following: a) Accompany it with the complete corresponding machine-readable source code, which must be distributed under the terms of Sections 1 and 2 above on a medium customarily used for software interchange; or, b) Accompany it with a written offer, valid for at least three years, to give any third party, for a charge no more than your cost of physically performing source distribution, a complete machine-readable copy of the corresponding source code, to be distributed under the terms of Sections 1 and 2 above on a medium customarily used for software interchange; or, c) Accompany it with the information you received as to the offer to distribute corresponding source code. (This alternative is allowed only for noncommercial distribution and only if you received the program in object code or executable form with such an offer, in accord with Subsection b above.) The source code for a work means the preferred form of the work for making modifications to it. For an executable work, complete source code means all the source code for all modules it contains, plus any associated interface definition files, plus the scripts used to control compilation and installation of the executable. However, as a special exception, the source code distributed need not include anything that is normally distributed (in either source or binary form) with the major components (compiler, kernel, and so on) of the operating system on which the executable runs, unless that component itself accompanies the executable. If distribution of executable or object code is made by offering access to copy from a designated place, then offering equivalent access to copy the source code from the same place counts as distribution of the source code, even though third parties are not compelled to copy the source along with the object code. 4. You may not copy, modify, sublicense, or distribute the Program except as expressly provided under this License. Any attempt otherwise to copy, modify, sublicense or distribute the Program is void, and will automatically terminate your rights under this License. However, parties who have received copies, or rights, from you under this License will not have their licenses terminated so long as such parties remain in full compliance. 5. You are not required to accept this License, since you have not signed it. However, nothing else grants you permission to modify or distribute the Program or its derivative works. These actions are prohibited by law if you do not accept this License. Therefore, by modifying or distributing the Program (or any work based on the Program), you indicate your acceptance of this License to do so, and all its terms and conditions for copying, distributing or modifying the Program or works based on it. 6. Each time you redistribute the Program (or any work based on the Program), the recipient automatically receives a license from the original licensor to copy, distribute or modify the Program subject to these terms and conditions. You may not impose any further restrictions on the recipients' exercise of the rights granted herein. You are not responsible for enforcing compliance by third parties to this License. 7. If, as a consequence of a court judgment or allegation of patent infringement or for any other reason (not limited to patent issues), conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot distribute so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not distribute the Program at all. For example, if a patent license would not permit royalty-free redistribution of the Program by all those who receive copies directly or indirectly through you, then the only way you could satisfy both it and this License would be to refrain entirely from distribution of the Program. If any portion of this section is held invalid or unenforceable under any particular circumstance, the balance of the section is intended to apply and the section as a whole is intended to apply in other circumstances. It is not the purpose of this section to induce you to infringe any patents or other property right claims or to contest validity of any such claims; this section has the sole purpose of protecting the integrity of the free software distribution system, which is implemented by public license practices. Many people have made generous contributions to the wide range of software distributed through that system in reliance on consistent application of that system; it is up to the author/donor to decide if he or she is willing to distribute software through any other system and a licensee cannot impose that choice. This section is intended to make thoroughly clear what is believed to be a consequence of the rest of this License. 8. If the distribution and/or use of the Program is restricted in certain countries either by patents or by copyrighted interfaces, the original copyright holder who places the Program under this License may add an explicit geographical distribution limitation excluding those countries, so that distribution is permitted only in or among countries not thus excluded. In such case, this License incorporates the limitation as if written in the body of this License. 9. The Free Software Foundation may publish revised and/or new versions of the General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. Each version is given a distinguishing version number. If the Program specifies a version number of this License which applies to it and "any later version", you have the option of following the terms and conditions either of that version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of this License, you may choose any version ever published by the Free Software Foundation. 10. If you wish to incorporate parts of the Program into other free programs whose distribution conditions are different, write to the author to ask for permission. For software which is copyrighted by the Free Software Foundation, write to the Free Software Foundation; we sometimes make exceptions for this. Our decision will be guided by the two goals of preserving the free status of all derivatives of our free software and of promoting the sharing and reuse of software generally. NO WARRANTY 11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. END OF TERMS AND CONDITIONS How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively convey the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA Also add information on how to contact you by electronic and paper mail. If the program is interactive, make it output a short notice like this when it starts in an interactive mode: Gnomovision version 69, Copyright (C) year name of author Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, the commands you use may be called something other than `show w' and `show c'; they could even be mouse-clicks or menu items--whatever suits your program. You should also get your employer (if you work as a programmer) or your school, if any, to sign a "copyright disclaimer" for the program, if necessary. Here is a sample; alter the names: Yoyodyne, Inc., hereby disclaims all copyright interest in the program `Gnomovision' (which makes passes at compilers) written by James Hacker. , 1 April 1989 Ty Coon, President of Vice This General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Library General Public License instead of this License. WordNet-Similarity-2.05/samples/0000755000175000017500000000000011025552366014737 5ustar sidsidWordNet-Similarity-2.05/samples/stoplist.txt0000644000175000017500000000240711015336623017357 0ustar sidsida aboard about above across after against all along alongside although amid amidst among amongst an and another anti any anybody anyone anything around as astride at aught bar barring because before behind below beneath beside besides between beyond both but by circa concerning considering despite down during each either enough everybody everyone except excepting excluding few fewer following for from he her hers herself him himself his hisself i idem if ilk in including inside into it its itself like many me mine minus more most myself naught near neither nobody none nor nothing notwithstanding of off on oneself onto opposite or other otherwise our ourself ourselves outside over own past pending per plus regarding round save self several she since so some somebody someone something somewhat such suchlike sundry than that the thee theirs them themselves there they thine this thou though through throughout thyself till to tother toward towards twain under underneath unless unlike until up upon us various versus via vis-a-vis we what whatall whatever whatsoever when whereas wherewith wherewithal which whichever whichsoever while who whoever whom whomever whomso whomsoever whose whosoever with within without worth ye yet yon yonder you you-all yours yourself yourselves WordNet-Similarity-2.05/samples/wn30compounds.txt0000644000175000017500000373724611015336623020240 0ustar sidsid's_gravenhage 11_november 12-tone_music 12-tone_system 14_july 15_august_1945 15_may_organization 15_minutes 16_pf 17_november 18-karat_gold 1_chronicles 1_esdras 1_kings 1_maccabees 1_samuel 1st-class_mail 1st_baron_beaverbrook 1st_baron_verulam 1st_class 1st_earl_attlee 1st_earl_baldwin_of_bewdley 1st_earl_of_balfour 1st_lieutenant 1st_viscount_montgomery_of_alamein 2-hydroxybenzoic_acid 2-methylpropenoic_acid 22-karat_gold 24-hour_interval 24-karat_gold 2_chronicles 2_esdras 2_kings 2_maccabees 2_samuel 2nd_lieutenant 30_minutes 365_days 366_days 3d_radar 3rd_october_organization 401-k_plan 440_yards 5-hydroxy-3-methylglutaryl-coenzyme_a_reductase 60_minutes 6_june_1944 880_yards 8_may_1945 a-scan_ultrasonography a._a._michelson a._a._milne a._conan_doyle a._e._burnside a._e._housman a._e._kennelly a._e._w._mason a._noam_chomsky a._testudineus a_battery a_capella_singing a_cappella_singing a_horizon a_kempis a_la_carte a_level aaland_islands aare_river aaron's_rod aaron_burr aaron_copland aaron_montgomery_ward aba_transit_number abandoned_infant abandoned_person abandoned_ship abasia_trepidans abatable_nuisance abatement_of_a_nuisance abbe_condenser abbott_lawrence_lowell abdominal_actinomycosis abdominal_aorta abdominal_aortic_aneurysm abdominal_breathing abdominal_cavity abdominal_delivery abdominal_external_oblique_muscle abdominal_muscle abdominal_nerve_plexus abdominal_pregnancy abdominal_wall abducens_muscle abducens_nerve abducent_nerve abductor_muscle abel_janszoon_tasman abel_tasman abelian_group abelmoschus_esculentus abelmoschus_moschatus aberdeen_angus abies_alba abies_amabilis abies_balsamea abies_bracteata abies_concolor abies_fraseri abies_grandis abies_lasiocarpa abies_lowiana abies_venusta ablative_absolute ablative_case able-bodied_seaman able_seaman abney_level abnormal_psychology abo_antibodies abo_blood_group_system abo_group abo_system abominable_snowman aboriginal_australian abortion-inducing_drug abortion_pill about_turn abrading_stone abraham's_bosom abraham_lincoln abraham_stoker abramis_brama abrasive_material abronia_elliptica abronia_fragrans abronia_latifolia abronia_maritima abronia_umbellata abronia_villosa abruptio_placentae abruptly-pinnate_leaf abruzzi_e_molise abscessed_tooth absence_seizure absence_without_leave absentee_ballot absentee_rate absinthe_oil absolute_alcohol absolute_ceiling absolute_frequency absolute_magnitude absolute_majority absolute_pitch absolute_scale absolute_space absolute_temperature absolute_threshold absolute_value absolute_viscosity absolute_zero absorbent_cotton absorbent_material absorption_band absorption_coefficient absorption_factor absorption_indicator absorption_spectrum absorption_unit abstract_art abstract_artist abstract_entity abstract_expressionism abstract_thought abu_ali_al-husain_ibn_abdallah_ibn_sina abu_dhabi abu_hafs_al-masri_brigades abu_nidal_organization abu_sayyaf abudefduf_saxatilis abukir_bay abul-walid_mohammed_ibn-ahmad_ibn-mohammed_ibn-roshd abutilon_theophrasti abutment_arch aby_moritz_warburg aby_warburg abyssal_zone abyssinian_banana abyssinian_cat acacia_auriculiformis acacia_cambegei acacia_catechu acacia_dealbata acacia_farnesiana acacia_melanoxylon acacia_pycnantha acacia_xanthophloea academic_administrator academic_costume academic_degree academic_department academic_freedom academic_gown academic_program academic_relation academic_requirement academic_robe academic_session academic_term academic_year academy_award academy_of_motion_picture_arts_and_sciences academy_of_television_arts_and_sciences acadia_national_park acalypha_virginica acanthisitta_chloris acanthocereus_pentagonus acanthocereus_tetragonus acanthocybium_solandri acanthophis_antarcticus acanthoscelides_obtectus acanthosis_nigricans acanthurus_chirurgus acanthus_family acanthus_mollis acapulco_de_juarez acapulco_gold acaroid_resin accaroid_resin acceleration_unit accelerator_factor accelerator_pedal accent_mark accentual_system acceptance_sampling acceptor_rna access_code access_road access_time accessory_after_the_fact accessory_before_the_fact accessory_cephalic_vein accessory_during_the_fact accessory_fruit accessory_hemiazygos_vein accessory_hemiazygous_vein accessory_nerve accessory_vertebral_vein accident_surgery accidental_injury accipiter_cooperii accipiter_gentilis accipiter_nisus accommodating_iol accommodating_lens_implant accommodation_endorser accommodation_ladder accommodation_reflex accompanying_vein accomplished_fact accord_and_satisfaction accordance_of_rights accordion_door account_book account_executive account_payable account_representative account_statement accounting_data accounting_entry accounting_firm accounting_principle accounting_standard accounting_system accounts_payable accounts_receivable accroides_gum accroides_resin accrual_basis accumulator_register accusative_case ace_inhibitor ace_of_clubs ace_of_diamonds ace_of_hearts ace_of_spades acellular_slime_mold acentric_chromosome acer_argutum acer_campestre acer_circinatum acer_glabrum acer_japonicum acer_macrophyllum acer_negundo acer_negundo_californicum acer_palmatum acer_pennsylvanicum acer_platanoides acer_pseudoplatanus acer_rubrum acer_saccharinum acer_saccharum acer_spicatum acerate_leaf acetate_disk acetate_rayon acetic_acid acetic_anhydride acetoacetic_acid acetone_body acetyl_chloride acetyl_group acetyl_radical 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carte_du_jour carter_administration cartesian_coordinate cartesian_coordinate_system cartesian_plane cartesian_product carthamus_tinctorius carthusian_order cartilage_bone cartilaginous_fish cartilaginous_structure cartilaginous_tube cartoon_strip cartridge_belt cartridge_brass cartridge_clip cartridge_ejector cartridge_extractor cartridge_font cartridge_fuse cartridge_holder cartridge_paper cartridge_remover carum_carvi carving_fork carving_knife cary_grant carya_aquatica carya_cordiformis carya_glabra carya_illinoensis carya_illinoinsis carya_laciniosa carya_myristicaeformis carya_myristiciformis carya_ovata carya_tomentosa caryocar_nuciferum caryophyllaceous_plant caryophylloid_dicot_family caryophylloid_dicot_genus caryota_urens casaba_melon casanova_de_seingalt cascade_everlasting cascade_liquefier cascade_mountains cascade_penstemon cascade_range cascade_transformer cascades_frog cascading_menu cascara_buckthorn cascara_sagrada cascarilla_bark case-fatality_proportion case-hardened_steel case-to-infection_proportion case-to-infection_ratio case_agreement case_history case_in_point case_knife case_law case_load case_officer case_shot case_study casein_glue casein_paint casemaking_clothes_moth casement_window casey_jones casey_stengel cash_account cash_advance cash_bar cash_basis cash_card cash_cow cash_crop cash_dispenser cash_equivalent cash_flow cash_in_hand cash_machine cash_price cash_register cash_surrender_value cashew_nut cashew_tree cashier's_check cashier's_cheque cashmere_goat casimir_funk casmerodius_albus caspar_bartholin caspase-mediated_cell_death caspian_sea cass_gilbert cassava_starch cassegrainian_telescope cassette_deck cassette_player cassette_recorder cassette_tape cassia-bark_tree cassia_acutifolia cassia_alata cassia_augustifolia cassia_auriculata cassia_bark cassia_fasciculata cassia_fistula cassia_grandis cassia_javonica cassia_marginata cassia_marilandica cassia_occidentalis cassia_roxburghii cassia_tora cassin's_kingbird cassiope_mertensiana cassius_clay cassius_longinus cassius_marcellus_clay cast-iron_plant cast_iron cast_of_characters castanea_chrysophylla castanea_crenata castanea_dentata castanea_mollissima castanea_ozarkensis castanea_pumila castanea_sativa castanopsis_chrysophylla caste_system caster_sugar castile_soap castilleja_chromosa castilleja_miniata castilleja_sessiliflora castilleja_sulphurea casting_lots casting_vote castle_in_spain castle_in_the_air castor-oil_plant castor_bean castor_bean_plant castor_canadensis castor_fiber castor_oil castor_sugar castration_anxiety casualty_care_research_center casuarina_equisetfolia casus_belli cat's_cradle cat's_eye cat's_feet cat's_foot cat_and_mouse cat_and_rat cat_bear cat_box cat_burglar cat_cracker cat_fancier cat_flea cat_food cat_rig cat_scan cat_scanner cat_scratch_disease cat_shark cat_sleep cat_squirrel cat_suit cat_thyme cat_valium catabatic_wind catalina_cherry catalina_island catalog_buying cataloged_procedure catalpa_bignioides catalpa_speciosa catalytic_converter catalytic_cracker catananche_caerulea cataract_canyon cataract_surgery catarrhal_fever catasetum_macrocarpum catastrophic_illness catatonic_schizophrenia catatonic_type_schizophrenia catch_crop catch_phrase catcher's_mask catchment_area catchment_basin catechetical_instruction categorical_imperative caterpillar_track caterpillar_tread catha_edulis catharacta_skua catharanthus_roseus cathartes_aura catherine_de_medicis catherine_howard catherine_i catherine_ii catherine_of_aragon catherine_parr catherine_the_great catherine_wheel cathode-ray_oscilloscope cathode-ray_tube cathode_ray catholic_church catholic_pope catholic_reaction_force catholic_school cationic_detergent catjang_pea catocala_nupta catoptrophorus_semipalmatus catskill_mountains catsup_bottle cattail_family cattail_millet cattle_boat cattle_breeding cattle_cake cattle_car cattle_drive cattle_egret cattle_farm cattle_grid cattle_guard cattle_pen cattle_plague cattle_ranch cattle_thief 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cell_organelle cell_theory cell_wall cellular_division cellular_inclusion cellular_phone cellular_respiration cellular_slime_mold cellular_telephone cellulose_acetate cellulose_ester cellulose_nitrate cellulose_tape cellulose_triacetate cellulose_xanthate celosia_argentea celosia_argentea_cristata celosia_cristata celsius_scale celtic_cross celtic_deity celtic_language celtis_australis celtis_laevigata celtis_occidentalis cembra_nut cembra_nut_tree cement_mixer cenchrus_ciliaris cenchrus_tribuloides cenozoic_era census_bureau census_taker centaurea_americana centaurea_cineraria centaurea_cyanus centaurea_gymnocarpa centaurea_imperialis centaurea_moschata centaurea_nigra centaurea_scabiosa centaurea_solstitialis centauria_calcitrapa centaurium_calycosum centaurium_minus centaurium_scilloides centennial_state center_bit center_field center_fielder center_for_disease_control_and_prevention center_line center_of_attention center_of_buoyancy center_of_curvature center_of_flotation 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chalice_vine chalk_dust chalk_line chalk_pit chalk_talk chamaea_fasciata chamaecrista_fasciculata chamaecyparis_lawsoniana chamaecyparis_nootkatensis chamaecyparis_thyoides chamaecytisus_palmensis chamaedaphne_calyculata chamaeleo_chamaeleon chamaeleo_oweni chamaemelum_nobilis chamber_music chamber_of_commerce chamber_orchestra chambered_nautilus chameleon_tree_frog chamfer_bit chamfer_plane chammy_leather chamois_cloth chamois_cress chamois_leather champagne_cup champagne_flute champion_lode champs_elysees chance-half_correlation chance_event chance_variable chancellor_of_the_exchequer chang_jiang chang_kuo chang_kuo-lao change-of-pace_ball change_of_color change_of_course change_of_direction change_of_integrity change_of_life change_of_location change_of_magnitude change_of_mind change_of_shape change_of_state change_ringing channel_bass channel_capacity channel_cat channel_catfish channel_island channel_islands_national_park channel_tunnel chanson_de_geste chao_phraya chaotic_attractor chaparral_broom chaparral_cock chaparral_mallow chaparral_pea chaparral_sage chapel_hill chapel_service characin_fish character-at-a-time_printer character_actor character_assassination character_printer character_reference character_set character_witness characterisic_function characteristic_curve characteristic_root_of_a_square_matrix charadrius_melodus charadrius_morinellus charadrius_vociferus charcoal_burner charcoal_gray charcoal_grey charcot-marie-tooth_disease chard_plant chardonnay_grape charge-exchange_accelerator charge_account charge_account_credit charge_card charge_d'affaires charge_of_quarters charge_per_unit charge_plate charge_sheet charge_unit chari_river charina_bottae chariot_race charitable_trust charity_case charity_shot charity_throw charity_toss charles's_law charles's_wain charles_a._lindbergh charles_andre_joseph_marie_de_gaulle charles_augustin_de_coulomb charles_augustus_lindbergh charles_baudelaire charles_bullfinch charles_camille_saint-saens charles_christopher_parker charles_cornwallis charles_dana_gibson charles_darwin charles_de_gaulle charles_dickens charles_digby_harrod charles_dillon_stengel charles_dodgson charles_dudley_warner charles_eames charles_edouard_jeanneret charles_edward_berry charles_edward_ives charles_evans_hughes charles_farrar_browne charles_follen_mckim charles_fourier charles_francis_hall charles_francois_gounod charles_franklin_kettering charles_frederick_menninger charles_frederick_worth charles_goodyear charles_grey charles_hard_townes charles_hardin_holley charles_henry_harrod charles_herbert_best charles_i charles_ii charles_ix charles_james_fox charles_john_huffam_dickens charles_joseph_clark charles_kay_ogden charles_kettering charles_l'enfant charles_lamb charles_laughton charles_lindbergh charles_liston charles_louis_de_secondat charles_louis_napoleon_bonaparte charles_lutwidge_dodgson charles_m._schulz charles_martin_hall charles_maurice_de_talleyrand charles_menninger charles_munroe_schulz charles_peirce charles_percy_snow charles_pierre_baudelaire charles_proteus_steinmetz charles_ringling charles_river charles_robert_darwin charles_robert_redford charles_sanders_peirce charles_schulz charles_stewart_parnell charles_stuart charles_taze_russell charles_the_bald charles_the_great charles_thomson_rees_wilson charles_townes charles_vii charles_watson-wentworth charles_wesley charles_wilkes charles_william_post charlestown_navy_yard charley_horse charlie_chaplin charlie_parker charlotte_anna_perkins_gilman charlotte_bronte charlotte_corday charlotte_russe charm_campaign charm_quark charnel_house charred_pancake_cup charronia_flavigula charter_member charter_school chartered_accountant chartres_cathedral chat_room chat_show chateura_pelagica chattahoochee_river chattel_mortgage chatter_mark chaulmoogra_oil chaulmoogra_tree chauna_torquata che_guevara cheap_money cheap_shot chechen_republic check-out_procedure check_bit check_character check_girl check_mark check_overdraft_credit check_register check_stub checker_board checkered_adder checkered_daffodil checkered_lily checkered_whiptail checking_account checking_program checkout_counter checkout_line checkout_time cheddar_cheese cheddar_pink cheek_muscle cheek_pouch cheese_cutter cheese_dip cheese_fondue cheese_pizza cheese_press cheese_rind cheese_sauce cheese_souffle cheese_spread cheese_tray chef's_salad cheilanthes_alabamensis cheilanthes_eatonii cheilanthes_gracillima cheilanthes_lanosa cheiranthus_allionii cheiranthus_asperus cheiranthus_cheiri chelate_compound chelidonium_majus chelifer_cancroides chelone_glabra chelonia_mydas chelonian_reptile chelydra_serpentina chem_lab chemical_action chemical_agent chemical_analysis chemical_attraction chemical_balance chemical_bomb chemical_bond chemical_chain chemical_change chemical_compound chemical_decomposition_reaction chemical_defence chemical_defense chemical_diabetes chemical_element chemical_energy chemical_engineering chemical_equilibrium chemical_formula chemical_group chemical_industry chemical_irritant chemical_mace chemical_mechanism chemical_notation chemical_operations chemical_phenomenon chemical_plant chemical_process chemical_property chemical_reaction chemical_reactor chemical_science chemical_substance chemical_terrorism chemical_warfare chemical_weapon chemical_weapons_convention chemin_de_fer chemist's_shop chemistry_department chemistry_lab chemistry_laboratory chen_caerulescens chen_n._yang chenille_cord chenin_blanc chenopodium_album chenopodium_ambrosioides chenopodium_bonus-henricus chenopodium_botrys chenopodium_capitatum chenopodium_glaucum chenopodium_hybridum chenopodium_murale chenopodium_rubrum chenopodium_vulvaria cheoplastic_metal chequing_account cherimoya_tree chernozemic_soil cherokee_rose cherry-tree_gum cherry_apple cherry_birch cherry_bomb cherry_crab cherry_laurel cherry_pepper cherry_plum cherry_red cherry_stone cherry_tomato cherry_tree cherrystone_clam chesapeake_bay chesapeake_bay_retriever cheshire_cat cheshire_cheese chess_board chess_club chess_game chess_master chess_match chess_move chess_opening chess_piece chess_player chess_set chest_cavity chest_of_drawers chest_pain chest_protector chest_register chest_tone chest_voice chester_a._arthur chester_alan_arthur chester_nimitz chester_william_nimitz chestnut-bark_disease chestnut_blight chestnut_canker chestnut_oak chestnut_tree cheval_glass chevalier_de_bayard chevalier_de_lamarck cheviot_hills chewing_gum chewing_out cheyne-stokes_respiration chiacoan_peccary chian_turpentine chiang_chung-cheng chiang_kai-shek chiasma_opticum chickasaw_plum chicken_and_rice chicken_breast chicken_broth chicken_cacciatora chicken_cacciatore chicken_casserole chicken_coop chicken_cordon_bleu chicken_drumstick chicken_farm chicken_feed chicken_hawk chicken_kiev chicken_leg chicken_little chicken_liver chicken_louse chicken_manure chicken_marengo chicken_mousse chicken_paprika chicken_paprikash chicken_provencale chicken_purloo chicken_roundworm chicken_run chicken_salad chicken_sandwich chicken_scratch chicken_snake chicken_soup chicken_stew chicken_stock chicken_taco chicken_tetrazzini chicken_wing chicken_wire chicken_yard chickpea_plant chickweed_phlox chicle_gum chicory_escarole chicory_plant chicory_root chief_assistant chief_constable chief_executive chief_executive_officer chief_financial_officer chief_joseph chief_justice chief_of_staff chief_of_state chief_operating_officer chief_petty_officer chief_secretary chiffon_cake chigger_flower chigoe_flea chihuahuan_desert chihuahuan_spotted_whiptail child's_body child's_game child's_play child's_room child_abuse child_care child_molester child_neglect child_pornography child_prodigy child_psychology child_support child_welfare_agency child_welfare_service childbed_fever childbirth-preparation_class childe_hassam chile_bonito chile_hazel chile_nut chile_pine chile_tarweed chilean_bonito chilean_cedar chilean_firebush chilean_flameflower chilean_hazelnut chilean_jasmine chilean_monetary_unit chilean_nut chilean_peso chilean_rimu chilean_strawberry chili_con_carne chili_dog chili_pepper chili_powder chili_sauce chili_vinegar chilli_pepper chills_and_fever chilomeniscus_cinctus chilopsis_linearis chimaera_monstrosa chimaphila_corymbosa chimaphila_umbellata chimney_bellflower chimney_breast chimney_corner chimney_plant chimney_swallow chimney_swift chimonanthus_praecox chin_music chin_rest chin_strap chin_wag chin_wagging china_aster china_cabinet china_clay china_closet china_fleece_vine china_grass china_jute china_pink china_rose china_stone china_tree chinaberry_tree chinch_bug chinchilla_laniger chinchilla_rat chinese_alligator chinese_angelica chinese_angelica_tree chinese_anise chinese_black_mushroom chinese_brown_sauce chinese_cabbage chinese_celery chinese_checkers chinese_chequers chinese_chestnut chinese_chive chinese_cinnamon chinese_cork_oak chinese_date chinese_deity 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football_stadium football_team football_tee footprint_evidence foramen_magnum foramen_of_monro forbidden_city forbidden_fruit force-feed_lubricating_system force_17 force_feed force_field force_majeure force_out force_per_unit_area force_play force_pump force_unit forced_feeding forced_landing forced_sale forceps_delivery forces_of_umar_al-mukhtar forcing_out ford_hermann_hueffer ford_madox_ford fore-and-aft_rig fore-and-aft_sail fore-and-aft_topsail fore_edge fore_plane fore_wing foregone_conclusion foreground_processing forehand_drive forehand_shot forehand_stroke foreign_agent foreign_aid foreign_bill foreign_correspondent foreign_country foreign_direct_investment foreign_draft foreign_exchange foreign_intelligence_service foreign_intelligence_surveillance_act foreign_intelligence_surveillance_court foreign_legion foreign_minister foreign_mission foreign_office foreign_policy foreign_service foreign_terrorist_organization forensic_medicine forensic_pathology forest_fire 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hindu_calendar_month hindu_deity hindu_kush hindu_kush_mountains hindu_numeral hinge_joint hinging_post hip_bath hip_boot hip_joint hip_pad hip_pocket hip_roof hip_socket hip_tile hippeastrum_puniceum hipped_roof hipped_tile hippo_regius hippobosca_equina hippocratic_oath hippocrepis_comosa hippodamia_convergens hippoglossoides_platessoides hippoglossus_hippoglossus hippoglossus_stenolepsis hippolyte_jean_giraudoux hippopotamus_amphibius hippotragus_niger hipsurus_caryi hiram_king_williams hiram_ulysses_grant hiram_williams hire_car hired_gun hired_hand hired_help hired_man hiring_freeze hiring_hall hirschsprung's_disease hirudo_medicinalis hirundo_nigricans hirundo_pyrrhonota hirundo_rustica hispanic_american hispid_pocket_mouse histamine_blocker histamine_headache histiocytic_leukaemia histiocytic_leukemia histocompatibility_complex historic_period historical_document historical_linguistics historical_paper historical_present historical_record historical_school history_department 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home-fried_potatoes home_appliance home_away_from_home home_banking home_base home_brew home_buyer home_computer home_counties home_court home_ec home_economics home_equity_credit home_equity_loan home_folk home_fries home_from_home home_front home_game home_ground home_guard home_help home_invasion home_key home_loan home_loan_bank home_movie home_office home_page home_plate home_port home_range home_reserve home_room home_rule home_run home_secretary home_stand home_study home_territory home_theater home_theatre home_truth homecourt_advantage homeland_security homeless_person homeobox_gene homeotic_gene homer_a._thompson homer_armstrong_thompson homer_thompson homestead_law homework_problem homing_device homing_pigeon homing_torpedo hominy_grits homo_erectus homo_habilis homo_heidelbergensis homo_rhodesiensis homo_sapiens homo_sapiens_neanderthalensis homo_sapiens_sapiens homo_soloensis homocercal_fin homogeneous_polynomial homogenized_milk homogentisic_acid homogyne_alpina 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hsuan_chiao huang_hai huang_he huayna_capac hub-and-spoke_system hub_of_the_universe hubbard_squash hubble's_constant hubble's_law hubble's_parameter hubble_constant hubble_law hubble_parameter huck_finn huckleberry_finn huckleberry_oak huddie_leadbetter hudson_bay hudson_bay_collared_lemming hudson_hoagland hudson_river hudson_river_school hudson_seal hudsonia_ericoides hudsonia_tomentosa hudsonian_godwit hue_and_cry hug_drug hugh_capet hugh_dowding hugo_alvar_henrik_aalto hugo_de_vries hugo_devries hugo_grotius hugo_junkers hugo_von_hoffmannsthal hugo_wolf hugueninia_tanacetifolia huig_de_groot huldreich_zwingli hulsea_algida hulsea_nana human_action human_activity human_being human_beings human_body human_botfly human_chorionic_gonadotrophin human_chorionic_gonadotropin human_death human_dynamo human_ecology human_elbow human_face human_foot human_gamma_globulin human_genome_project human_growth_hormone human_head human_immunodeficiency_virus human_knee human_language_technology 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hydnocarpus_laurifolia hydnocarpus_oil hydnocarpus_wightiana hydrangea_anomala hydrangea_arborescens hydrangea_family hydrangea_macrophylla_hortensis hydrangea_paniculata hydrangea_petiolaris hydrastis_canadensis hydrated_aluminium_oxide hydrated_aluminum_oxide hydrated_lime hydrated_oxide hydraulic_brake hydraulic_brake_cylinder hydraulic_brakes hydraulic_cement hydraulic_engineering hydraulic_press hydraulic_pump hydraulic_ram hydraulic_system hydraulic_transmission hydraulic_transmission_system hydrazo_group hydrazo_radical hydrazoic_acid hydrilla_verticillata hydriodic_acid hydrobates_pelagicus hydrobromic_acid hydrocharis_morsus-ranae hydrochloric_acid hydrochoerus_hydrochaeris hydrocyanic_acid hydrodamalis_gigas hydroelectric_turbine hydrofluoric_acid hydrofluosilicic_acid hydrogen_atom hydrogen_azide hydrogen_bomb hydrogen_bond hydrogen_bromide hydrogen_carbonate hydrogen_chloride hydrogen_cyanide hydrogen_fluoride hydrogen_iodide hydrogen_ion hydrogen_ion_concentration 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hyoscyamus_muticus hyoscyamus_niger hypentelium_nigricans hyper-eutectoid_steel hyperbaric_chamber hyperbilirubinemia_of_the_newborn hyperbolic_geometry hyperchromic_anaemia hyperchromic_anemia hyperemesis_gravidarum hyperfocal_distance hyperglyphe_perciformis hypericum_androsaemum hypericum_ascyron hypericum_calycinum hypericum_crux_andrae hypericum_gentianoides hypericum_hypericoides hypericum_maculatum hypericum_perforatum hypericum_prolificum hypericum_pyramidatum hypericum_spathulatum hypericum_tetrapterum hypericum_virginianum hyperkinetic_syndrome hypermedia_system hyperoodon_ampullatus hypersensitivity_reaction hypertext_mark-up_language hypertext_markup_language hypertext_system hypertext_transfer_protocol hypertrophic_cardiomyopathy hypertrophic_rosacea hyphantria_cunea hypnotic_trance hypo-eutectoid_steel hypochaeris_radicata hypochlorous_acid hypochromic_anaemia hypochromic_anemia hypodermic_needle hypodermic_syringe hypogastric_artery hypogastric_plexus hypogastric_vein hypoglossal_nerve hypoglycaemic_agent hypoglycemic_agent hyponitrous_acid hypophosphoric_acid hypophosphorous_acid hypophyseal_stalk hypophysial_dwarf hypoplastic_anaemia hypoplastic_anemia hypoplastic_dwarf hypostasis_of_christ hypothalamic_releasing_factor hypothalamic_releasing_hormone hypothetical_creature hypothetical_imperative hypovolemic_shock hypoxic_hypoxia hypoxis_hirsuta hypsiglena_torquata hypsiprymnodon_moschatus hyssop_loosestrife hyssop_oil hyssopus_officinalis hysterical_neurosis hysteron_proteron i._a._richards i._f._stone i._m._pei i_chronicles i_corinthians i_esdra i_john i_kings i_maccabees i_peter i_samuel i_thessalonians i_timothy ian_douglas_smith ian_fleming ian_lancaster_fleming ian_smith ian_wilmut iberian_peninsula ibis_ibis ibizan_hound ibizan_podenco ibn_al-haytham ibn_talal_hussein ibolium_privet ibota_privet ice-cream_bean ice-cream_cake ice-cream_cone ice-cream_float ice-cream_soda ice-cream_sundae ice-hockey_player ice-hockey_rink ice-skating_rink 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identity_operator identity_theft identity_verification ideological_barrier idesia_polycarpa idiom_neutral idiomatic_expression idiopathic_disease idiopathic_disorder idiopathic_hemochromatosis idiopathic_thrombocytopenic_purpura idiot_box idiot_light idiot_savant iditarod_trail iditarod_trail_dog_sled_race idle_pulley idle_talk idle_wheel idle_words idler_pulley idol_worship idol_worshiper idria_columnaris ieoh_ming_pei ignace_jan_paderewski ignace_paderewski ignatius_of_loyola igneous_rock ignis_fatuus ignition_coil ignition_interlock ignition_key ignition_lock ignition_switch ignition_system ignoratio_elenchi igor_fyodorovich_stravinsky igor_ivanovich_sikorsky igor_sikorsky igor_stravinsky igor_tamm igor_yevgeneevich_tamm iguana_iguana iguanid_lizard iguassu_falls iguazu_falls ii_chronicles ii_corinthians ii_esdras ii_john ii_kings ii_maccabees ii_peter ii_samuel ii_thessalonians ii_timothy iii_john ijssel_river il_duce ilama_tree ileal_artery ileocecal_valve ileocolic_artery ileocolic_vein iles_comores iles_marquises ilex_cornuta ilex_decidua ilex_glabra ilex_paraguariensis iliac_artery iliac_vein iliamna_acerifolia iliamna_remota iliamna_ruvularis ilich_ramirez_sanchez ilich_sanchez iliolumbar_artery iliolumbar_vein ill_fame ill_health ill_humor ill_humour ill_luck ill_nature ill_service ill_temper ill_turn ill_will illecebrum_verticullatum illegal_possession illegitimate_child illegitimate_enterprise illicium_anisatum illicium_floridanum illicium_verum illinois_river illiterate_person illumination_unit ilya_ehrenberg ilya_grigorievich_ehrenberg ilya_ilich_metchnikov image_breaker image_compression image_orthicon image_scanner imaginary_being imaginary_creature imaginary_number imaginary_part imaginary_part_of_a_complex_number imaginary_place imagination_image imaginative_comparison imamu_amiri_baraka imitation_leather imitative_electronic_deception immaculate_conception immaculate_conception_of_the_virgin_mary immanuel_kant immediate_allergy 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n-type_semiconductor n._y._stock_exchange nabalus_alba nabalus_serpentarius nabothian_cyst nabothian_follicle nabothian_gland nacreous_cloud nada_daiquiri nadine_gordimer naegele's_rule naemorhedus_goral nag_hammadi nag_hammadi_library nagami_kumquat nagari_script nageia_nagi naha_city naiad_family nail-tailed_kangaroo nail-tailed_wallaby nail_enamel nail_hole nail_polish nail_pulling nail_removal nail_varnish naive_art naive_realism naja_haje naja_hannah naja_naja naja_nigricollis naked_eye naked_lady naked_mole_rat naked_option nalidixic_acid name_and_address name_calling name_day name_dropper name_part name_tag namib_desert namoi_river nan_ling nan_river nancy_freeman_mitford nancy_mitford nancy_witcher_astor nand_circuit nand_gate nanda_devi nanga_parbat napaea_dioica naphthalene_poisoning napier's_bones napier's_rods napierian_logarithm napkin_ring naples_garlic napoleon_bonaparte napoleon_i napoleon_iii napoleonic_wars naproxen_sodium narcissistic_personality narcissus_jonquilla narcissus_papyraceus narcissus_pseudonarcissus narcotic_antagonist narcotics_agent nares_deep narragansett_bay narrow-body_aircraft narrow-leaf_cattail narrow-leaf_penstemon narrow-leaved_bottletree narrow-leaved_everlasting_pea narrow-leaved_flame_flower narrow-leaved_plantain narrow-leaved_reedmace narrow-leaved_spleenwort narrow-leaved_strap_fern narrow-leaved_water_plantain narrow-leaved_white-topped_aster narrow_beech_fern narrow_boat narrow_escape narrow_gauge narrow_goldenrod narrow_margin narrow_wale narrowbody_aircraft narrowhead_morel narthecium_americanum narthecium_ossifragum nasal_bone nasal_canthus nasal_cavity nasal_concha nasal_consonant nasal_decongestant nasal_meatus nasal_septum nasal_sinus nasal_twang nasalis_larvatus nash_equilibrium nasofrontal_vein nasogastric_feeding nasolacrimal_duct nasopharyngeal_leishmaniasis nasotracheal_tube nasturtium_amphibium nasturtium_family nasturtium_officinale nasua_narica nat_turner natal_day natal_plum natalie_wood nathan_bailey nathan_birnbaum nathan_hale nathaniel_bailey nathaniel_bowditch nathaniel_currier nathaniel_hawthorne nation_of_islam national_academy_of_sciences national_aeronautics_and_space_administration national_anthem national_archives_and_records_administration national_assistance national_association_of_realtors national_association_of_securities_dealers_automated_quotations national_bank national_baseball_hall_of_fame national_capital national_censorship national_climatic_data_center national_debt national_debt_ceiling national_flag national_geospatial-intelligence_agency national_guard national_guard_bureau national_holiday national_income national_institute_of_justice national_institute_of_standards_and_technology national_institutes_of_health national_insurance national_intelligence_community national_labor_relations_board national_leader national_liberation_army national_liberation_front_of_corsica national_library_of_medicine national_monument national_oceanic_and_atmospheric_administration national_park national_park_service national_reconnaissance_office national_rifle_association national_science_foundation national_security_agency national_security_council national_service national_socialism national_socialist_german_workers'_party national_technical_information_service national_trading_policy national_trust national_volunteers_association national_weather_service nationalist_china nationalist_leader native_alaskan native_american native_australian native_bear native_beech native_cat native_cranberry native_fuchsia native_hawaiian native_holly native_land native_language native_orange native_peach native_pear native_pomegranate native_speaker native_sulfur native_sulphur natrix_maura natrix_natrix natrix_sipedon natural_ability natural_action natural_childbirth natural_covering natural_depression natural_elevation natural_enclosure natural_endowment natural_event natural_family_planning natural_fiber natural_fibre natural_gas natural_glass natural_history natural_immunity natural_language natural_language_processing natural_language_processing_application natural_language_processor natural_law natural_logarithm natural_number natural_object natural_order natural_phenomenon natural_philosophy natural_process natural_resin natural_resource natural_resources natural_rubber natural_science natural_scientist natural_selection natural_shape natural_spring natural_state natural_theology natural_virtue nature_study nature_worship nauclea_diderrichii naucrates_ductor nauru_island naut_mi nautch_dance nautch_girl nautical_chain nautical_linear_unit nautical_mile nautical_signal_flag naval_academy naval_air_warfare_center_weapons_division naval_attache naval_battle naval_blockade naval_brass naval_campaign naval_chart naval_commander naval_division naval_engineer naval_engineering naval_equipment naval_forces naval_gun naval_installation naval_missile naval_officer naval_radar naval_research_laboratory naval_shipyard naval_special_warfare naval_surface_warfare_center naval_tactical_data_system naval_underwater_warfare_center naval_unit naval_weaponry navel_orange navel_point navigation_light navigational_chart navigational_instrument navigational_system navy_base navy_bean navy_blue navy_cross navy_department navy_man navy_seal navy_secretary navy_yard nazi_germany nazi_party ne_plus_ultra neandertal_man neanderthal_man neap_tide neapolitan_ice_cream near-death_experience near_beer near_east near_gale near_miss near_thing near_vision neat's-foot_oil nebraska_fern nebuchadnezzar_ii nebuchadrezzar_ii nebular_hypothesis neck_bone neck_brace neck_exercise neck_of_the_woods neck_opening neck_ruff neck_sweetbread neckar_river necklace_poplar necklace_tree necrobiosis_lipoidica necrobiosis_lipoidica_diabeticorum necrotizing_enteritis necrotizing_enterocolitis nectarine_tree necturus_maculosus needle_bearing needle_biopsy needle_blight needle_bush needle_cast needle_furze needle_palm needle_rush needle_spike_rush needle_wood needlenose_pliers needlepoint_embroidery neem_cake neem_seed neem_tree neftali_ricardo_reyes negaprion_brevirostris negara_brunei_darussalam negative_charge negative_chemotaxis negative_correlation negative_feedback negative_feedback_circuit negative_identification negative_magnetic_pole negative_muon negative_pole negative_reinforcer negative_reinforcing_stimulus negative_stimulation negative_stimulus negev_desert neglect_of_duty negotiable_instrument negro_peach negro_pepper negro_race negro_spiritual negro_vine negroid_race nei_monggol neil_armstrong neil_simon neisseria_gonorrhoeae nell_gwynn nell_gwynne nell_gywn nellie_bly nellie_ross nellie_tayloe_ross nelson_algren nelson_mandela nelson_rolihlahla_mandela nelumbo_lutea nelumbo_nucifera nematode_worm nemean_games nemean_lion nemophila_aurita nemophila_maculata nemophila_menziesii neo_jazz neoceratodus_forsteri neofiber_alleni neohygrophorus_angelesianus neolentinus_ponderosus neolithic_age neomys_anomalus neomys_fodiens neon_induction_lamp neon_lamp neon_tube neonatal_death neonatal_hyperbilirubinemia neonatal_intensive_care_unit neonatal_mortality neonatal_mortality_rate neonatal_period neophron_percnopterus neoplastic_cell neosho_river neotoma_cinerea neotoma_floridana neotoma_fuscipes nepal_trumpet_flower nepalese_monetary_unit nepalese_rupee nepeta_cataria nepeta_hederaceae nephelium_lappaceum nephelium_litchi nephelium_longana nephelium_mutabile nephrogenic_diabetes_insipidus nephrolepis_exaltata nephrolepis_exaltata_bostoniensis nephrolepis_pectinata nephrops_norvegicus nephrotic_syndrome nephthytis_afzelii nerita_peloronta neritic_zone neritid_gastropod nerium_oleander nero's_crown nero_claudius_caesar_drusus_germanicus nerodia_sipedon neroli_oil nerve_agent nerve_block_anaesthesia nerve_block_anesthesia nerve_cell nerve_center nerve_centre nerve_compression nerve_deafness nerve_end nerve_ending nerve_entrapment nerve_fiber nerve_fibre nerve_gas nerve_growth_factor nerve_impulse nerve_pathway nerve_plexus nerve_tissue nerve_tract nervii_olfactorii nervous_breakdown nervous_disorder nervous_exhaustion nervous_impulse nervous_prostration nervous_strain nervous_system nervous_tissue nervus_abducens nervus_accessorius nervus_coccygeus nervus_facialis nervus_femoralis nervus_glossopharyngeus nervus_hypoglosus nervus_ischiadicus nervus_oculomotorius nervus_opticus nervus_phrenicus nervus_radialis nervus_saphenus nervus_spinalis nervus_trigeminus nervus_ulnaris nervus_vagus nervus_vestibulocochlearis nesselrode_pudding nest_egg nesting_place nestor_notabilis nestor_paz_zamora_commission nestorian_church net_ball net_estate net_income net_melon net_profit net_sales net_ton nether_region netherlands_antilles netherlands_guiana netted_melon nettle-leaved_bellflower nettle-leaved_goosefoot nettle_family nettle_rash nettle_tree nettleleaf_goosefoot network_architecture network_army network_programming network_topology neural_arch neural_impulse neural_net neural_network neural_structure neural_tube neurogenic_bladder neuroglial_cell neuroleptic_agent neuroleptic_drug neurological_disease neurological_disorder neuromuscular_blocking_agent neuromuscular_junction neuropterous_insect neurotic_depression neurotrichus_gibbsii neutral_spirits neutralisation_reaction neutralization_fire neutralization_reaction neutron_bomb neutron_flux neutron_radiation neutron_star neva_river nevado_de_colima never-never_land nevil_shute nevil_shute_norway neville_chamberlain nevoid_elephantiasis nevus_flammeus new_amsterdam new_ballgame new_britain new_brunswick new_caledonia new_caledonian_pine new_caledonian_yew new_criticism new_deal new_dealer new_delhi new_edition new_england new_england_aster new_england_boiled_dinner new_england_clam_chowder new_englander new_english_bible new_flower new_forest new_greek new_guinea new_hampshire new_hampshirite new_haven new_hebrides new_ireland new_jazz new_jersey new_jerseyan new_jerseyite new_latin new_line new_london new_look new_mexican new_mexico new_moon new_netherland new_norwegian new_orleans new_penny new_people's_army new_phase_of_the_moon new_river new_river_gorge_bridge new_scotland_yard new_siberian_islands new_south_wales new_stone_age new_style_calendar new_testament new_town new_wave new_waver new_world new_world_anteater new_world_beaver new_world_blackbird new_world_chat new_world_coral_snake new_world_flycatcher new_world_goldfinch new_world_jay new_world_least_weasel new_world_leishmaniasis new_world_monkey new_world_mouse new_world_opah new_world_oriole new_world_porcupine new_world_sparrow new_world_tapir new_world_vulture new_world_warbler new_year new_year's new_year's_day new_year's_eve new_york new_york_aster new_york_bay new_york_city new_york_fern new_york_minute new_york_state new_york_state_barge_canal new_york_stock_exchange new_york_strip new_yorker new_zealand new_zealand_beech new_zealand_cotton new_zealand_dacryberry new_zealand_daisybush new_zealand_dollar new_zealand_honeysuckle new_zealand_islands new_zealand_mountain_pine new_zealand_spinach new_zealand_white_pine new_zealand_wine_berry new_zealand_wren new_zealander newborn_baby newborn_infant newburg_sauce newcastle_disease newel_post newfoundland_and_labrador newfoundland_dog newfoundland_dwarf_birch newport_news news_agency news_article news_bulletin news_conference news_event news_item news_leak news_magazine news_media news_organisation news_organization news_photography news_program news_reader news_report news_show news_story newspaper_ad newspaper_advertisement newspaper_article newspaper_clipping newspaper_column newspaper_columnist newspaper_critic newspaper_editor newspaper_headline newspaper_publisher newsstand_operator newton's_first_law newton's_first_law_of_motion newton's_law newton's_law_of_gravitation newton's_law_of_motion newton's_second_law newton's_second_law_of_motion newton's_theory_of_gravitation newton's_third_law newton's_third_law_of_motion newtonian_mechanics newtonian_reflector newtonian_telescope newtown_wonder next_friend next_of_kin nez_perce ngaio_marsh nguyen_tat_thanh ni-hard_iron ni-resist_iron niagara_falls niagara_river nicandra_physaloides nicaraguan_capital nicaraguan_monetary_unit niccolo_machiavelli niccolo_paganini nicene_creed nicholas_i nicholas_ii nicholas_v nicholas_vachel_lindsay nickel-base_alloy nickel-cadmium_accumulator nickel-iron_accumulator nickel-iron_battery nickel_alloy nickel_bronze nickel_note nickel_silver nickel_steel nicker_nut nicker_seed nicol_prism nicola_amati nicola_sacco nicolas_de_malebranche nicolas_leonard_sadi_carnot nicolas_poussin nicolaus_copernicus nicolo_amati nicotiana_alata nicotiana_glauca nicotiana_rustica nicotiana_tabacum nicotinamide_adenine_dinucleotide nicotinamide_adenine_dinucleotide_phosphate nicotine_addiction nicotine_poisoning nicotinic_acid nictitating_membrane niels_abel niels_bohr niels_henrik_abel niels_henrik_david_bohr niemann-pick_disease nierembergia_frutescens nierembergia_repens nierembergia_rivularis nigella_damascena nigella_hispanica nigella_sativa niger-kordofanian_language niger_franc niger_river nigerian_capital nigerian_monetary_unit night-blooming_cereus night_bell night_bird night_blindness night_club night_court night_game night_heron night_jasmine night_jessamine night_latch night_letter night_life night_lizard night_owl night_porter night_raven night_rider night_school night_shift night_snake night_soil night_terror night_vision night_watch night_watchman nigroporus_vinosus nihau_island nihil_obstat nihilistic_delusion nikita_khrushchev nikita_sergeyevich_khrushchev nikola_tesla nikolaas_tinbergen nikolai_andreyevich_rimski-korsakov nikolai_andreyevich_rimsky-korsakov nikolai_ivanovich_bukharin nikolai_ivanovich_lobachevsky nikolai_lenin nikolai_vasilievich_gogol nile_crocodile nile_river nilgiri_hills nilo-saharan_language nilotic_language nim_tree nimble_will nimbus_cloud nina_from_carolina nine-banded_armadillo nine_iron ninepin_ball nineteenth_amendment ninth_cranial_nerve ninth_of_ab ninth_of_av niobrara_river nip_and_tuck nipa_fruticans nipa_palm nipple_shield nissen_hut nitrate_bacterium nitric_acid nitric_bacteria nitric_bacterium nitric_oxide nitrite_bacterium nitro_group nitrogen_balance nitrogen_cycle nitrogen_dioxide nitrogen_fixation nitrogen_mustard nitrogen_narcosis nitrogen_oxide nitrogen_trichloride nitrohydrochloric_acid nitrous_acid nitrous_bacteria nitrous_bacterium nitrous_oxide nitta_tree nizhni_novgorod nizhnyi_novgorod no-go_area no-hit_game no-par-value_stock no-par_stock no-parking_zone no-win_situation no_ball no_fault_automobile_insurance no_fault_insurance no_man's_land noachian_deluge noah's_flood noah_and_the_flood noah_webster noam_chomsky nob_hill nobel_laureate nobel_prize noble_cane noble_gas noble_metal noblesse_oblige noctiluca_miliaris noctuid_moth nocturnal_emission nodal_rhythm nodding_groundsel nodding_onion nodding_thistle nodding_wild_onion nodes_of_ranvier noel_coward noise_conditions noise_level noise_pollution nol_pros nolina_microcarpa nolle_prosequi nolo_contendere nom_de_guerre nom_de_plume nomia_melanderi nominal_aphasia nominal_damages nominal_head nominal_phrase nominal_value nominating_address nominating_speech nominative_case non-cash_expense non-christian_priest non-dedicated_file_server non-euclidean_geometry non-flowering_plant non-insulin-dependent_diabetes non-insulin-dependent_diabetes_mellitus non-ionic_detergent non-market_economy non-nucleoside_reverse_transcriptase_inhibitor non-standard_speech non-volatile_storage non_pros non_prosequitur non_sequitur non_vult nonbiodegradable_pollution noncallable_bond nonce_word noncoding_dna noncommissioned_officer nondepository_financial_institution nondirectional_antenna nondiscretionary_trust nonesuch_clover nonfat_dry_milk nonfictional_prose nonflowering_plant nonfluent_aphasia nongonococcal_urethritis nongovernmental_organization nonlinear_correlation nonlinear_distortion nonlinear_system nonmalignant_neoplasm nonmalignant_tumor nonmalignant_tumour nonparametric_statistic nonparametric_statistics nonparticulate_radiation nonpasserine_bird nonprofit_organization nonproliferation_center nonrapid_eye_movement nonrapid_eye_movement_sleep nonreligious_person nonrestrictive_clause nonsense_verse nonsingular_matrix nonsmoking_car nonsocial_infection nonsolid_color nonsolid_colour nonspecific_urethritis nonsteroidal_anti-inflammatory nonsteroidal_anti-inflammatory_drug nonstop_flight nontextual_matter nonthrombocytopenic_purpura nontricyclic_antidepressant nontricyclic_antidepressant_drug nontricyclic_drug nonvascular_organism nonvascular_plant nonverbal_intelligence nonviolent_resistance nonvolatile_storage nook_and_cranny nooks_and_crannies noonan's_syndrome nootka_cypress norbert_wiener norethindrone_acetate norfolk_island norfolk_island_pine norfolk_jacket norfolk_terrier norfolk_wherry norma_jean_baker normal_curve normal_distribution normal_dwarf normal_fault normal_school normal_tension_glaucoma norman_architecture norman_conquest norman_french norman_jewison norman_mailer norman_mattoon_thomas norman_rockwell norman_thomas norse_deity norse_mythology north-seeking_pole north-south_direction north_africa north_america north_american north_american_country north_american_free_trade_agreement north_american_nation north_atlantic north_atlantic_council north_atlantic_treaty north_atlantic_treaty_organization north_borneo north_by_east north_by_west north_carolina north_carolinian north_cascades_national_park north_celestial_pole north_channel north_dakota north_dakotan north_dravidian north_equatorial_current north_frigid_zone north_germanic north_germanic_language north_island north_island_edelweiss north_korea north_korean north_korean_monetary_unit north_korean_won north_northeast north_northwest north_pacific north_peak north_platte north_platte_river north_pole north_sea north_side north_star north_star_state north_temperate_zone north_vietnam north_wind north_yorkshire northeast_by_east northeast_by_north northeastern_united_states northern_alliance northern_baptist northern_baptist_convention northern_barramundi northern_bedstraw northern_beech_fern northern_bobwhite northern_bog_lemming northern_casque-headed_frog northern_cricket_frog northern_cross northern_dewberry northern_dune_tansy northern_europe northern_flying_squirrel northern_harrier northern_hemisphere northern_holly_fern northern_ireland northern_jacob's_ladder northern_lights northern_lobster northern_mammoth northern_mariana_islands northern_marianas northern_oak_fern northern_oriole northern_parula northern_phalarope northern_pike northern_pin_oak northern_pitch_pine northern_pocket_gopher northern_porgy northern_red_oak northern_rhodesia northern_scup northern_sea_robin northern_shrike northern_snakehead northern_snow_bedstraw northern_spy northern_storm_petrel northern_territory northern_white_cedar northern_whiting northern_woodsia northrop_frye northwest_by_north northwest_by_west northwest_passage northwest_territories northwest_wind northwestern_united_states norway_lobster norway_maple norway_rat norway_spruce norwegian_elkhound norwegian_krone norwegian_lobster norwegian_monetary_unit norwegian_sea norwich_terrier nose_candy nose_cone nose_count nose_dive nose_drops nose_flute nose_job nose_ring nota_bene notary_public notational_system note_of_hand note_payable note_receivable note_value notebook_computer notebook_entry notechis_scutatus notemigonus_crysoleucas nothofagus_cuninghamii nothofagus_dombeyi nothofagus_menziesii nothofagus_obliqua nothofagus_procera nothofagus_solanderi nothofagus_truncata notice_board notions_counter notonecta_undulata notophthalmus_viridescens notornis_mantelli notoryctus_typhlops notropis_atherinoides notropis_cornutus nougat_bar noughts_and_crosses noun_phrase nouvelle_cuisine nouvelle_vague nova_lisboa nova_lox nova_salmon nova_scotia nova_scotia_lox nova_scotia_salmon nova_scotian nova_style_salmon nova_zembla novaya_zemlya novelty_shop november_1 november_11 november_2 november_5 nrem_sleep nubian_desert nucifraga_caryocatactes nucifraga_columbiana nuclear-powered_ship nuclear-powered_submarine nuclear_cataract nuclear_chemist nuclear_chemistry nuclear_club nuclear_deterrence nuclear_energy nuclear_engineering nuclear_explosion nuclear_family nuclear_fission nuclear_fuel nuclear_fusion nuclear_fusion_reaction nuclear_magnetic_resonance nuclear_medicine nuclear_meltdown nuclear_physicist nuclear_physics nuclear_power nuclear_propulsion nuclear_reaction nuclear_reactor nuclear_regulatory_commission nuclear_resonance nuclear_rna nuclear_rocket nuclear_submarine nuclear_terrorism nuclear_transplantation nuclear_warhead nuclear_weapon nuclear_winter nucleic_acid nucleolar_organiser nucleolar_organizer nucleolus_organiser nucleolus_organizer nucleon_number nucleoside_reverse_transcriptase_inhibitor nucleus_niger nude_dancing nude_mouse nude_painting nude_person nude_sculpture nude_statue nuisance_abatement nuisance_tax nuisance_value null_set null_space nullarbor_plain number_1 number_agreement number_cruncher number_crunching number_one number_one_wood number_representation_system number_system number_theorist numbers_game numbers_pool numbers_racket numdah_rug numenius_arquata numenius_borealis numeration_system numerical_analysis numerical_quantity numerical_value numida_meleagris nun's_habit nun_buoy nunc_dimittis nuphar_advena nuphar_lutea nuphar_sagittifolium nurse's_aide nurse-patient_relation nurse_clinician nurse_log nurse_practitioner nurse_shark nursery_rhyme nursery_school nursing_aide nursing_bottle nursing_care nursing_home nursing_school nusa_tenggara nut-leaved_screw_tree nut_and_bolt nut_bar nut_bread nut_butter nut_case nut_grass nut_house nut_pine nut_sedge nut_tree nutmeg_family nutmeg_flower nutmeg_geranium nutmeg_hickory nutmeg_melon nutmeg_state nutmeg_tree nutrient_agar nutrient_artery nutritionist's_calorie nuts_and_bolts nuttall's_oak nuttall_oak nux_vomica nuytsia_floribunda nyctaginia_capitata nyctanassa_violacea nyctereutes_procyonides nycticebus_pygmaeus nycticebus_tardigradua nycticorax_nycticorax nylon_stocking nymphaea_alba nymphaea_caerulea nymphaea_lotus nymphaea_odorata nymphaea_stellata nymphalid_butterfly nymphalis_antiopa nymphicus_hollandicus nyquist_frequency nyquist_rate nyssa_aquatica nyssa_sylvatica o._henry o_level o_ring oahu_island oak-leaved_goosefoot oak_apple oak_blight oak_chestnut oak_fern oak_leaf_cluster oak_tree oakleaf_goosefoot oast_house oat_cell_carcinoma oatmeal_cookie oaxaca_de_juarez ob_river obedience_plant obedient_plant obesity_diet obidoxime_chloride obiter_dictum object-oriented_database object-oriented_database_management_system object-oriented_programing object-oriented_programing_language object-oriented_programming object-oriented_programming_language object_ball object_code object_glass object_language object_lens object_lesson object_of_a_preposition object_of_the_verb object_program object_recognition objective_case objective_lens objet_d'art oblanceolate_leaf obligate_anaerobe oblique_angle oblique_bandage oblique_case oblique_triangle oblique_vein_of_the_left_atrium oblong_leaf oblong_woodsia oboe_d'amore oboe_da_caccia obovate_leaf observation_dome observation_post observation_station observation_tower observed_fire observer's_meridian obsessive-compulsive_disorder obsessive-compulsive_personality obstacle_race obstetrical_delivery obstetrical_toad obstruction_of_justice obstructive_shock obturator_vein obtuse-angled_triangle obtuse_angle obtuse_leaf obtuse_triangle occam's_razor occipital_bone occipital_cortex occipital_gyrus occipital_lobe occipital_protuberance occipital_vein occipitomastoid_suture occluded_front occult_arts occupancy_rate occupation_licence occupation_license occupational_disease occupational_group occupational_hazard occupational_safety_and_health_act occupational_safety_and_health_administration occupational_therapy ocean_bottom ocean_current ocean_floor ocean_liner ocean_perch ocean_pout ocean_state ocean_sunfish ocean_trip oceanic_abyss oceanic_bird oceanic_bonito oceanic_whitetip_shark oceanites_oceanicus ocellated_turkey ochna_family ochna_serrulata ochotona_collaris ochotona_princeps ochroma_lagopus ocimum_basilicum ockham's_razor oconee_bells octadecanoic_acid octal_digit octal_notation octal_number_system octal_numeration_system octane_number octane_rating octanedioic_acid october_12 october_24 october_revolution ocular_muscle oculomotor_nerve oculopharyngeal_muscular_dystrophy oculus_dexter oculus_sinister ocyurus_chrysurus odd-even_check odd-job_man odd-leg_caliper odd-pinnate_leaf odd-toed_ungulate odd_fellow odd_fish odd_hassel odd_man_out oddone_colonna odds_and_ends oder_river odo_of_lagery odobenus_divergens odobenus_rosmarus odocoileus_hemionus odocoileus_hemionus_columbianus odocoileus_virginianus odontaspis_taurus odontoid_process oecanthus_fultoni oedipal_complex oedipus_complex oedipus_rex oeil_de_boeuf oenanthe_aquatica oenanthe_crocata oenothera_biennis oenothera_fruticosa oenothera_macrocarpa oesophageal_reflux oesophageal_veins oesophagogastric_junction oestrus_ovis off-axis_reflector off-line_equipment off-line_operation off-speed_pitch off_year offensive_activity offer_price office_block office_boy office_building office_furniture office_of_inspector_general office_of_intelligence_support office_of_management_and_budget office_of_naval_intelligence office_of_the_dead office_staff officer's_mess official_document official_emissary official_immunity offset_lithography offset_printing offsetting_balance offshore_rig ogden_nash ogee_arch ohio_buckeye ohio_goldenrod ohio_river ohio_state_university ohm's_law ohmic_resistance oil-hardened_steel oil-industry_analyst oil-rich_seed oil-water_interface oil_beetle oil_burner oil_business oil_cake oil_cartel oil_change oil_color oil_colour oil_company oil_conservation oil_filter oil_furnace oil_future oil_geologist oil_gland oil_heater oil_industry oil_lamp oil_meal oil_nut oil_of_cloves oil_of_turpentine oil_of_vitriol oil_of_wintergreen oil_paint oil_painter oil_painting oil_palm oil_pipeline oil_pressure oil_production oil_pump oil_refinery oil_rig oil_rigger oil_shale oil_slick oil_stain oil_tanker oil_tycoon oil_well ojos_del_salado okapia_johnstoni okeechobee_waterway okefenokee_swamp okinawa_campaign oklahoma_city okra_plant olaf_ii olav_ii old-age_insurance old-age_pension old-age_pensioner old-field_toadflax old-maid's_bonnet old_age old_bag old_bailey old_boy old_boy_network old_bulgarian old_bullion old_catholic old_catholic_church old_church_slavic old_church_slavonic old_codger old_colony old_country old_delhi old_dominion old_dominion_state old_english old_english_sheepdog old_faithful old_fashioned old_french old_frisian old_geezer old_glory old_gold old_growth old_guard old_hand old_hickory old_high_german old_icelandic old_irish old_ironsides old_italian old_lady old_latin old_line_state old_maid old_maid_flower old_man old_man's_beard old_man_of_the_mountain old_master old_money old_nick old_norse old_north_french old_north_state old_person old_prussian old_rose old_salt old_saxon old_school old_school_tie old_sledge old_south old_squaw old_stager old_style old_style_calendar old_style_font old_testament old_times old_witch_grass old_witchgrass old_wives'_tale old_woman old_world old_world_beaver old_world_buffalo old_world_chat old_world_coot old_world_coral_snake old_world_crayfish old_world_flycatcher old_world_hop_hornbeam old_world_jay old_world_least_weasel old_world_leishmaniasis old_world_mistletoe old_world_monkey old_world_oriole old_world_porcupine old_world_quail old_world_rabbit old_world_robin old_world_scops_owl old_world_vulture old_world_warbler old_world_white_pelican old_world_yew olduvai_gorge olea_cunninghamii olea_europaea olea_lanceolata oleander_fern oleandra_mollis oleandra_neriiformis olearia_argophylla olearia_haastii oleaster_family olecranon_process oleic_acid oleo_oil oleoresin_capiscum olfactory_brain olfactory_bulb olfactory_impairment olfactory_modality olfactory_nerve olfactory_organ olfactory_perception olfactory_property olfactory_sensation olfersia_cervina olga_korbut oligocene_epoch oligochaete_worm oligoporus_leucospongia olive-drab_uniform olive-tree_agaric olive_branch olive_brown olive_drab olive_family olive_green olive_oil olive_ridley olive_tree oliver_cromwell oliver_ellsworth oliver_goldsmith oliver_hardy oliver_hazard_perry oliver_heaviside oliver_stone oliver_wendell_holmes oliver_wendell_holmes_jr. olla_podrida olympian_games olympian_zeus olympic_games olympic_god olympic_national_park olympic_salamander omani_monetary_unit omani_rial omar_bradley omar_khayyam omar_nelson_bradley omega-3_fatty_acid omega-6_fatty_acid omega_centauri omelet_pan omelette_pan omental_bursa omnidirectional_antenna omnidirectional_radio_range omnidirectional_range omomyid_group omphalotus_illudens on-line_database on-off_switch on/off_switch on_the_qui_vive on_the_road on_tour oncidium_papilio oncidium_papilio_kramerianum oncorhynchus_keta oncorhynchus_kisutch oncorhynchus_nerka oncorhynchus_tshawytscha ondatra_zibethica one-armed_bandit one-dimensional_language one-flowered_pyrola one-flowered_wintergreen one-man_rule one-night_stand one-way_light_time one-way_street one_c one_dollar_bill one_iron one_million_million one_million_million_million one_of_the_boys one_percent one_shot one_thousand one_thousand_million one_thousand_thousand onion_bagel onion_bread onion_butter onion_dome onion_louse onion_mildew onion_plant onion_roll onion_salt onion_smut onion_stem onion_thrips onion_yellow-dwarf_virus onion_yellow_dwarf onobrychis_viciaefolia onobrychis_viciifolia onoclea_sensibilis onoclea_struthiopteris ononis_repens ononis_spinosa onopordon_acanthium onopordum_acanthium onward_motion onyx_marble oom_paul_kruger oort_cloud ooze_leather op_art opal_glass opaque_gem open-air_market open-air_marketplace open-angle_glaucoma open-class_word open-door_policy open-end_credit open-end_fund open-end_investment_company open-end_wrench open-face_sandwich open-heart_surgery open-hearth_furnace open-hearth_process open_account open_air open_chain open_circuit open_door open_fireplace open_fracture open_frame open_house open_interval open_letter open_marriage open_order open_primary open_sandwich open_secret open_sesame open_shop open_sight open_society open_university open_weave opencast_mining opening_line opening_move opening_night openside_plane opera_bouffe opera_cloak opera_comique opera_company opera_glasses opera_hat opera_hood opera_house opera_star operant_conditioning operatic_star operating_budget operating_capability operating_capital operating_cost operating_expense operating_instructions operating_microscope operating_procedure operating_room operating_statement operating_surgeon operating_system operating_table operating_theater operating_theatre operation_code operation_desert_storm operational_casualty operational_cell operational_damage operations_research operative_field operator_gene opheodrys_aestivus opheodrys_vernalis ophiodon_elongatus ophioglossum_pendulum ophiophagus_hannah ophrys_apifera ophrys_insectifera ophrys_muscifera ophrys_sphegodes ophthalmia_neonatorum ophthalmic_artery ophthalmic_vein opinion_poll opisthocomus_hoazin opium_addict opium_den opium_poppy opium_taker opossum_rat opossum_shrimp opossum_wood opportunistic_infection opportunity_cost opposite_number opposite_word opsanus_tau optative_mood optic_axis optic_chiasm optic_chiasma optic_cup optic_disc optic_disk optic_nerve optic_radiation optic_tract optical_aberration optical_bench optical_condenser optical_crown optical_crown_glass optical_density optical_device optical_disc optical_disk optical_fiber optical_fibre optical_flint optical_fusion optical_glass optical_illusion optical_instrument optical_lens optical_maser optical_opacity optical_phenomenon optical_prism optical_pyrometer optical_telescope opuntia_cholla opuntia_lindheimeri opuntia_tuna or_circuit or_gate oracle_of_apollo oracle_of_delphi oral_cancer oral_cavity oral_communication oral_contraception oral_contraceptive oral_contraceptive_pill oral_contract oral_exam oral_examination oral_fissure oral_herpes oral_personality oral_phase oral_poliovirus_vaccine oral_presentation oral_roberts oral_sex oral_smear oral_stage orange-blossom_orchid orange-juice_concentrate orange_balsam orange_bat orange_daisy orange_fleabane orange_free_state orange_grass orange_group orange_grove orange_hawkweed orange_horseshoe_bat orange_juice orange_liqueur orange_marmalade orange_milkweed orange_milkwort orange_mushroom_pimple orange_order orange_peel orange_peel_fungus orange_pekoe orange_red orange_rind orange_river orange_sneezeweed orange_soda orange_toast orange_tortrix orange_tree orange_yellow orange_zest orb-weaving_spider orb_web orbiculate_leaf orbignya_cohune orbignya_martiana orbignya_phalerata orbignya_spesiosa orbit_period orbital_cavity orbital_motion orbital_plane orbital_point orbital_rotation orchard_apple_tree orchard_grass orchard_oriole orchestra_pit orchestral_bells orchid_cactus orchid_family orchid_tree orchidaceous_plant orchis_mascula orchis_papilionaceae orchis_spectabilis orcinus_orca ord_kangaroo_rat ordeal_bean ordeal_tree order_acarina order_accipitriformes order_actinaria order_actiniaria order_actinomycetales order_actinomyxidia order_aepyorniformes order_agaricales order_alcyonaria order_alismales order_amoebida order_amoebina order_amphipoda order_anacanthini order_anaspida order_andreaeales order_anguilliformes order_anoplura order_anostraca order_anseriformes order_anthocerotales order_anura order_aphyllophorales 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order_cycadofilicales order_cyclostomata order_cydippea order_cydippida order_cydippidea order_cypriniformes order_decapoda order_dermaptera order_dermoptera order_diapensiales order_dicranales order_dictyoptera order_dinocerata order_dinoflagellata order_dinornithiformes order_diptera order_discocephali order_ebenales order_edentata order_embiodea order_embioptera order_endomycetales order_entomophthorales order_ephemerida order_ephemeroptera order_equisetales order_ericales order_erysiphales order_eubacteriales order_eubryales order_euphausiacea order_eurotiales order_eurypterida order_exocycloida order_fagales order_falconiformes order_filicales order_foraminifera order_form order_fucales order_gadiformes order_galliformes order_ganoidei order_gaviiformes order_gentianales order_geophilomorpha order_geraniales order_ginkgoales order_gnetales order_graminales order_gregarinida order_gruiformes order_guttiferales order_gymnophiona order_haemosporidia order_haplosporidia order_heliozoa order_helotiales order_hemiptera order_heterosomata order_heterotrichales order_hymenogastrales order_hymenoptera order_hypericales order_hypermastigina order_hypocreales order_hyracoidea order_ichthyosauria order_ictodosauria order_insectivora order_insessores order_isoetales order_isopoda order_isoptera order_isospondyli order_juglandales order_jungermanniales order_lagomorpha order_laminariales order_lechanorales order_lepidodendrales order_lepidoptera order_lichenales order_liliales order_lobata order_loricata order_lycoperdales order_lycopodiales order_lyginopteridales order_madreporaria order_mallophaga order_malvales order_mantophasmatodea order_marattiales order_marchantiales order_marsupialia order_mecoptera order_moniliales order_monotremata order_mucorales order_musales order_myaceae order_mycelia_sterilia order_mycoplasmatales order_mycrosporidia order_myricales order_myrtales order_mysidacea order_myxobacterales order_myxobacteria order_myxobacteriales order_myxosporidia order_naiadales order_neuroptera order_nidulariales order_notostraca order_nudibranchia order_octopoda order_odonata order_oedogoniales order_of_business order_of_magnitude order_of_our_lady_of_mount_carmel order_of_payment order_of_saint_benedict order_of_the_day order_of_the_purple_heart order_oleales order_ophioglossales order_opiliones order_opuntiales order_orchidales order_ornithischia order_orthoptera order_ostariophysi order_osteoglossiformes order_ostracodermi order_palmales order_pandanales order_papaverales order_paper order_parietales order_passeriformes order_pectinibranchia order_pediculati order_pedipalpi order_pelecaniformes order_pelycosauria order_perciformes order_percomorphi order_perissodactyla order_peronosporales order_pezizales order_phalangida order_phallales order_phasmatodea order_phasmida order_pholidota order_picariae order_piciformes order_piperales order_plantaginales order_platyctenea order_plecoptera order_plectognathi order_pleuronectiformes order_plumbaginales order_podicipediformes order_podicipitiformes order_polemoniales order_polygonales order_polymastigina order_polypodiales order_primates order_primulales order_proboscidea order_procellariiformes order_proteales order_protura order_pseudomonadales order_pseudoscorpiones order_pseudoscorpionida order_psilophytales order_psilotales order_psittaciformes order_psocoptera order_pterosauria order_pulmonata order_pycnogonida order_radiolaria order_rajiformes order_ranales order_ranunculales order_raptores order_rhamnales order_rheiformes order_rhoeadales order_rhynchocephalia order_rickettsiales order_rodentia order_rosales order_rubiales order_salicales order_salientia order_santalales order_sapindales order_saprolegniales order_sarcosporidia order_sarraceniales order_saurischia order_sauropterygia order_scandentia order_sclerodermatales order_scleroparei order_scorpionida order_scrophulariales order_secotiales order_selaginellales order_siluriformes order_siphonaptera order_siphonophora order_sirenia order_solenichthyes order_solenogastres order_spatangoida order_sphaeriales order_sphaerocarpales order_sphagnales order_sphenisciformes order_spirochaetales order_squamata order_stegocephalia order_stereospondyli order_stomatopoda order_strigiformes order_struthioniformes order_synentognathi order_taxales order_temnospondyli order_testacea order_testudinata order_testudines order_tetraodontiformes order_thecodontia order_therapsida order_thymelaeales order_thysanoptera order_thysanura order_tinamiformes order_torpediniformes order_tremellales order_trichoptera order_trogoniformes order_tuberales order_tubulidentata order_tulostomatales order_ulvales order_umbellales order_uredinales order_urodella order_uropygi order_urticales order_ustilaginales order_volvocales order_xiphosura order_xyridales order_zeomorphi order_zygnemales order_zygnematales ordered_series orderly_sergeant ordinal_number ordinary_annuity ordinary_bicycle ordinary_care ordinary_life_insurance ordinary_shares ordnance_store ordnance_survey ordovician_period ore_bed ore_dressing ore_processing oreamnos_americanus orectolobus_barbatus oregon_alder oregon_ash oregon_cedar oregon_crab_apple oregon_fir oregon_grape oregon_holly_grape oregon_jargon oregon_larch oregon_lily oregon_maple oregon_myrtle oregon_oak oregon_pine oregon_white_oak oreo_cookie oreopteris_limbosperma oreortyx_picta_palmeri organ_donor organ_loft organ_of_corti organ_of_hearing organ_of_speech organ_pipe organ_stop organ_transplant organic_brain_syndrome organic_chemistry organic_compound organic_disorder organic_evolution organic_fertiliser organic_fertilizer organic_law organic_light-emitting_diode organic_phenomenon organic_process organic_structure organization_chart organization_expense organization_for_the_prohibition_of_chemical_weapons organization_man organization_of_american_states organization_of_petroleum-exporting_countries organization_of_the_oppressed_on_earth organized_crime organized_labor organized_religion organophosphate_nerve_agent oriel_window oriental_alabaster oriental_arborvitae oriental_beetle oriental_bittersweet oriental_black_mushroom oriental_bush_cherry oriental_cherry oriental_cockroach oriental_garlic oriental_person oriental_plane oriental_poppy oriental_roach oriental_scops_owl oriental_sore oriental_spruce oriental_studies orientation_course origanum_dictamnus origanum_majorana origanum_vulgare original_sin origination_fee orinasal_phone orinoco_river oriolus_oriolus orites_excelsa orkney_islands orlando_di_lasso orlop_deck orly_group ormosia_coarctata ormosia_monosperma ornithischian_dinosaur ornithogalum_pyrenaicum ornithogalum_thyrsoides ornithogalum_umbellatum ornithopod_dinosaur ornithorhynchus_anatinus orontium_aquaticum orphan_site orphans'_asylum orson_welles ortega_y_gasset orthoboric_acid orthochromatic_film orthodontic_braces orthodontic_treatment orthodox_catholic_church orthodox_church orthodox_jew orthodox_judaism orthodox_sleep orthogonal_opposition orthomorphic_projection orthophosphoric_acid orthophosphorous_acid orthopristis_chrysopterus orthopterous_insect orthostatic_hypotension orthotomus_sutorius orthotropous_ovule ortilis_vetula_macalli ortolan_bunting orudis_kt orville_wright orycteropus_afer oryctolagus_cuniculus oryx_gazella oryza_sativa oryzomys_palustris oryzopsis_hymenoides oryzopsis_miliacea os_breve os_capitatum os_frontale os_hamatum os_hyoideum os_ischii os_longum os_lunatum os_nasale os_palatinum os_pisiforme os_pubis os_scaphoideum os_sesamoideum os_sphenoidale os_tarsi_fibulare os_temporale os_trapezium os_trapezoideum os_triquetrum os_zygomaticum osage_orange osage_river osaka_bay osama_bin_laden oscar_fingal_o'flahertie_wills_wilde oscar_hammerstein oscar_hammerstein_ii oscar_palmer_robertson oscar_robertson oscar_wilde oscine_bird osculating_circle osip_emilevich_mandelstam osip_mandelstam osman_i osmanthus_americanus osmerus_eperlanus osmerus_mordax osmotic_pressure osmunda_cinnamonea osmunda_clatonia osmunda_regalis osseous_labyrinth osseous_tissue osso_buco osteitis_deformans ostensive_definition osteogenesis_imperfecta osteogenic_sarcoma osteosclerosis_congenita ostrea_gigas ostrich_fern ostrya_carpinifolia ostrya_virginiana ostwald's_theory_of_indicators oswald_spengler oswald_veblen oswego_tea otaheite_arrowroot otaheite_arrowroot_starch otaria_byronia otc_market otc_security otc_stock othman_i otho_i otho_of_lagery otic_ganglion otis_skinner otis_tarda otitis_externa otitis_interna otitis_media ottava_rima ottawa_river otter_hound otter_shrew ottmar_mergenthaler otto_frisch otto_fritz_meyerhof otto_hahn otto_heinrich_warburg otto_i otto_jespersen otto_loewi otto_meyerhof otto_neumann_sverdrup otto_robert_frisch otto_the_great otto_von_bismarck otto_wagner ottoman_dynasty ottoman_empire ottoman_turk ottorino_respighi otus_asio otus_scops otus_sunia ouachita_river ouija_board our_lady's_bedstraw our_lady's_mild_thistle 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political_economy political_entity political_hack political_incorrectness political_leader political_liberty political_machine political_movement political_orientation political_party political_platform political_prisoner political_program political_relation political_science political_scientist political_sphere political_sympathies political_system political_theory political_unit polka_dot poll_parrot poll_taker poll_tax pollachius_pollachius pollen_count pollen_tube polling_booth polling_day polling_place polling_station polly_fish polo-neck_collar polo_ball polo_mallet polo_pony polo_shirt polo_stick polyarteritis_nodosa polyborus_cheriway_audubonii polyborus_plancus polybotria_cervina polybotrya_cervina polychaete_worm polychete_worm polyconic_projection polycystic_kidney_disease polydactylus_virginicus polyergus_rufescens polyester_fiber polygala_alba polygala_lutea polygala_paucifolia polygala_senega polygala_vulgaris polygenic_disease polygenic_disorder polygonal_shape polygonatum_biflorum polygonatum_commutatum polygonia_comma polygonum_aubertii polygonum_fagopyrum polygonum_orientale polyhedral_angle polymeric_amide polymethyl_methacrylate polymonium_caeruleum_van-bruntiae polynesian_tattler polyneuritic_psychosis polyodon_spathula polyoma_virus polyostotic_fibrous_dysplasia polyphemus_moth polyphonic_letter polyphonic_music polyphonic_prose polyphosphoric_acid polypodium_aureum polypodium_glycyrrhiza polypodium_polypodioides polypodium_scouleri polypodium_virgianum polypodium_vulgare polyporus_frondosus polyporus_squamosus polyporus_tenuiculus polyprion_americanus polysemantic_word polysemous_word polystichum_acrostichoides polystichum_aculeatum polystichum_adiantiformis polystichum_braunii polystichum_lonchitis polystichum_scopulinum polystichum_setiferum polysyllabic_word polytechnic_institute polyunsaturated_fat polyunsaturated_fatty_acid polyurethane_foam polyvinyl_acetate polyvinyl_chloride polyvinyl_resin pomace_fly pomacentrus_leucostictus pomaderris_apetala pomatomus_saltatrix pomegranate_tree pomelo_tree pomme_blanche pomme_de_prairie pommel_horse pomolobus_pseudoharengus pomoxis_annularis pomoxis_nigromaculatus pompey_the_great ponce_de_leon poncirus_trifoliata pond-apple_tree pond-scum_parasite pond_apple pond_bald_cypress pond_cypress pond_lily pond_pine pond_scum ponderosa_pine pondweed_family pongamia_glabra pongo_pygmaeus pons_asinorum pons_varolii ponte_25_de_abril pontederia_cordata pontius_pilate pontoon_bridge pontoon_plane pony_cart pony_express poodle_dog pooecetes_gramineus pool_ball pool_cue pool_player pool_stick pool_table pooling_of_interest poop_deck poor_box poor_boy poor_devil poor_fish poor_law poor_man's_orchid poor_man's_pulse poor_man's_weatherglass poor_people poor_person poor_rates poor_speller poor_white_trash pop-up_book pop_art pop_bottle pop_fly pop_group pop_music pop_quiz pop_tent popcorn_ball pope's_nose pope_alexander_vi popillia_japonica poplar_bluff poplar_tree popliteal_artery popliteal_vein poppet_valve poppy_day poppy_family poppy_mallow poppy_seed popular_democratic_front_for_the_liberation_of_palestine popular_front popular_front_for_the_liberation_of_palestine popular_front_for_the_liberation_of_palestine-general_command popular_music popular_music_genre popular_opinion popular_struggle_front popularity_contest populated_area population_commission population_control population_growth population_profile population_scientist population_shift populist_party populus_alba populus_balsamifera populus_canescens populus_deltoides populus_grandidentata populus_heterophylla populus_nigra populus_nigra_italica populus_tremula populus_tremuloides populus_trichocarpa porcelain_clay porcupine_ball porcupine_fish porcupine_provision pore_fungus pore_mushroom poring_over pork-and-veal_goulash pork_and_beans pork_barrel pork_belly pork_butcher pork_loin pork_pie pork_roast pork_sausage pork_tenderloin porkpie_hat porn_merchant poronotus_triacanthus porphyrio_porphyrio porphyritic_rock porphyrula_martinica porpoise_oil port-access_coronary_bypass_surgery port-wine_stain port_arthur port_jackson_fig port_jackson_heath port_jackson_pine port_louis port_moresby port_of_call port_of_entry port_of_spain port_orford_cedar port_sudan port_vila port_watcher port_wine porta_hepatis portable_circular_saw portable_computer portable_saw portacaval_shunt portal_hypertension portal_site portal_system portal_tomb portal_vein porter's_beer porterhouse_steak portia_tree portland_cement portmanteau_word porto_novo porto_rico portrait_camera portrait_lens portrait_painter portuguese_cypress portuguese_escudo portuguese_guinea portuguese_heath portuguese_man-of-war portuguese_monetary_unit portuguese_republic portulaca_grandiflora portulaca_oleracea portunus_puber porzana_porzana pos_tagger position_effect position_paper positional_notation positional_representation_system positive_charge positive_chemotaxis positive_correlation positive_degree positive_feedback positive_fraud positive_identification positive_magnetic_pole positive_muon positive_pole positive_reinforcer positive_reinforcing_stimulus positive_stimulus positron_emission_tomography positron_emission_tomography_scanner posse_comitatus possessive_case possible_action possum_haw possum_oak post-hole_digger post-mortem_examination post-obit_bond post-office_box post-office_box_number post-rotational_nystagmus post_and_lintel post_card post_chaise post_doc post_exchange post_hoc post_hoc_ergo_propter_hoc post_hole post_horn post_horse post_house post_oak post_office post_road postage_meter postage_stamp postal_card postal_clerk postal_code postal_order postal_rate_commission postal_service postcentral_gyrus poste_restante poster_board poster_boy poster_child poster_color poster_colour poster_girl poster_paint posterior_cardinal_vein posterior_cerebral_artery posterior_facial_vein posterior_labial_veins posterior_meningeal_artery posterior_naris posterior_pituitary posterior_pituitary_gland posterior_serratus_muscle posterior_subcapsular_cataract posterior_synechia posterior_temporal_artery posterior_vein_of_the_left_ventricle postfix_notation posthole_digger posthumous_birth posthypnotic_amnesia posthypnotic_suggestion postmaster_general postmature_infant postmortem_examination postnasal_drip posttraumatic_amnesia posttraumatic_epilepsy posttraumatic_stress_disorder postural_hypotension pot_cheese pot_farm pot_likker pot_liquor pot_marigold pot_marjoram pot_metal pot_plant pot_roast potage_st._germain potamogale_velox potamogeton_americanus potamogeton_crispus potamogeton_gramineous potamogeton_nodosus potamophis_striatula potash_alum potash_muriate potassium-argon_dating potassium_acid_carbonate potassium_alum potassium_bicarbonate potassium_bitartrate potassium_bromide potassium_carbonate potassium_chlorate potassium_chloride potassium_cyanide potassium_dichromate potassium_ferrocyanide potassium_hydrogen_carbonate potassium_hydrogen_tartrate potassium_hydroxide potassium_iodide potassium_muriate potassium_nitrate potassium_permanganate potassium_sodium_tartrate potato_bean potato_beetle potato_blight potato_bug potato_chip potato_disease potato_family potato_fern potato_fungus potato_mildew potato_mold potato_mosaic potato_moth potato_murrain potato_nose potato_pancake potato_peel potato_peelings potato_race potato_salad potato_scab_bacteria potato_skin potato_tree potato_tuber_moth potato_tuberworm potato_vine potato_wart potato_wart_fungus potato_worm potato_yellow-dwarf_virus potato_yellow_dwarf potbelly_stove potemkin_village potential_difference potential_divider potential_drop potential_energy potential_unit potentilla_anserina poterium_sanguisorba potomac_river potos_caudivolvulus potos_flavus potsdam_conference pott's_disease potter's_clay potter's_earth potter's_field potter's_wheel potter_bee potter_wasp potty_chair potty_seat pouched_mammal pouched_mole pouched_mouse pouched_rat pouf_paste pound_cake pound_net pound_sign pound_sterling pounds_per_square_inch pouter_pigeon pouteria_campechiana_nervosa pouteria_zapota poverty_grass poverty_level poverty_line poverty_trap pow_camp powder-post_termite powder_and_shot powder_blue powder_compact powder_flask powder_horn powder_keg powder_magazine powder_metallurgy powder_method powder_monkey powder_photography powder_room powder_store powder_technique powdered_ginger powdered_milk powdered_mustard powdered_sugar powdery_mildew power-assisted_steering power-station_worker power_brake power_breakfast power_broker power_cable power_company power_cord power_couple power_dive power_drill power_failure power_grid power_hammer power_hunger power_law power_line power_loading power_loom power_meter power_module power_mower power_of_appointment power_of_attorney power_outage power_pack power_plant power_play power_point power_politics power_pylon power_saw power_series power_service power_shovel power_station power_steering power_structure power_system power_takeoff power_tool power_train power_trip power_unit power_user power_walking power_worker pr_man practical_application practical_joke practical_joker practical_nurse practical_politics practice_game practice_of_law practice_of_medicine practice_range practice_session practice_teacher praetorian_guard pragmatic_sanction prairia_sabbatia prairie_anemone prairie_aster prairie_berry prairie_bird's-foot_trefoil prairie_chicken prairie_coneflower prairie_cordgrass prairie_crab prairie_dock prairie_dog prairie_fire prairie_fowl prairie_fox prairie_gentian prairie_golden_aster prairie_gourd prairie_gourd_vine prairie_grass prairie_grouse prairie_lotus prairie_mallow prairie_marmot prairie_mimosa prairie_orchid prairie_rattler prairie_rattlesnake prairie_rocket prairie_sage prairie_sagewort prairie_schooner prairie_smoke prairie_soil prairie_star prairie_state prairie_sunflower prairie_trefoil prairie_trillium prairie_vole prairie_wagon prairie_wake-robin prairie_white-fringed_orchid prairie_white-fringed_orchis prairie_willow prairie_wolf prayer_beads prayer_book prayer_mat prayer_meeting prayer_of_azariah_and_song_of_the_three_children prayer_rug prayer_service prayer_shawl prayer_wheel praying_mantid praying_mantis pre-emptive_strike preacher_man precambrian_aeon precambrian_eon precambrian_period precentral_gyrus precession_of_the_equinoxes precious_coral precious_metal precious_stone precision_cookie precision_rifle preclinical_phase preclinical_test preclinical_trial precocious_dentition preconceived_idea preconceived_notion preconceived_opinion predatory_animal predicate_calculus predictor_variable preemployment_training_program preemptive_bid preemptive_right preen_gland preference_shares preferred_shares preferred_stock prefix_notation prefrontal_cortex prefrontal_leucotomy prefrontal_leukotomy prefrontal_lobe prefrontal_lobotomy pregnancy_test pregnant_chad prehistoric_culture preinvasive_cancer preliminary_exam preliminary_examination preliminary_prospectus premature_baby premature_ejaculation premature_infant premature_labor premature_labour premature_ventricular_contraction premenstrual_syndrome premium_bond prenanthes_alba prenanthes_purpurea prenanthes_serpentaria prenatal_diagnosis prep_school preparation_fire preparatory_school prepositional_object prepositional_phrase prepyloric_vein pres_young presbyterian_church presbytes_entellus prescription_drug prescription_medicine prescriptive_grammar prescriptive_linguistics presence_chamber presence_of_mind presenile_dementia present_moment present_participle present_perfect present_perfect_tense present_progressive present_progressive_tense present_tense present_times president_abraham_lincoln president_adams president_andrew_johnson president_arthur president_benjamin_harrison president_buchanan president_bush president_carter president_cleveland president_clinton president_coolidge president_eisenhower president_fillmore president_ford president_franklin_roosevelt president_garfield president_george_w._bush president_grant president_harding president_harrison president_hayes president_hoover president_jefferson president_john_adams president_john_f._kennedy president_john_quincy_adams president_johnson president_kennedy president_lincoln president_lyndon_johnson president_madison president_mckinley president_monroe president_nixon president_of_the_united_states president_pierce president_polk president_reagan president_roosevelt president_taft president_taylor president_theodore_roosevelt president_truman president_tyler president_van_buren president_washington president_william_henry_harrison president_wilson presidential_directive presidential_term presidents'_day presiding_officer press_agency press_agent press_association press_box press_clipping press_conference press_corps press_cutting press_gallery press_gang press_lord press_of_canvas press_of_sail press_photographer press_release press_run press_stud pressure-feed_lubricating_system pressure_cabin pressure_cooker pressure_dome pressure_feed pressure_gage pressure_gauge pressure_group pressure_level pressure_point pressure_sensation pressure_sore pressure_suit pressure_unit pressurized_water_reactor preterm_baby preterm_infant pretrial_conference prevailing_party prevailing_westerly prevailing_wind preventive_attack preventive_medicine preventive_strike previous_question priacanthus_arenatus price-to-earnings_ratio price_bracket price_competition price_control price_cut price_cutting price_floor price_freeze price_gouging price_increase price_index price_level price_list price_of_admission price_reduction price_support price_tag price_war pricing_system prickle_cell prickly-edged_leaf prickly-seeded_spinach prickly_ash prickly_custard_apple prickly_heat prickly_lettuce prickly_pear prickly_pear_cactus prickly_pine prickly_poppy prickly_shield_fern pride_of_barbados pride_of_bolivia pride_of_california pride_of_place priest-penitent_privilege prima_ballerina prima_donna primary_amenorrhea primary_atypical_pneumonia primary_care primary_care_physician primary_care_provider primary_cell primary_censorship primary_coil primary_color primary_color_for_light primary_color_for_pigments primary_colour primary_colour_for_light primary_colour_for_pigments primary_dentition primary_dysmenorrhea primary_election primary_feather primary_health_care primary_quill primary_school primary_sex_character primary_sex_characteristic primary_sexual_characteristic primary_solid_solution primary_subtractive_color_for_light primary_subtractive_colour_for_light primary_syphilis primary_tooth primary_winding prime_factor prime_interest_rate prime_meridian prime_minister prime_mover prime_number prime_of_life prime_quantity prime_time primer_coat priming_coat primitive_art primitive_person primordial_dwarf primrose_family primrose_jasmine primrose_path primula_auricula primula_elatior primula_polyantha primula_sinensis primula_veris primula_vulgaris primum_mobile primus_stove prince's_pine prince-of-wales_feather prince-of-wales_fern prince-of-wales_plume prince_albert prince_albert's_yew prince_albert_yew prince_charles prince_charming prince_consort prince_edward prince_edward_island prince_eugene_of_savoy prince_fumimaro_konoe prince_fumimaro_konoye prince_klemens_wenzel_nepomuk_lothar_von_metternich prince_of_darkness prince_of_smolensk prince_of_wales prince_of_wales_heath prince_otto_eduard_leopold_von_bismarck prince_otto_von_bismarck prince_peter_kropotkin prince_philip prince_rupert princess_diana princess_feather princess_grace_of_monaco princess_of_wales princess_pine princess_royal princeton_university princeton_wordnet principal_axis principal_diagonal principal_investigator principal_sum principality_of_andorra principality_of_liechtenstein principality_of_monaco principle_of_equivalence principle_of_liquid_displacement principle_of_parsimony principle_of_relativity principle_of_superposition print_buffer print_media print_run print_seller print_shop printed_circuit printed_symbol printer's_devil printer's_ink printer_cable printing_business printing_company printing_concern printing_ink printing_machine printing_operation printing_press printing_process printing_shop printing_unit priodontes_giganteus prionace_glauca prionotus_carolinus priority_processing priscoan_aeon priscoan_eon prism_spectroscope prison_camp prison_cell prison_chaplain prison_farm prison_guard prison_house prison_term prisoner's_base prisoner_of_war prisoner_of_war_camp prisoner_of_war_censorship pristis_pectinatus pritzelago_alpina private_citizen private_corporation private_detective private_enterprise private_eye private_foundation private_instructor private_investigator private_line private_nuisance private_parts private_practice private_property private_road private_school private_security_force private_treaty privately_held_corporation privet_andromeda privet_hedge privilege_against_self_incrimination privilege_of_the_floor privy_council privy_purse prix_de_rome prix_fixe prix_goncourt prize_fight prize_money prize_ring prize_winner pro-choice_faction pro-life_faction probability_theorist probability_theory probable_cause probate_court probate_will probation_officer probiotic_bacterium probiotic_flora probiotic_microflora problem-oriented_language problem_solver problem_solving proboscidea_arenaria proboscidea_fragrans proboscidea_louisianica proboscis_flower proboscis_monkey proboscis_worm procaine_hydrochloride procavia_capensis procellaria_aequinoctialis procellariiform_seabird process_cheese process_of_monition process_printing processed_cheese processing_time processional_march processus_coronoideus prociphilus_tessellatus procrustean_bed procrustean_rule procrustean_standard procursive_epilepsy procyon_cancrivorus procyon_lotor producer_gas producer_price_index product-moment_correlation_coefficient product_development product_introduction product_line product_research production_cost production_line production_order professional_association professional_baseball professional_basketball professional_boxing professional_dancer professional_football professional_golf professional_golfer professional_life professional_organisation professional_organization professional_person professional_relation professional_tennis professional_tennis_player professional_wrestling profit-and-loss_statement profit_and_loss profit_and_loss_account profit_margin profit_sharing profit_taker progne_subis program_library program_line program_music program_trading programing_language programme_music programmed_cell_death programming_error programming_language progress_report progressive_aspect progressive_emphysematous_necrosis progressive_party progressive_rock progressive_tax progressive_tense progressive_vaccinia prohibition_era prohibition_party projection_screen projective_device projective_geometry projective_technique projective_test prolate_cycloid prolonge_knot prolonged_interrogation promenade_deck promised_land promissory_note promotion_system promotional_expense promotional_material prompt_box prompt_copy prompter's_box prone_float pronghorn_antelope pronominal_phrase proof_spirit prop_root propanoic_acid propellant_explosive propeller_plane propenoic_acid propenyl_alcohol proper_fraction proper_name proper_noun property_line property_man property_master property_owner property_right property_settlement property_tax prophylactic_device propionic_acid proportional_counter proportional_counter_tube proportional_font proportional_representation proportional_sample proportional_sampling proportional_tax proposal_of_marriage propositional_calculus propositional_logic propoxyphene_hydrochloride propping_up proprietary_colony proprietary_drug proprietorship_certificate propulsion_system propyl_alcohol propyl_group propyl_radical propylene_glycol proscenium_arch proscenium_wall prose_poem prosecuting_attorney prosecuting_officer prosodic_system prosopis_glandulosa prosopis_juliflora prosopis_juliiflora prosopis_pubescens prosopium_cylindraceum prosopium_williamsonii prosper_meniere prospero_lambertini prostate_cancer prostate_gland prostate_specific_antigen prostatic_adenocarcinoma prosthetic_device protea_cynaroides protea_family protea_mellifera protease_inhibitor protective_coloration protective_cover protective_covering protective_embankment protective_fold protective_garment protective_tariff protector_of_boundaries protein_folding protein_molecule proteles_cristata proteolytic_enzyme proteosome_vaccine proterozoic_aeon proterozoic_eon protest_march protestant_church protestant_deacon protestant_denomination protestant_episcopal_church protestant_reformation proteus_anguinus prothrombin_accelerator protium_guianense protium_heptaphyllum proto-indo_european protoctist_family protoctist_genus protoctist_order proton_accelerator proton_magnetic_resonance protoplasmic_astrocyte protozoal_infection proud_flesh provincial_capital proving_ground provisional_ira provisional_irish_republican_army provitamin_a provost_court provost_guard provost_marshal prowl_car proxima_centauri proxy_fight proxy_war prudhoe_bay prumnopitys_amara prumnopitys_andina prumnopitys_elegans prumnopitys_ferruginea prumnopitys_taxifolia prune_cake prune_whip prunella_modularis prunella_vulgaris pruning_hook pruning_knife pruning_saw pruning_shears prunus_alleghaniensis prunus_americana prunus_amygdalus prunus_angustifolia prunus_armeniaca prunus_avium prunus_besseyi prunus_capuli prunus_caroliniana prunus_cerasifera prunus_cerasus prunus_cerasus_austera prunus_cerasus_caproniana prunus_cerasus_marasca prunus_cuneata prunus_dasycarpa prunus_demissa prunus_domestica prunus_domestica_insititia prunus_dulcis prunus_dulcis_amara prunus_glandulosa prunus_ilicifolia prunus_incisa prunus_insititia prunus_japonica prunus_laurocerasus prunus_lyonii prunus_maritima prunus_mexicana prunus_mume prunus_nigra prunus_padus prunus_pensylvanica prunus_persica prunus_persica_nectarina prunus_pumila prunus_pumilla_susquehanae prunus_salicina prunus_serotina prunus_serrulata prunus_sieboldii prunus_spinosa prunus_subcordata prunus_subhirtella prunus_susquehanae prunus_tenella prunus_triloba prunus_virginiana prunus_virginiana_demissa pruritus_ani pruritus_vulvae prussian_asparagus prussian_blue prussic_acid pry_bar przevalski's_horse przewalski's_horse psa_blood_test psephurus_gladis psetta_maxima psettichthys_melanostichus pseudaletia_unipuncta pseudechis_porphyriacus pseudemys_concinna pseudemys_rubriventris pseudemys_scripta pseudobombax_ellipticum pseudococcus_comstocki pseudococcus_fragilis pseudocolus_fusiformis pseudohypertrophic_dystrophy pseudolarix_amabilis pseudomonas_pyocanea pseudomonas_solanacearum pseudopleuronectes_americanus pseudoryx_nghetinhensis pseudotaxus_chienii pseudotsuga_macrocarpa pseudotsuga_menziesii pseudowintera_colorata psi_particle psidium_cattleianum psidium_guajava psidium_guineense psidium_littorale psidium_littorale_longipes psilotum_nudum psittacula_krameri psittacus_erithacus psocopterous_insect psophia_crepitans psophocarpus_tetragonolobus psoralea_esculenta psoriatic_arthritis psychedelic_drug psychedelic_rock psychiatric_hospital psychic_communication psychic_energy psychic_phenomena psychic_phenomenon psychic_trauma psychical_communication psychoactive_drug psychoactive_substance psychoanalytic_process psychodelic_drug psychogalvanic_response psychogenic_fugue psychological_condition psychological_disorder psychological_feature psychological_medicine psychological_moment psychological_operation psychological_science psychological_state psychological_warfare psychology_department psychometric_test psychomotor_development psychomotor_epilepsy psychopathic_personality psychopsis_krameriana psychopsis_papilio psychosexual_development psychosomatic_disorder psychotherapy_group psychotic_belief psychotic_depression psychotic_person psychotria_capensis psychotropic_agent pt_boat pteretis_struthiopteris pteridium_aquilinum pteridium_esculentum pteris_cretica pteris_multifida pteris_serrulata pternohyla_fodiens pterocarpus_angolensis pterocarpus_indicus pterocarpus_macrocarpus pterocarpus_marsupium pterocarpus_santalinus pterocarya_fraxinifolia pterocles_alchata pterocles_indicus pterocnemia_pennata pteropogon_humboltianum pteropus_capestratus pteropus_hypomelanus pterospermum_acerifolium pteroylglutamic_acid pteroylmonoglutamic_acid pterygoid_muscle pterygoid_plexus pterygoid_process ptilonorhynchus_violaceus ptloris_paradisea ptolemaic_dynasty ptolemaic_system ptolemy_i ptolemy_ii ptomaine_poisoning ptyas_mucosus ptychozoon_homalocephalum pub_crawl pubic_bone pubic_hair pubic_louse pubic_region public-relations_campaign public-service_corporation public_address_system public_assistance public_charity public_convenience public_debate public_debt public_defender public_discussion public_domain 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camp_down camp_out cancel_out cannonball_along cant_over cap_off care_a_hang care_for carpet_bomb carry-the_can carry_away carry_back carry_forward carry_off carry_on carry_out carry_over carry_through carry_to_term carry_weight cart_away cart_off carve_out carve_up cascade_down cash_in cash_in_on cash_in_one's_chips cash_out cast_about cast_anchor cast_around cast_aside cast_away cast_down cast_off cast_on cast_out catch_a_glimpse catch_a_wink catch_cold catch_fire catch_it catch_on catch_one's_breath catch_out catch_sight catch_some_z's catch_up catch_up_with cause_to_be_perceived cause_to_sleep cave_in cavern_out cell_phone center_on center_punch chain_up chalk_out chalk_up chance_on chance_upon change_by_reversal change_course change_form change_hands change_integrity change_intensity change_magnitude change_over change_owners change_posture change_shape change_state change_surface change_taste charge_up chase_after chase_away chat_up cheat_on check_in check_into check_off check_out check_over check_up_on cheer_up chew_out chew_over chew_the_fat chew_up chicken_out chill_out chime_in chin_up chip_at chip_away chip_away_at chip_in chip_off chirk_up chisel_in chock_up choke_back choke_down choke_off choke_up choose_up chop_down chop_off chop_up chuck_out chuck_up_the_sponge chum_up churn_out churn_up circle_around circle_round clam_up clamp_down clap_on clap_together clap_up clean_house clean_out clean_up clear_away clear_off clear_out clear_the_air clear_the_throat clear_up click_off click_open climb_down climb_on climb_up cling_to clock_in clock_off clock_on clock_out clock_up clog_up close_down close_in close_off close_out close_up cloud_over cloud_up clown_around clue_in clutter_up co-occur_with cobble_together cobble_up cock_up cold_work collide_with collocate_with color_in colour_in comb_out come_about come_across come_after come_alive come_along come_apart come_around come_away come_back come_before come_by come_close come_down come_forth come_forward come_home come_in come_in_for come_in_handy come_into come_into_being come_near come_of_age come_off come_on come_out come_out_of_the_closet come_over come_round come_short come_through come_to come_to_grips come_to_hand come_to_life come_to_light come_to_mind come_to_the_fore come_together come_up come_up_to come_upon come_with commit_suicide conceive_of concentrate_on condole_with confer_with conform_to conjure_up conk_out connive_at construe_with contra_danse contract_in contract_out cooccur_with cook_out cook_up cool_down cool_it cool_off cool_one's_heels coop_in coop_up cop_out cope_with copy_out cordon_off core_out cork_up cotton_on cotton_up cough_out cough_up count_down count_off count_on count_out couple_on couple_up court_favor court_favour cover_for cover_up cozy_up crack_down crack_up crank_out crank_up crap_up crash_land crawfish_out crawl_in cream_off create_by_mental_act create_from_raw_material create_from_raw_stuff create_mentally create_verbally creep_in creep_up crock_up crop_out crop_up cross_examine cross_off cross_oneself cross_out cross_question crowd_out crowd_together crush_out cry_for cry_out cry_out_for cull_out curl_up curry_favor curry_favour curtain_off cut_across cut_away cut_back cut_corners cut_down cut_in cut_into cut_off cut_out cut_short cut_through cut_to cut_to_ribbons cut_up cycle_on dam_up dash_down dash_off date_back date_from date_stamp deal_out deck_out deck_up declare_oneself deed_over deep_freeze default_on deliver_the_goods depend_on depend_upon descant_on devolve_on die_away die_back die_down die_hard die_off die_out dig_in dig_into dig_out dig_up dine_in dine_out dip_into dip_solder disagree_with dish_out dish_the_dirt dish_up dispense_with dispose_of divvy_up do_a_job_on do_away_with do_by do_drugs do_good do_in do_it do_justice do_one's_best do_the_dishes do_the_honors do_up do_well do_well_by do_work doctor_up dole_out doll_up dope_off dope_up doss_down double_back double_birdie double_bogey double_crochet double_cross double_dye double_over double_stitch double_tongue double_up doze_off drag_a_bunt drag_down drag_in drag_on drag_one's_feet drag_one's_heels drag_out drag_through_the_mud drag_up drain_the_cup draw_a_bead_on draw_a_blank draw_a_line draw_and_quarter draw_away draw_back draw_close draw_in draw_near draw_off draw_out draw_rein draw_the_line draw_together draw_up dream_up dredge_up dress_down dress_out dress_ship dress_up drift_apart drift_away drift_off drill_in drink_down drink_in drink_up drive_around drive_away drive_back drive_home drive_in drive_off drive_out drive_up drone_on drool_over drop_a_line drop_anchor drop_away drop_back drop_behind drop_by drop_by_the_wayside drop_dead drop_down drop_in drop_like_flies drop_off drop_one's_serve drop_open drop_out drop_the_ball drown_out drowse_off drum_out drum_up dry_clean dry_out dry_up dummy_up dwell_on dwindle_away dwindle_down earth_up ease_off ease_up eat_at eat_away eat_in eat_into eat_on eat_out eat_up ebb_away ebb_down ebb_off ebb_out edge_in edge_up edit_out egg_on eke_out embark_on encroach_upon end_up enter_upon escape_from even_off even_out even_up excel_at explode_a_bombshell express_emotion express_feelings express_joy express_mirth extend_oneself extend_to face_off face_the_music face_up factor_analyse factor_analyze factor_in factor_out fade_away fade_out fag_out faggot_up fall_all_over fall_apart fall_asleep fall_away fall_back fall_behind fall_by_the_wayside fall_down fall_flat fall_for fall_from_grace fall_in fall_in_line fall_in_love fall_into fall_into_place fall_off fall_open fall_out fall_over fall_over_backwards fall_short fall_short_of fall_through fall_under fall_upon fan_out fancy_up farm_out fast_dye fasten_on fatten_out fatten_up feast_one's_eyes feather_one's_nest feed_back feed_in feed_on feed_upon feel_for feel_like feel_like_a_million feel_like_a_million_dollars feel_out felt_up fence_in fend_for fend_off ferret_out fess_up fetch_up fiddle_with fig_out fig_up fight_back fight_down fight_off figure_out figure_skate file_away file_in file_out fill_again fill_in fill_out fill_the_bill fill_up film_over filter_out find_fault find_oneself find_out finish_off finish_out finish_up fire_up firm_up fit_in fit_out fit_the_bill fix_up fizzle_out flag_down flake_off flake_out flame_up flap_down flare_out flare_up flash_back flatten_out flesh_out fling_off flip_one's_lid flip_one's_wig flip_out flip_over flirt_with flood_in flood_out flow_away flow_from flow_off flow_out fluff_up flump_down flush_down flush_it fly_blind fly_by fly_contact fly_high fly_in_the_face_of fly_in_the_teeth_of fly_off_the_handle fly_on fly_open fly_the_coop foam_at_the_mouth fob_off focus_on fog_up foist_off fold_up folk_dance follow_out follow_suit follow_through follow_up follow_up_on fool_around fool_away foot_up foray_into force_back force_out fork_out fork_over fork_up form_bubbles fort_up foul_out foul_up frame_in frame_up freak_out freeze_down freeze_off freeze_out freshen_up frig_around frighten_away frighten_off fritter_away frivol_away frost_over froth_at_the_mouth frown_on frown_upon fuck_off fuck_up fudge_together fund_raise furbish_up gain_ground gain_vigor gang_up garbage_down gas_up gather_in gather_up gear_up get_a_line get_a_load get_a_look get_a_noseful get_a_whiff get_about get_across get_ahead get_along get_along_with get_around get_around_to get_at get_away get_back get_behind get_by get_cracking get_down get_dressed get_even get_going get_hitched_with get_hold get_hold_of get_in get_in_touch get_into get_it get_it_on get_laid get_married get_moving get_off get_off_the_ground get_on get_on_with get_one's_lumps get_onto get_out get_over get_rid_of get_rolling get_started get_stuck get_the_best get_the_better_of get_the_goods get_the_hang get_the_jump get_the_picture get_through get_to get_to_grips get_together get_under_one's_skin get_up get_weaving get_well get_wind get_wise get_word get_worse gild_the_lily ginger_up give-up_the_ghost give_a_damn give_a_hang give_a_hoot give_and_take give_away give_back give_birth give_care give_chase give_ear give_forth give_full_measure give_in give_it_a_try give_it_a_whirl give_it_the_deep_six give_notice give_off give_one's_best give_out give_rise give_suck give_thanks give_the_axe give_the_bounce give_the_eye give_the_gate give_the_glad_eye give_the_once_over give_the_sack give_tongue_to give_up give_vent give_voice give_way glam_up glance_over glass_in glass_over glaze_over glide_by gloss_over gnaw_at go_a_long_way go_about go_across go_after go_against go_ahead go_all_out go_along go_around go_away go_back go_back_on go_bad go_ballistic go_by go_deep go_down go_down_on go_dutch go_far go_for go_for_broke go_forth go_forward go_home go_in go_into go_off go_off_at_half-cock go_off_half-cocked go_on go_out go_over go_past go_steady go_through go_through_the_motions go_to go_to_bed go_to_pieces go_to_pot go_to_sleep go_to_the_dogs go_to_war go_under go_up go_with go_wrong gobble_up gold_plate goose_step gouge_out grass_over grease_one's_palms grind_away grind_down grind_out grind_to_a_halt grope_for gross_out ground_out grow_over grow_up grub_out grub_up guide_on gum_up gun_down gussy_up hack_on had_best ham_it_up hammer_in hammer_out hand_down hand_out hand_over handle_with_kid_gloves hang_around hang_back hang_by_a_hair hang_by_a_thread hang_glide hang_in hang_on hang_out hang_together hang_up happen_upon hard_surface hark_back harsh_on hash_out hash_over haul_away haul_off haul_up have-to_doe_with have_a_ball have_a_bun_in_the_oven have_a_fit have_a_go have_a_go_at_it have_a_good_time have_a_look have_down have_got have_in_mind have_intercourse have_it_away have_it_coming have_it_off have_kittens have_on have_sex have_the_best have_words have_young haze_over head_for_the_hills head_home head_off head_up heap_up hear_out heat_up heave_up hedge_in heft_up help_oneself help_out hem_and_haw hem_in hew_out hide_out hightail_it hike_up 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keep_back keep_company keep_down keep_going keep_guard keep_in keep_in_line keep_mum keep_note keep_off keep_on keep_one's_distance keep_one's_eyes_off keep_one's_eyes_open keep_one's_eyes_peeled keep_one's_eyes_skinned keep_one's_hands_off keep_one's_mouth_shut keep_one's_nose_to_the_grindstone keep_one's_shoulder_to_the_wheel keep_open keep_out keep_pace keep_quiet keep_step keep_tabs_on keep_to_oneself keep_track keep_up key_out kick_about kick_around kick_back kick_down kick_downstairs kick_in kick_off kick_one's_heels kick_out kick_the_bucket kick_up kick_upstairs kill_off kill_oneself kink_up kip_down kit_out kit_up knock_about knock_against knock_back knock_cold knock_down knock_off knock_out knock_over knock_up know_apart know_the_score know_what's_going_on know_what's_what knuckle_down knuckle_under lace_into lace_up lam_into land_up lap_up lark_about lash_out lash_together last_out latch_on laugh_at laugh_away laugh_loudly laugh_off laugh_softly lay_aside lay_away lay_claim lay_down lay_eyes_on lay_hands_on lay_in lay_into lay_off lay_on_the_line lay_out lay_over lay_to_rest lay_up lay_waste_to lead_astray lead_by_the_nose lead_off lead_on lead_up league_together leak_out lean_against lean_back lean_on leap_out leave_alone leave_behind leave_no_stone_unturned leave_off leave_office leave_out lech_after leech_onto leg_it lend_oneself let_down let_drive let_fly let_go let_go_of let_in let_it_go let_loose let_off let_on let_out let_the_cat_out_of_the_bag let_up letter_bomb level_off lie_about lie_around lie_awake lie_dormant lie_down lie_in lie_in_wait lie_low lie_with lift_off lift_out lift_up light_up light_upon lighten_up limber_up line_one's_pockets line_up linger_over link_up lip_off listen_in live_down live_in live_it_up live_on live_out live_over live_together live_up_to live_with liven_up load_down load_up lock_away lock_in lock_out lock_up lodge_in log_in log_off log_on log_out log_up log_z's loll_around look_across look_after look_around look_at look_away look_back look_backward look_down_on look_for look_forward look_into look_like look_on look_out look_out_on look_out_over look_sharp look_to look_up look_up_to look_upon loose_off loosen_up lop_off lord_it_over lose_it lose_one's_temper lose_sight_of lose_track lose_weight lounge_about lounge_around louse_up lower_oneself luck_into luck_it luck_out luck_through lust_after machine_gun machine_wash mail_out make_a_clean_breast_of make_a_face make_a_motion make_a_point make_a_stink make_as_if make_believe make_bold make_clean make_do make_for make_full make_fun make_good make_grow make_happy make_hay make_headway make_it make_love make_merry make_no_bones_about make_noise make_off make_out make_over make_pass make_peace make_pure make_relaxed make_sense make_sure make_unnecessary make_up make_up_one's_mind make_vibrant_sounds make_water make_way make_whoopie map_out march_on march_out mark_down mark_off mark_out mark_up mat_up matte_up matter_to max_out measure_out measure_up 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pass_through pass_up pass_water pass_with_flying_colors patch_up pay_back pay_cash pay_for pay_heed pay_off pay_out pay_up peck_at peel_off peer_review peg_away peg_down pelt_along pen_up pension_off pep_up perk_up persist_in peter_out phase_in phase_out pick_apart pick_at pick_off pick_out pick_over pick_up pick_up_the_gauntlet piddle_away pig_it pig_out pile_up pin_down pin_up pine_away pip_out pipe_down pipe_in pipe_up pitch_in pitch_into place_down place_upright plank_down plank_over plaster_over play_a_joke_on play_a_trick_on play_along play_around play_back play_down play_false play_hooky play_it_by_ear play_off play_out play_possum play_tricks play_up plonk_down plough_on plow_ahead pluck_at plug_away plug_in plug_into plump_down plump_for plump_in plump_out plump_up plunk_down plunk_for point_jam point_out point_the_way point_up poke_at poke_fun poke_into poke_out polish_off polish_up pony_up pooch_out poop_out pop_in pop_off pop_out pop_the_question pop_up portion_out 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out_of_hand out_of_nothing out_of_place out_of_sight out_of_the_blue out_of_the_way out_of_thin_air out_of_view out_of_wedlock outside_marriage over_again over_and_over over_and_over_again over_here par_excellence pari_passu per_annum per_capita per_diem per_se per_year post_meridiem pretty_much prima_facie pro_forma pro_rata pro_re_nata pro_tem pro_tempore properly_speaking put_differently quite_a quite_an right_along right_away right_on right_smart round_the_clock scot_free second_class second_hand since_a_long_time_ago sincerely_yours sine_die six_times so_far so_to_speak soon_enough sort_of sotto_voce step_by_step straight_off strictly_speaking sub_rosa summa_cum_laude sure_as_shooting sure_enough tete_a_tete that_is_to_say that_much the_least_bit the_other_way_around the_right_way the_whole_way then_again this_evening this_night thousand_times three_times through_an_experiment through_and_through through_empirical_observation thus_far til_now time_and_again time_and_time_again to_a_fault to_a_great_extent to_a_greater_extent to_a_higher_place to_a_lesser_extent to_a_lower_place to_a_man to_a_t to_advantage to_all_intents_and_purposes to_and_fro to_be_precise to_be_sure to_begin_with to_boot to_both_ears to_date to_each_one to_it to_no_degree to_one_ear to_order to_perfection to_that to_that_degree to_that_effect to_that_extent to_the_contrary to_the_full to_the_highest_degree to_the_hilt to_the_letter to_the_limit to_the_lowest_degree to_the_south to_wit together_with too_much too_soon tooth_and_nail tout_ensemble two_times ultra_vires unable_to_help under_arms under_it under_that under_the_circumstances under_way until_now up_and_down up_here up_the_stairs up_to_now upside_down very_fast very_loudly very_much very_much_like very_softly very_well vice_versa viva_voce week_after_week week_by_week what_is_more whatever_may_come when_first_seen when_the_time_comes without_doubt word_for_word you_bet you_said_it WordNet-Similarity-2.05/samples/sample.pl0000755000175000017500000001507111020102554016545 0ustar sidsid#! /usr/bin/perl -w use strict; use warnings; # Sample Perl program, showing how to use the # WordNet::Similarity measures. # WordNet::QueryData is required by all the # relatedness modules. use WordNet::QueryData; # 'use' each module that you wish to use. use WordNet::Similarity::random; use WordNet::Similarity::path; use WordNet::Similarity::wup; use WordNet::Similarity::lch; use WordNet::Similarity::jcn; use WordNet::Similarity::res; use WordNet::Similarity::lin; use WordNet::Similarity::hso; use WordNet::Similarity::lesk; use WordNet::Similarity::vector; use WordNet::Similarity::vector_pairs; # Get the concepts. my $wps1 = shift; my $wps2 = shift; unless (defined $wps1 and defined $wps2) { print STDERR "Undefined input\n"; print STDERR "Usage: sample.pl synset1 synset2\n"; print STDERR "\tSynsets must be in word#pos#sense format (ex., dog#n#1)\n"; exit 1; } # Load WordNet::QueryData print STDERR "Loading WordNet... "; my $wn = WordNet::QueryData->new; die "Unable to create WordNet object.\n" if(!$wn); print STDERR "done.\n"; # Create an object for each of the measures of # semantic relatedness. print STDERR "Creating jcn object... "; my $jcn = WordNet::Similarity::jcn->new($wn, "config-files/config-jcn.conf"); die "Unable to create jcn object.\n" if(!defined $jcn); my ($error, $errString) = $jcn->getError(); die $errString if($error > 1); print STDERR "done.\n"; print STDERR "Creating res object... "; my $res = WordNet::Similarity::res->new($wn, "config-files/config-res.conf"); die "Unable to create res object.\n" if(!defined $res); ($error, $errString) = $res->getError(); die $errString if($error > 1); print STDERR "done.\n"; print STDERR "Creating lin object... "; my $lin = WordNet::Similarity::lin->new($wn, "config-files/config-lin.conf"); die "Unable to create lin object.\n" if(!defined $lin); ($error, $errString) = $lin->getError(); die $errString if($error > 1); print STDERR "done.\n"; print STDERR "Creating wup object... "; my $wup = WordNet::Similarity::wup->new($wn, "config-files/config-wup.conf"); die "Unable to create wup object.\n" if(!defined $wup); ($error, $errString) = $wup->getError(); die $errString if($error > 1); print STDERR "done.\n"; print STDERR "Creating lch object... "; my $lch = WordNet::Similarity::lch->new($wn, "config-files/config-lch.conf"); die "Unable to create lch object.\n" if(!defined $lch); ($error, $errString) = $lch->getError(); die $errString if($error > 1); print STDERR "done.\n"; print STDERR "Creating hso object... "; my $hso = WordNet::Similarity::hso->new($wn, "config-files/config-hso.conf"); die "Unable to create hso object.\n" if(!defined $hso); ($error, $errString) = $hso->getError(); die $errString if($error > 1); print STDERR "done.\n"; print STDERR "Creating path object... "; my $path = WordNet::Similarity::path->new($wn, "config-files/config-path.conf"); die "Unable to create path object.\n" if(!defined $path); ($error, $errString) = $path->getError(); die $errString if($error > 1); print STDERR "done.\n"; print STDERR "Creating random object... "; my $random = WordNet::Similarity::random->new($wn, "config-files/config-random.conf"); die "Unable to create random object.\n" if(!defined $random); ($error, $errString) = $random->getError(); die $errString if($error > 1); print STDERR "done.\n"; print STDERR "Creating lesk object... "; my $lesk = WordNet::Similarity::lesk->new($wn, "config-files/config-lesk.conf"); die "Unable to create lesk object.\n" if(!defined $lesk); ($error, $errString) = $lesk->getError(); die $errString if($error > 1); print STDERR "done.\n"; print STDERR "Creating vector object... "; my $vector = WordNet::Similarity::vector->new($wn, "config-files/config-vector.conf"); die "Unable to create vector object.\n" if(!defined $vector); ($error, $errString) = $vector->getError(); die $errString if($error > 1); print STDERR "done.\n"; print STDERR "Creating vector_pairs object... "; my $vector_pairs = WordNet::Similarity::vector_pairs->new($wn, "config-files/config-vector_pairs.conf"); die "Unable to create vector_pairs object.\n" if(!defined $vector_pairs); ($error, $errString) = $vector_pairs->getError(); die $errString if($error > 1); print STDERR "done.\n"; # Find the relatedness of the concepts using each of # the measures. my $value = $jcn->getRelatedness($wps1, $wps2); ($error, $errString) = $jcn->getError(); die $errString if($error > 1); print "JCN Similarity = $value\n"; print "JCN ErrorString = $errString\n" if $error; $value = $res->getRelatedness($wps1, $wps2); ($error, $errString) = $res->getError(); die $errString if($error > 1); print "RES Similarity = $value\n"; print "RES ErrorString = $errString\n" if $error; $value = $lin->getRelatedness($wps1, $wps2); ($error, $errString) = $lin->getError(); die $errString if($error > 1); print "LIN Similarity = $value\n"; print "LIN ErrorString = $errString\n" if $error; $value = $wup->getRelatedness($wps1, $wps2); ($error, $errString) = $wup->getError(); die $errString if($error > 1); print "WUP Similarity = $value\n"; print "WUP ErrorString = $errString\n" if $error; $value = $lch->getRelatedness($wps1, $wps2); ($error, $errString) = $lch->getError(); die $errString if($error > 1); print "LCH Similarity = $value\n"; print "LCH ErrorString = $errString\n" if $error; $value = $hso->getRelatedness($wps1, $wps2); ($error, $errString) = $hso->getError(); die $errString if($error > 1); my $trace = $hso->getTraceString(); print "HSO Similarity = $value\n"; print "HSO ErrorString = $errString\n" if $error; print "HSO TRACE\n\n$trace\n"; $value = $path->getRelatedness($wps1, $wps2); ($error, $errString) = $path->getError(); die $errString if($error > 1); print "PATH Similarity = $value\n"; print "PATH ErrorString = $errString\n" if $error; $value = $random->getRelatedness($wps1, $wps2); ($error, $errString) = $random->getError(); die $errString if($error > 1); print "RANDOM Similarity = $value\n"; print "RANDOM ErrorString = $errString\n" if $error; $value = $lesk->getRelatedness($wps1, $wps2); ($error, $errString) = $lesk->getError(); die $errString if($error > 1); print "LESK Similarity = $value\n"; print "LESK ErrorString = $errString\n" if $error; $value = $vector->getRelatedness($wps1, $wps2); ($error, $errString) = $vector->getError(); die $errString if($error > 1); print "VECTOR Similarity = $value\n"; print "VECTOR ErrorString = $errString\n" if $error; $value = $vector_pairs->getRelatedness($wps1, $wps2); ($error, $errString) = $vector_pairs->getError(); die $errString if($error > 1); print "VECTOR_PAIRS Similarity = $value\n"; print "VECTOR_PAIRS ErrorString = $errString\n" if $error; __END__ WordNet-Similarity-2.05/samples/config-files/0000755000175000017500000000000011025552366017304 5ustar sidsidWordNet-Similarity-2.05/samples/config-files/config-lch.conf0000644000175000017500000000167611015336623022171 0ustar sidsidWordNet::Similarity::lch # A sample configuration file for the lch measure trace::0 # turn off (0) tracing. Turn on tracing by setting # this to 1 or 2. The default value is off. If the value # is omitted, then 1 is assumed. 0, 1, and 2 are # the only valid settings for this option. cache:: # turn on caching. Turn off caching by setting this # to 0. The default is on. If the value is omitted, # then 1 is assumed. 0 and 1 are the only valid # settings. maxCacheSize::1000 # limit the cache size to 1000 pairs of query words. # The default is 5000. The value of this option # must be a non-negative integer or "unlimited" (without # the quotes). rootNode::1 # use a unique root node in the noun and verb taxonomies. # Use the value 0 if you don't want a unique root node. # If the value is omitted, then 1 is assumed. The only # valid values are 0 and 1. WordNet-Similarity-2.05/samples/config-files/config-lin.conf0000644000175000017500000000243711015336623022201 0ustar sidsidWordNet::Similarity::lin # A sample configuration file for the lin measure trace::0 # turn off (0) tracing. Turn on tracing by setting # this to 1 or 2. The default value is off. If the value # is omitted, then 1 is assumed. 0, 1, and 2 are # the only valid settings for this option. cache:: # turn on caching. Turn off caching by setting this # to 0. The default is on. If the value is omitted, # then 1 is assumed. 0 and 1 are the only valid # settings. maxCacheSize::1000 # limit the cache size to 1000 pairs of query words. # The default is 5000. The value of this option # must be a non-negative integer or "unlimited" (without # the quotes). rootNode::1 # use a unique root node in the noun and verb taxonomies. # Use the value 0 if you don't want a unique root node. # If the value is omitted, then 1 is assumed. The only # valid values are 0 and 1. # infocontent::lib/WordNet/ic-semcor.dat # use the information content file generated from # SemCor using the sense tags. The value of this # parameter must be the path to an information content # file. Sample infocontent files are in the # samples/Infocontent directory. By default, the # information content measures use ic-semcor.dat WordNet-Similarity-2.05/samples/config-files/config-vector_pairs.conf0000644000175000017500000000422611015336623024115 0ustar sidsidWordNet::Similarity::vector_pairs # A sample configuration file for the vector_pairs measure trace::0 # turn off (0) tracing. Turn on tracing by setting # this to 1 or 2. The default value is off. If the value # is omitted, then 1 is assumed. 0, 1, and 2 are # the only valid settings for this option. cache:: # turn on caching. Turn off caching by setting this # to 0. The default is on. If the value is omitted, # then 1 is assumed. 0 and 1 are the only valid # settings. maxCacheSize::1000 # limit the cache size to 1000 pairs of query words. # The default is 5000. The value of this option # must be a non-negative integer or "unlimited" (without # the quotes). stem:: # turn stemming on. The value of this option must be # either 0 or 1. If the value is omitted, then 1 is # assumed. When stemming is on (1), all the words of # a gloss are stemmed before overlaps are determined. # If the value is 0, then no stemming is done. relation::samples/vector-pairs-relation.dat # This option specifies the vector relation file # to be used. The value can be either a relative # or an absolute path. The vector_pairs measure by # default does not need a relation file and uses # glosses with examples. N.B., the format of a # vector_pairs relation file is the same as the format # of the lesk relation file. stop::samples/stoplist.txt # This option specifies the path to a list of stop words # that should be ignored for the gloss overlaps. The # value can be either a relative or an absolute path. # The default is to not use a stop list; therefore, no # words are ignored. vectordb::lib/WordNet/wordvectors.dat # specifies the path to a database file containing # word vectors. The value can be either a relative or # an absolute path. The program wordVectors.pl in # /utils can be used to generate a file. compounds::samples/wn30compounds.txt # specifies a list of compound words in WordNet 3.0. # The value can be either a relative or an absolute path. # The program compounds.pl in /utils can be used # to generate a list of compounds. WordNet-Similarity-2.05/samples/config-files/config-path.conf0000644000175000017500000000170011015336623022343 0ustar sidsidWordNet::Similarity::path # A sample configuration file for the path measure trace::0 # turn off (0) tracing. Turn on tracing by setting # this to 1 or 2. The default value is off. If the value # is omitted, then 1 is assumed. 0, 1, and 2 are # the only valid settings for this option. cache:: # turn on caching. Turn off caching by setting this # to 0. The default is on. If the value is omitted, # then 1 is assumed. 0 and 1 are the only valid # settings. maxCacheSize::1000 # limit the cache size to 1000 pairs of query words. # The default is 5000. The value of this option # must be a non-negative integer or "unlimited" (without # the quotes). rootNode::1 # use a unique root node in the noun and verb taxonomies. # Use the value 0 if you don't want a unique root node. # If the value is omitted, then 1 is assumed. The only # valid values are 0 and 1. WordNet-Similarity-2.05/samples/config-files/config-random.conf0000644000175000017500000000161511015336623022674 0ustar sidsidWordNet::Similarity::random # A sample configuration file for the random measure trace::0 # turn off (0) tracing. Turn on tracing by setting # this to 1 or 2. The default value is off. If the value # is omitted, then 1 is assumed. 0, 1, and 2 are # the only valid settings for this option. cache:: # turn on caching. Turn off caching by setting this # to 0. The default is on. If the value is omitted, # then 1 is assumed. 0 and 1 are the only valid # settings. maxCacheSize::1000 # limit the cache size to 1000 pairs of query words. # The default is 5000. The value of this option # must be a non-negative integer or "unlimited" (without # the quotes). maxrand::10 # set the maximum number that can be generated to 10. # the value of this option must be a positive, floating- # point number. The default is 1. WordNet-Similarity-2.05/samples/config-files/config-all.conf0000644000175000017500000001506111015336623022164 0ustar sidsidThis is a master list of comments as used in the example configuration files found in this directory. This is not intended to be used as a configuration file but rather as a plain text summary of possible options and their values. In fact, the measures will not accept this as a configuration file. All of these options have default values that are described below. The only exception to this is vectordb, which has no default. If an option is listed without a value (as in trace:: or cache:: ), then the default value is used. Note that in the configuration files anything following a # is treated as a comment, so the following text can be used directly in a configuration file. You will want to make sure to change the value of an option as fits your needs however! # ---------------------------------------------------------------------- # The following options are supported for all measures trace::0 # Turns off (0) tracing. Turn on tracing by setting # to 1 or 2. The effect of these different levels will # depend on the measure being used. The default value # is off (0). If the value is omitted, then the default # is used. 0, 1, and 2 are the only valid settings. cache::1 # Turns on (1) caching. Turn off caching by setting # to 0. The default is on (1). If the value is omitted, # then the default is used. 0 and 1 are the only valid # settings. maxCacheSize::1000 # Limit the cache size to 1000 pairs of query words. # The default is 5000. If the value is omitted, then # the default is used. The value of this option # must be a non-negative integer or "unlimited" (without # the quotes). # ---------------------------------------------------------------------- # The following option is supported by : # path, lch, wup, res, lin, jcn rootNode::1 # Turns on (1) a (hypothetical) top-level root node for # the nouns, and another for the verbs. Turn off the # root nodes by setting to 0. The default is to use (1) # a unique top-level root node. If the value is omitted, # then the default is used. 0 and 1 are the only valid # settings. # ---------------------------------------------------------------------- # The following option is supported by : # res, lin, jcn infocontent::lib/WordNet/infocontent.dat # Specifies an information content file. The value of # this option must be the name of a file, or a relative # or absolute path name. The default value of this option # is $INSTALLDIR/WordNet/ic-semcor.dat, where $INSTALLDIR # is the directory in which the WordNet::Similarity modules # are installed. # ---------------------------------------------------------------------- # The following options are supported by vector and lesk stem::1 # Turns on (1) stemming. Turn off stemming by setting # this value to 0. The default value is on (1). If the # value is omitted, then the default is used. When # stemming is on (1), all the words in a gloss are stemmed # by the WordNet stemmer before overlaps are identified. stop::samples/stoplist.txt # Specifies the name of a stop list, which consists of # words that are to be ignored in a gloss overlap. The # value of this must be a file name, or an absolute or # relative path name. The default is to not use a stop # list. If the value is omitted, then the default is used. # ---------------------------------------------------------------------- # The following options are supported by the lesk measure relation::samples/lesk-relation.dat # Specifies a lesk relation file. This value can be a file # name, or an absolute or relative path name. The default # is to use the file $INSTALLDIR/WordNet/lesk-relation.dat, # where $INSTALLDIR is the directory in which the # WordNet::Similarity modules are installed. If the value is # ommited, then the default is used. Please note that the # format of the lesk relation file is not the same as # that of the vector relation file. The lesk relation file # consists of relation pairs that specify glosses that # are to be compared for overlaps. normalize::1 # Turns on (1) normalization of lesk scoring. Turn off # by setting this value to 0. The default value is off # (0). If the value is omitted, then the default is used. # When normalization is enabled, the gloss overlap score # is normalized by the size of the glosses. The details # are described in Banerjee and Pedersen (2002). # ---------------------------------------------------------------------- # The following options are supported by the vector measure vectordb::lib/WordNet/wordvectors.dat # Specifies a database file containing word vectors. # The value of this option must be a file name, or an # absolute or relative path name. utils/wordVectors.pl # must be used to generate this file. This option is # required, and there is no default value. If the # option is not specified, or if the option is specified # without a value, the vector measure will fail. relation::samples/vector-relation.dat # Specifies a vector relation file. This value can be a file # name, or an absolute or relative path name. The default # is to use the glos-example relation. If the value is # ommited, then the default is used. Please note that the # format of the vector relation file is not the same as # that of the lesk relation file. The vector relation file # consists of single relations that specify which glossess # of a word will be used in constructing the gloss vector. compounds::samples/wn30compounds.txt # Specifies a file of WordNet compounds. The value of # this option must be a file name, or an absolute or # relative path. The program utils /compounds.pl can # be used to generate this file. When compounds are # specified, compound words that occur in glosses are # identified prior to creating word vectors. The default # is to ignore compound words. If the value of this # option is omitted, then the default is used. # ---------------------------------------------------------------------- # The following option is supported by the random measure maxrand::1 # The random measure will generate measures between 0 # and this value. The value of this option may be an # integer or a real number. The default value is 1. # If the value of this option is omitted, then the # default is used. # ---------------------------------------------------------------------- WordNet-Similarity-2.05/samples/config-files/config-lesk.conf0000644000175000017500000000372211015336623022353 0ustar sidsidWordNet::Similarity::lesk # A sample configuration file for the lesk measure. trace::0 # turn off (0) tracing. Turn on tracing by setting # this to 1 or 2. The default value is off. If the value # is omitted, then 1 is assumed. 0, 1, and 2 are # the only valid settings for this option. cache:: # turn on caching. Turn off caching by setting this # to 0. The default is on. If the value is omitted, # then 1 is assumed. 0 and 1 are the only valid # settings. maxCacheSize::1000 # limit the cache size to 1000 pairs of query words. # The default is 5000. The value of this option # must be a non-negative integer or "unlimited" (without # the quotes). stem:: # turn stemming on. The value of this option must be # either 0 or 1. If the value is omitted, then 1 is # assumed. When stemming is on (1), all the words of # a gloss are stemmed before overlaps are determined. # If the value is 0, then no stemming is done. relation::lib/WordNet/lesk-relation.dat # This option specifies the lesk relation file # to be used. The value can be either a relative # or an absolute path. The default is to use # the file lib/WordNet/lesk-relation.dat. N.B., the # format of a lesk relation file is different from the # format of a vector relation file. stop::samples/stoplist.txt # This option specifies the path to a list of stop words # that should be ignored for the gloss overlaps. The # value can be either a relative or an absolute path. # The default is to not use a stop list; therefore, no # words are ignored. normalize::1 # normalize words. The value of this option must be # 0 or 1. If the value is omitted, 1 is assumed. A # value of 0 switches normalization off. When normalization # is enabled, the score obtained by couting the gloss # overlaps is normalized by the size of the glosses. The # details are described in Banerjee and Pedersen (2002). WordNet-Similarity-2.05/samples/config-files/config-vector.conf0000644000175000017500000000416711015336623022723 0ustar sidsidWordNet::Similarity::vector # A sample configuration file for the vector measure trace::0 # turn off (0) tracing. Turn on tracing by setting # this to 1 or 2. The default value is off. If the value # is omitted, then 1 is assumed. 0, 1, and 2 are # the only valid settings for this option. cache:: # turn on caching. Turn off caching by setting this # to 0. The default is on. If the value is omitted, # then 1 is assumed. 0 and 1 are the only valid # settings. maxCacheSize::1000 # limit the cache size to 1000 pairs of query words. # The default is 5000. The value of this option # must be a non-negative integer or "unlimited" (without # the quotes). stem:: # turn stemming on. The value of this option must be # either 0 or 1. If the value is omitted, then 1 is # assumed. When stemming is on (1), all the words of # a gloss are stemmed before overlaps are determined. # If the value is 0, then no stemming is done. relation::samples/vector-relation.dat # This option specifies the vector relation file # to be used. The value can be either a relative # or an absolute path. The vector measure by default # does not need a relation file and uses glosses with # examples. N.B., the format of a vector relation # file is different from the format of the lesk # relation file. stop::samples/stoplist.txt # This option specifies the path to a list of stop words # that should be ignored for the gloss overlaps. The # value can be either a relative or an absolute path. # The default is to not use a stop list; therefore, no # words are ignored. vectordb::lib/WordNet/wordvectors.dat # specifies the path to a database file containing # word vectors. The value can be either a relative or # an absolute path. The program wordVectors.pl in # /utils can be used to generate a file. compounds::samples/wn30compounds.txt # specifies a list of compound words in WordNet 3.0. # The value can be either a relative or an absolute path. # The program compounds.pl in /utils can be used # to generate a list of compounds. WordNet-Similarity-2.05/samples/config-files/config-res.conf0000644000175000017500000000244011015336623022202 0ustar sidsidWordNet::Similarity::res # A sample configuration file for the res measure trace::0 # turn off (0) tracing. Turn on tracing by setting # this to 1 or 2. The default value is off. If the value # is omitted, then 1 is assumed. 0, 1, and 2 are # the only valid settings for this option. cache:: # turn on caching. Turn off caching by setting this # to 0. The default is on. If the value is omitted, # then 1 is assumed. 0 and 1 are the only valid # settings. maxCacheSize::1000 # limit the cache size to 1000 pairs of query words. # The default is 5000. The value of this option # must be a non-negative integer or "unlimited" (without # the quotes). rootNode::1 # use a unique root node in the noun and verb taxonomies. # Use the value 0 if you don't want a unique root node. # If the value is omitted, then 1 is assumed. The only # valid values are 0 and 1. # infocontent::lib/WordNet/ic-semcor.dat # use the information content file generated from # SemCor using the sense tags. The value of this # parameter must be the path to an information content # file. Sample infocontent files are in the # samples/Infocontent directory. By default, the # information content measures use ic-semcor.dat WordNet-Similarity-2.05/samples/config-files/config-hso.conf0000644000175000017500000000134411015336623022204 0ustar sidsidWordNet::Similarity::hso # A sample configuration file for the hso measure trace::1 # turn off (0) tracing. Turn on tracing by setting # this to 1 or 2. The default value is off. If the value # is omitted, then 1 is assumed. 0, 1, and 2 are # the only valid settings for this option. cache:: # turn on caching. Turn off caching by setting this # to 0. The default is on. If the value is omitted, # then 1 is assumed. 0 and 1 are the only valid # settings. maxCacheSize::1000 # limit the cache size to 1000 pairs of query words. # The default is 5000. The value of this option # must be a non-negative integer or "unlimited" (without # the quotes). WordNet-Similarity-2.05/samples/config-files/config-wup.conf0000644000175000017500000000167611015336623022236 0ustar sidsidWordNet::Similarity::wup # A sample configuration file for the wup measure trace::0 # turn off (0) tracing. Turn on tracing by setting # this to 1 or 2. The default value is off. If the value # is omitted, then 1 is assumed. 0, 1, and 2 are # the only valid settings for this option. cache:: # turn on caching. Turn off caching by setting this # to 0. The default is on. If the value is omitted, # then 1 is assumed. 0 and 1 are the only valid # settings. maxCacheSize::1000 # limit the cache size to 1000 pairs of query words. # The default is 5000. The value of this option # must be a non-negative integer or "unlimited" (without # the quotes). rootNode::1 # use a unique root node in the noun and verb taxonomies. # Use the value 0 if you don't want a unique root node. # If the value is omitted, then 1 is assumed. The only # valid values are 0 and 1. WordNet-Similarity-2.05/samples/config-files/config-jcn.conf0000644000175000017500000000244011015336623022163 0ustar sidsidWordNet::Similarity::jcn # A sample configuration file for the jcn measure trace::0 # turn off (0) tracing. Turn on tracing by setting # this to 1 or 2. The default value is off. If the value # is omitted, then 1 is assumed. 0, 1, and 2 are # the only valid settings for this option. cache:: # turn on caching. Turn off caching by setting this # to 0. The default is on. If the value is omitted, # then 1 is assumed. 0 and 1 are the only valid # settings. maxCacheSize::1000 # limit the cache size to 1000 pairs of query words. # The default is 5000. The value of this option # must be a non-negative integer or "unlimited" (without # the quotes). rootNode::1 # use a unique root node in the noun and verb taxonomies. # Use the value 0 if you don't want a unique root node. # If the value is omitted, then 1 is assumed. The only # valid values are 0 and 1. # infocontent::lib/WordNet/ic-semcor.dat # use the information content file generated from # SemCor using the sense tags. The value of this # parameter must be the path to an information content # file. Sample infocontent files are in the # samples/Infocontent directory. By default, the # information content measures use ic-semcor.dat WordNet-Similarity-2.05/samples/vector-pairs-relation.dat0000644000175000017500000000014311015336623021653 0ustar sidsidRelationFile example-example example-glos example-syns glos-glos glosexample-glosexample hype-hype WordNet-Similarity-2.05/samples/millercharles.txt0000644000175000017500000000061111015336623020317 0ustar sidsidcar automobile gem jewel journey voyage boy lad coast shore asylum madhouse magician wizard midday noon furnace stove food fruit bird cock bird crane tool implement brother monk crane implement lad brother journey car monk oracle cemetery woodland food rooster coast hill forest graveyard shore woodland monk slave coast forest lad wizard chord smile glass magician noon string rooster voyage WordNet-Similarity-2.05/samples/vector-relation.dat0000644000175000017500000000013011015336623020533 0ustar sidsidVectorRelationFile also attr example glosexample glos holo hype hypo mero pert sim syns WordNet-Similarity-2.05/samples/lesk-relation.dat0000644000175000017500000000043411015336623020176 0ustar sidsidRelationFile also-attr example-example example-glos example-syns glos-also glos-attr glos-example glos-glos holo-hype holo-hypo holo-mero holo-pert hype-hypo(mero) hype-pert example(hype(hype))-syns(sim) hypo-also hypo-attr hypo-glos hypo-holo hypo-hype mero-hypo mero-mero mero-pert WordNet-Similarity-2.05/samples/README0000644000175000017500000000552111015336623015615 0ustar sidsid /samples README This directory contains a number sample files that demonstrate various aspects of the WordNet::Similarity package and related utilities. We recommend that you save a copy of the files in this directory for future use. Information content =================== The directory /Infocontent is intended to contain a large number of precomputed information content files that can be downloaded from : http://www.d.umn.edu/~tpederse/similarity.html These files are the output of the word counting programs brownFreq.pl, BNCFreq.pl, treebankFreq.pl, etc. and are used by the information content measures: res, lin, and jcn. You will find these files very useful if you plan on using the res, lin, or jcn measures. The file Infocontent/README contains a complete description of the files that are available, and how they were created. Make sure you download the version of the information content files that is for your version of WordNet (for e.g WordNet v3.0). WordNet Compounds ================= The file wn30compounds.txt contains a list of "WordNet compounds" as found in version 3.0 of WordNet. A WordNet compound is any multi word expression that appears in WordNet that includes a _. These are mainly nouns, and include proper nouns (winston_churchill), foreign words (ipso_facto), expressions (face_to_face), etc. This file of compounds are required if you are running one of the information content programs (BNCFreq.pl, treebankFreq.pl, etc.). It is a useful option for the lesk and vector measures as well. stoplist ======== The file stoplist.txt contains a list of stop words. Stop words are words excluded from some natural language processing task because the words are non-informative or misleading. For example the word "a" has several senses in WordNet: the blood type "A", an abbreviation for angstrom, adenine, etc., but most often the word "a" is used as an indefinite article. Configuration files =================== the /config-files sub-directory contains a sample configuration file for each measure module (WordNet::Similarity::res, WordNet::Similairyty::lesk, WordNet::Similarity::wup, etc.). Relation files ============== The files vector-relation.dat and lesk-relation.dat are used by the vector_pairs and lesk measures respectively. Run 'perldoc WordNet::Similarity::lesk' and 'perldoc WordNet::Similarity::vector_pairs' for more information. Word files ========== The file millercharles.txt contains the 30 word pairs used in : Miller and Charles, 1991, Contextual Correlates of Semantic Similarity. Language and Cognitive Processes, 6(1):1-28. The file resnikdiab.txt contains the 27 verb pairs used in : Resnik and Diab, 2000, Measuring Verb Similarity, Appears in the Proceedings of the Twenty Second Annual Meeting of the Cognitive Science Society (COGSCI2000), Philadelphia, August. WordNet-Similarity-2.05/samples/resnikdiab.txt0000644000175000017500000000063311015336623017610 0ustar sidsidbathe kneel chill toughen compose manufacture compress unionize crinkle boggle displease disillusion dissolve dissipate embellish decorate festoon decorate fill inject hack unfold initiate enter lean kneel loosen inflate loosen open neutralize energize obsess disillusion open inflate percolate unionize plunge bathe prick compose swagger waddle unfold divorce wash sap weave enrich whisk deflate wiggle rotate WordNet-Similarity-2.05/utils/0000755000175000017500000000000011025552366014433 5ustar sidsidWordNet-Similarity-2.05/utils/rankFormat.pl0000755000175000017500000001056311020104753017070 0ustar sidsid#! /usr/bin/perl -w # # rankFormat.pl version 2.05 # (Last updated $Id: rankFormat.pl,v 1.6 2008/05/30 23:12:43 sidz1979 Exp $) # # ----------------------------------------------------------------------------- use strict; use Getopt::Long; # Declarations! our ($opt_version, $opt_help); my (@parts, %hash); # Get the options! &GetOptions("version", "help"); # If the version information has been requested... if(defined $opt_version) { &printVersion(); exit; } # If detailed help has been requested... if(defined $opt_help) { &printHelp(); exit; } while(<>) { s/[\r\f\n]//; s/^\s+//; s/\s+$//; @parts = split(/\s+/); $hash{"$parts[0]<>$parts[1]<>"} = sprintf("%.2f", $parts[2]) if(@parts); } print "0\n"; my $lastRank = 1; my $rank = 1; my $last; foreach my $key (sort {$hash{$b} <=> $hash{$a}} keys %hash) { $lastRank = $rank if(defined $last && $hash{$key} != $last); print "$key$lastRank $hash{$key} 0 0 0\n"; $rank++; $last = $hash{$key}; } # ----------------- Subroutines Start Here ---------------------- # Subroutine to print detailed help sub printHelp { &printUsage(); print "\nProgram to rank the output of similarity.pl based on the semantic\n"; print "relatedness of the word pairs. The output is in a format that can\n"; print "processed by the rank.pl program of the Text::NSP package. The rank.pl\n"; print "program takes two lists of ranked pairs of words and computes the\n"; print "correlation between them according to Spearman's correlation\n"; print "coefficient. The input can be specified in file(s) on the command line,\n"; print "or if no file(s) are specified, then STDIN is assumed. Output is written\n"; print "to STDOUT.\n\n"; print "Options: \n"; print "--help Displays this help screen.\n"; print "--version Displays version information.\n\n"; } # Subroutine to print minimal usage notes sub minimalUsageNotes { &printUsage(); print "Type rankFormat.pl --help for detailed help.\n"; } # Subroutine that prints the usage sub printUsage { print "Usage: rankFormat.pl [FILE... |--help | --version]\n"; } # Subroutine to print the version information sub printVersion { print "rankFormat.pl version 2.05\n"; print "Copyright (c) 2006-2008, Ted Pedersen and Siddharth Patwardhan.\n"; } __END__ =head1 NAME rankFormat.pl - Measure the correlation between two different rankings of word pairs measured for semantic relatedness. =head1 SYNOPSIS rankFormat.pl [FILE... | --help | --version] =head1 DESCRIPTION Program to rank the output of similarity.pl based on the semantic relatedness of the word pairs. The output is in a format that can processed by the rank.pl program of the L package. The rank.pl program takes two lists of ranked pairs of words and computes the correlation between them according to Spearman's correlation coefficient. This can be used for comparing the output of two different measures on the same set of word pairs, or for comparing the output of similarity.pl with a human created gold standard (such as the Miller and Charles 30 word pair data). =head1 OPTIONS B<--help> Displays a detailed usage message B<--version> Displays version information. =head1 BUGS Report to WordNet::Similarity mailing list : L =head1 SEE ALSO WordNet home page : L WordNet::Similarity home page : L More info about Text::NSP L =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 COPYRIGHT Copyright (c) 2006-2008, Ted Pedersen and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. =cut WordNet-Similarity-2.05/utils/rawtextFreq.pl0000755000175000017500000004560311021105346017303 0ustar sidsid#! /usr/bin/perl -w # # rawtextFreq.pl version 2.05 # (Last updated $Id: rawtextFreq.pl,v 1.27 2008/06/03 00:04:43 sidz1979 Exp $) # # ----------------------------------------------------------------------------- use strict; use File::Find; use WordNet::Similarity::FrequencyCounter; # Variable declarations my %stopWords; my %offsetFreq; # Some modules used use Getopt::Long; use WordNet::QueryData; use WordNet::Tools; # First check if no commandline options have been provided... in which case # print out the usage notes! if($#ARGV == -1) { &minimalUsageNotes(); exit 1; } our @opt_infiles; our ($opt_version, $opt_help, $opt_stopfile, $opt_outfile); our ($opt_wnpath, $opt_resnik, $opt_smooth, $opt_stdin); # Now get the options! my $ok = GetOptions("version", "help", "stopfile=s", "outfile=s","wnpath=s", "resnik", "smooth=s", "stdin","infile=s" => \@opt_infiles); # GetOptions should have already printed out a detail error message if # $ok is false $ok or die "Error getting command-line arguments\n"; # If the version information has been requested if(defined $opt_version) { &printVersion(); exit 0; } # If detailed help has been requested if(defined $opt_help) { &printHelp(); exit 0; } # make sure either --stdin or --infile was given unless($opt_stdin or scalar @opt_infiles) { minimalUsageNotes(); exit 1; } # make sure that both --stdin and --infile were NOT given if($opt_stdin and scalar @opt_infiles) { minimalUsageNotes(); exit 1; } # make sure --outfile is given if(!defined $opt_outfile) { &minimalUsageNotes(); exit 1; } # Load the stop words if specified if(defined $opt_stopfile) { print STDERR "Loading stoplist... "; open(WORDS, '<', "$opt_stopfile") or die "Couldn't open $opt_stopfile.\n"; while() { s/[\r\f\n]//g; $stopWords{$_} = 1; } close WORDS; print STDERR "done.\n"; } # Get the path to WordNet... my ($wnPCPath, $wnUnixPath); if(defined $opt_wnpath) { $wnPCPath = $opt_wnpath; $wnUnixPath = $opt_wnpath; } elsif(defined $ENV{WNSEARCHDIR}) { $wnPCPath = $ENV{WNSEARCHDIR}; $wnUnixPath = $ENV{WNSEARCHDIR}; } elsif(defined $ENV{WNHOME}) { $wnPCPath = $ENV{WNHOME} . "\\dict"; $wnUnixPath = $ENV{WNHOME} . "/dict"; } else { $wnPCPath = "C:\\Program Files\\WordNet\\3.0\\dict"; $wnUnixPath = "/usr/local/WordNet-3.0/dict"; } # Load up WordNet print STDERR "Loading WordNet... "; my $wn = (defined $opt_wnpath) ? (WordNet::QueryData->new($opt_wnpath)) : (WordNet::QueryData->new()); die "Unable to create WordNet::QueryData object.\n" if(!$wn); $wnPCPath = $wnUnixPath = $wn->dataPath() if(defined $wn->can('dataPath')); my $wntools = WordNet::Tools->new($wn); die "Unable to create WordNet::Tools object.\n" if(!$wntools); print STDERR "done.\n"; # Load the topmost nodes of the hierarchies print STDERR "Loading topmost nodes of the hierarchies... "; my $topHash = WordNet::Similarity::FrequencyCounter::createTopHash($wn); die "Unable to create topHash.\n" if(!defined($topHash)); print STDERR "done.\n"; # Read the input, form sentences and process each # We need to process each sentence (or clause really) at the same time. # This is because process() tries to find compounds in the sentence. It is # not sufficient to process a line at a time because a sentence often # spans more than one line; therefore, a compound could also span a line # break (for example, the first word of a compound could be the last word # on a line, and the second word of the compound could be the first word # on the next line). my $sentence = ""; # check if we're reading from a file or from STDIN if(scalar @opt_infiles) { # first we have to figure out what files to process. The files # are specified with in --infile option. The value of the option # can be a filename, a directory, or a pattern (as understood by # Perl's glob() function). my @infiles = getFiles(@opt_infiles); print STDERR "Computing frequencies...\n"; foreach my $i (0..$#infiles) { my $infile = $infiles[$i]; print STDERR (" Processing '$infile' (", $i + 1, "/",$#infiles + 1, " files)... "); open(IFH, '<', $infile) or die "Cannot open file '$infile': $!"; while(my $line = ) { $line =~ s/[\r\f\n]//g; my @parts = split(/[.?!,;:]/, $line); foreach (1..$#parts) { $sentence .= shift(@parts)." "; &process($sentence); $sentence = ""; } $sentence .= shift(@parts)." " if(@parts); } &process($sentence); close IFH or die "Cannot not close file '$infile': $!"; print STDERR " done.\n"; } } else { print STDERR "Computing frequencies... "; while (my $line = ) { $line =~ s/[\r\f\n]//g; my @parts = split(/[.?!,;:]/, $line); foreach (1..$#parts) { $sentence .= shift(@parts) . " "; &process($sentence); $sentence = ""; } $sentence .= shift(@parts) . " " if @parts; } &process($sentence); print STDERR "done.\n"; } # Smoothing! if(defined $opt_smooth) { print STDERR "Smoothing... "; if($opt_smooth eq 'ADD1') { foreach my $pos ("noun", "verb") { my $localpos = $pos; if(!open(IDX, $wnUnixPath."/data.$pos")) { if(!open(IDX, $wnPCPath."/$pos.dat")) { print STDERR "Unable to open WordNet data files.\n"; exit; } } $localpos =~ s/(^[nv]).*/$1/; while() { last if(/^\S/); } my ($offset) = split(/\s+/, $_, 2); $offset =~ s/^0*//; $offsetFreq{$localpos}{$offset}++; while() { ($offset) = split(/\s+/, $_, 2); $offset =~ s/^0*//; $offsetFreq{$localpos}{$offset}++; } close(IDX); } print STDERR "done.\n"; } else { print STDERR "\nWarning: Unknown smoothing '$opt_smooth'.\n"; print STDERR "Use --help for details.\n"; print STDERR "Continuing without smoothing.\n"; } } # Propagating frequencies up the WordNet hierarchies... print STDERR "Propagating frequencies up through WordNet... "; my $newFreq = WordNet::Similarity::FrequencyCounter::propagateFrequency(\%offsetFreq, $wn, $topHash); print STDERR "done.\n"; # Print the output to file print STDERR "Writing output file... "; open(OUT, ">$opt_outfile") || die "Unable to open $opt_outfile for writing: $!\n"; print OUT "wnver::".$wntools->hashCode()."\n"; foreach my $pos ("n", "v") { foreach my $offset (sort {$a <=> $b} keys %{$newFreq->{$pos}}) { print OUT "$offset$pos ".($newFreq->{$pos}->{$offset}); print OUT " ROOT" if($topHash->{$pos}->{$offset}); print OUT "\n"; } } close(OUT); print STDERR "done.\n"; # ----------------- Subroutines start Here ---------------------- # Processing of each sentence # (1) Convert to lowercase # (2) Remove all unwanted characters # (3) Combine all consequetive occurrence of numbers into one # (4) Remove leading and trailing spaces # (5) Form all possible compounds in the words # (6) Get the frequency counts sub process { my $block; $block = lc(shift); $block =~ s/\'//g; $block =~ s/[^a-z0-9]+/ /g; while($block =~ s/([0-9]+)\s+([0-9]+)/$1$2/g){} $block =~ s/^\s+//; $block =~ s/\s+$//; $block = $wntools->compoundify($block); while($block =~ /([\w_]+)/g) { WordNet::Similarity::FrequencyCounter::updateWordFrequency($1, \%offsetFreq, $wn, $opt_resnik) if(!defined $stopWords{$1}); } } sub getFiles { my @inpatterns = @_; my @infiles; # the options to pass to File::Find::find() my %options = ( wanted => sub { unless(-d $File::Find::name) { push @infiles, $File::Find::name; } }, follow_fast => 1 ); foreach my $pattern (@inpatterns) { if(-d $pattern) { find(\%options, $pattern); } elsif(-e $pattern and not -d $pattern) { push @infiles, $pattern; } else { my @files = glob $pattern; foreach my $file (@files) { if(-d $file) { find(\%options, $pattern); } else { push @infiles, $file; } } } } return @infiles; } # Subroutine to print detailed help sub printHelp { &printUsage(); print "\nThis program computes the information content of concepts, by\n"; print "counting the frequency of their occurrence in raw text.\n"; print "Options: \n"; print "--outfile Specifies the output file OUTFILE.\n"; print "--stdin Read the input from the standard input\n"; print "--infile INFILE is the name of an input file\n"; print "--stopfile STOPFILE is a list of stop listed words that will\n"; print " not be considered in the frequency count.\n"; print "--wnpath Option to specify WNPATH as the location of WordNet data\n"; print " files. If this option is not specified, the program tries\n"; print " to determine the path to the WordNet data files using the\n"; print " WNHOME environment variable.\n"; print "--resnik Option to specify that the frequency counting should\n"; print " be performed according to the method described by\n"; print " Resnik (1995).\n"; print "--smooth Specifies the smoothing to be used on the probabilities\n"; print " computed. SCHEME specifies the type of smoothing to\n"; print " perform. It is a string, which can be only be 'ADD1'\n"; print " as of now. Other smoothing schemes will be added in\n"; print " future releases.\n"; print "--help Displays this help screen.\n"; print "--version Displays version information.\n\n"; } # Subroutine to print minimal usage notes sub minimalUsageNotes { &printUsage(); print "Type rawtextFreq.pl --help for detailed help.\n"; } # Subroutine that prints the usage sub printUsage { print <<'EOT'; Usage: rawtextFreq.pl --outfile OUTFILE {--stdin | --infile FILE [--infile FILE ...]} [--stopfile FILE] [--resnik] [--wnpath PATH] [--smooth SCHEME] | --help | --version EOT } # Subroutine to print the version information sub printVersion { print "rawtextFreq.pl version 2.05\n"; print "Copyright (c) 2005-2008, Ted Pedersen, Satanjeev Banerjee, Siddharth Patwardhan and Jason Michelizzi.\n"; } __END__ =head1 NAME rawtextFreq.pl - Compute Information Content from Raw / Plain Text =head1 SYNOPSIS rawtextFreq.pl --outfile OUTFILE [--stopfile=STOPFILE] {--stdin | --infile FILE [--infile FILE ...]} [--wnpath WNPATH] [--resnik] [--smooth=SCHEME] | --help | --version =head1 OPTIONS B<--outfile>=I The name of a file to which output should be written B<--stopfile>=I A file containing a list of stop listed words that will not be considered in the frequency counts. A sample file can be down- loaded from http://www.d.umn.edu/~tpederse/Group01/WordNet/words.txt B<--wnpath>=I Location of the WordNet data files (e.g., /usr/local/WordNet-3.0/dict) B<--resnik> Use Resnik (1995) frequency counting B<--smooth>=I Smoothing should used on the probabilities computed. SCHEME can only be ADD1 at this time B<--help> Show a help message B<--version> Display version information B<--stdin> Read from the standard input the text that is to be used for counting the frequency of words. B<--infile>=I The name of a raw text file to be used to count word frequencies. This can actually be a filename, a directory name, or a pattern (as understood by Perl's glob() function). If the value is a directory name, then all the files in that directory and its subdirectories will be used. If you are looking for some interesting files to use, check out Project Gutenberg: . This option may be given more than once (if more than one file should be used). =head1 DESCRIPTION This program reads a corpus of plain text and computes frequency counts from that corpus and then uses those to determine the information content of each synset in WordNet. In brief it does this by first assigning counts to each synset for which it obtains a frequency count in the corpus, and then those counts are propagated up the WordNet hierarchy. More details on this process can be found in the documentation of the lin, res, and jcn measures in L and in the publication by Patwardhan, et. al. (2003) referred to below. The utility programs L, L, L, L all function in exactly the same way as this plain text program (rawtextFreq.pl), except that they include the ability to deal with the format of the corpus with which they are used. None of these programs requires sense-tagged text; instead they simply distribute the counts of the observed form of word to all the synsets in the corpus to which it could be associated. The different forms of a word are found via the validForms and querySense methods of L. For example, if the observed word is 'bank', then a count is given to the synsets associated with the financial institution, a river shore, the act of turning a plane, etc. =head2 Distributing Counts to Synsets If the corpora is sense-tagged, then distributing the counts of sense-tagged words to synsets is trivial; you increment the count of each synset for which you have a sense tagged instance. It is very hard to obtain large quantities of sense tagged text, so in general it is not feasible to obtain information content values from large sense-tagged corpora. As such this program and the related *Freq.pl utilities are all trying to increment the counts of synsets based on the occurence of raw untagged word forms. In this case it is less obvious how to proceed. This program supports two methods for distributing the counts of an observed word forms in untagged text to synsets. One is our default method, and we refer to the other as Resnik counting. In our default counting scheme, each synset receives the total count of each word form associated with it. Suppose the word 'bank' can be associated with six different synets. In our default scheme each of those synsets would receive a count for each occurrence of 'bank'. In Resnik counting, the count would be divided between the possible synsets, so in this case each synset would get one sixth (1/6) of the total count. =head2 How are These Counts Used? This program maps word forms to synsets. These synset counts are then propagated up the WordNet hierarchy to arrive at Information Content values for each synset, which are then used by the Lin (lin), Resnik (res), and Jiang & Conrath (jcn) measures of semantic similarity. By default these measures use counts derived from the cntlist file provided by WordNet, which is based on frequency counts from the sense-tagged SemCor corpus. This consists of approximately 200,000 sense tagged tokens taken from the Brown Corpus and the Red Badge of Courage. A file called ic-semcor.dat is created during installation of L from cntlist. In fact, the util program semCorFreq.pl is used to do this. This is the only one of the *Freq.pl utility programs that uses sense tagged text, and in fact it only uses the counts from cntlist, not the actual sense tagged text. This program simply creates an alternative version of the ic-semcor.dat file based on counts obtained from raw untagged text. =head2 Why Use This Program? The default information content file (ic-semcor.dat) is based on SemCor, which includes sense tagged portions of the Brown Corpus and the Red Badge of Courage. It has the advantage of being sense tagged, but is from a rather limited domain and is somewhat small in size (200,000 sense tagged tokens). If you are working in a different domain or have access to a larger quantity of corpora, you might find that this program provides information content values that better reflect your underlying domain or problem. =head2 How can these counts be reliable if they aren't based on sense tagged text? Remember once the counts are given to a synset, those counts are propogated upwards, so that each synset receives the counts of its children. These are then used in the calculation of the information content of each synset, which is simply : information content (synset) = - log [probability (synset)] More details on this calculation and how they are used in the res, lin, and jcn measures can be found in the WordNet::Similarity module doumentation, and in the following publication: Using Measures of Semantic Relatedness for Word Sense Disambiguation (Patwardhan, Banerjee and Pedersen) - Appears in the Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics, pp. 241-257, February 17-21, 2003, Mexico City. L We believe that a propagation effect will result in concentrations or clusters of information content values in the WordNet hierarchy. For example, if you have a text about banking, while the different counts of "bank" will be dispersed around WordNet, there will also be other financial terms that occur with bank that will occur near the financial synset in WordNet, and lead to a concentration of counts in that region of WordNet. It is best to view this as a conjecture or hypothesis at this time. Evidence for or against would be most interesting. You can use raw text of any kind in this program. We sometimes use text from Project Gutenburg, for example the Complete Works of Shakespeare, available from L =head1 BUGS Report to WordNet::Similarity mailing list : L =head1 SEE ALSO L WordNet home page : L WordNet::Similarity home page : L =head1 AUTHORS Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Jason Michelizzi =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen, Satanjeev Banerjee, Siddharth Patwardhan and Jason Michelizzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to Free Software Foundation, Inc. 59 Temple Place - Suite 330 Boston, MA 02111-1307, USA =cut WordNet-Similarity-2.05/utils/brownFreq.pl0000755000175000017500000002442611020104753016734 0ustar sidsid#! /usr/bin/perl -w # # brownFreq.pl version 2.05 # (Last updated $Id: brownFreq.pl,v 1.18 2008/05/30 23:12:43 sidz1979 Exp $) # # ----------------------------------------------------------------------------- # Some modules used use strict; use Getopt::Long; use WordNet::QueryData; use WordNet::Tools; use WordNet::Similarity::FrequencyCounter; # Variable declarations my %stopWords; my %offsetFreq; # First check if no commandline options have been provided... in which case # print out the usage notes! if ($#ARGV == -1) { &minimalUsageNotes(); exit; } # Now get the options! our ($opt_version, $opt_help, $opt_stopfile, $opt_outfile, $opt_wnpath, $opt_resnik, $opt_smooth); &GetOptions("version", "help", "stopfile=s", "outfile=s", "wnpath=s", "resnik", "smooth=s"); # If the version information has been requested if(defined $opt_version) { &printVersion(); exit; } # If detailed help has been requested if(defined $opt_help) { &printHelp(); exit; } # Output file must be specified unless(defined $opt_outfile) { &minimalUsageNotes(); exit; } # Get the path to WordNet... my ($wnPCPath, $wnUnixPath); if(defined $opt_wnpath) { $wnPCPath = $opt_wnpath; $wnUnixPath = $opt_wnpath; } elsif (defined $ENV{WNSEARCHDIR}) { $wnPCPath = $ENV{WNSEARCHDIR}; $wnUnixPath = $ENV{WNSEARCHDIR}; } elsif (defined $ENV{WNHOME}) { $wnPCPath = $ENV{WNHOME} . "\\dict"; $wnUnixPath = $ENV{WNHOME} . "/dict"; } else { $wnPCPath = "C:\\Program Files\\WordNet\\3.0\\dict"; $wnUnixPath = "/usr/local/WordNet-3.0/dict"; } # Load the stop words if specified if(defined $opt_stopfile) { print STDERR "Loading stoplist... "; open(WORDS, "$opt_stopfile") || die("Couldnt open $opt_stopfile.\n"); while () { s/[\r\f\n]//g; $stopWords{$_} = 1; } close WORDS; print STDERR "done.\n"; } # Load up WordNet print STDERR "Loading WordNet... "; my $wn=(defined $opt_wnpath)? (WordNet::QueryData->new($opt_wnpath)):(WordNet::QueryData->new()); die "Unable to create WordNet::QueryData object.\n" if(!$wn); $wnPCPath = $wnUnixPath = $wn->dataPath() if($wn->can('dataPath')); my $wntools = WordNet::Tools->new($wn); die "Unable to create WordNet::Tools object.\n" if(!$wntools); print STDERR "done.\n"; # Load the topmost nodes of the hierarchies print STDERR "Loading topmost nodes of the hierarchies... "; my $topHash = WordNet::Similarity::FrequencyCounter::createTopHash($wn); print STDERR "done.\n"; # Read the input, form sentences and process each print STDERR "Computing frequencies... "; my $sentence = ""; my $line = ""; while($line=<>) { $line=~s/[\r\f\n]//g; $line=~s/^[A-Z]+[0-9]+ [0-9]+ //; my @parts = split(/[.?!]/, $line); foreach (1..$#parts) { $sentence .= shift(@parts)." "; &process($sentence); $sentence = ""; } $sentence .= shift(@parts)." " if(@parts); } &process($sentence); print STDERR "done.\n"; # Smoothing! if(defined $opt_smooth) { print STDERR "Smoothing... "; if($opt_smooth eq 'ADD1') { foreach my $pos ("noun", "verb") { my $localpos = $pos; if(!open(IDX, $wnUnixPath."/data.$pos")) { if(!open(IDX, $wnPCPath."/$pos.dat")) { print STDERR "Unable to open WordNet data files.\n"; exit; } } $localpos =~ s/(^[nv]).*/$1/; while() { last if(/^\S/); } my ($offset) = split(/\s+/, $_, 2); $offset =~ s/^0*//; $offsetFreq{$localpos}{$offset}++; while() { ($offset) = split(/\s+/, $_, 2); $offset =~ s/^0*//; $offsetFreq{$localpos}{$offset}++; } close(IDX); } print STDERR "done.\n"; } else { print STDERR "\nWarning: Unknown smoothing '$opt_smooth'.\n"; print STDERR "Use --help for details.\n"; print STDERR "Continuing without smoothing.\n"; } } # Propagating frequencies up the WordNet hierarchies... print STDERR "Propagating frequencies up through WordNet... "; my $newFreq = WordNet::Similarity::FrequencyCounter::propagateFrequency(\%offsetFreq, $wn, $topHash); print STDERR "done.\n"; # Print the output to file print STDERR "Writing output file... "; open(OUT, ">$opt_outfile") || die "Unable to open $opt_outfile for writing.\n"; print OUT "wnver::".$wntools->hashCode()."\n"; foreach my $pos ("n", "v") { foreach my $offset (sort {$a <=> $b} keys %{$newFreq->{$pos}}) { print OUT "$offset$pos $newFreq->{$pos}->{$offset}"; print OUT " ROOT" if($topHash->{$pos}->{$offset}); print OUT "\n"; } } close(OUT); print "done.\n"; # ----------------- Subroutines start Here ---------------------- # Processing of each sentence # (1) Convert to lowercase # (2) Remove all unwanted characters # (3) Combine all consequetive occurrence of numbers into one # (4) Remove leading and trailing spaces # (5) Form all possible compounds in the words # (6) Get the frequency counts sub process { my $block; $block = lc(shift); $block =~ s/\'//g; $block =~ s/[^a-z0-9]+/ /g; while($block =~ s/([0-9]+)\s+([0-9]+)/$1$2/g){} $block =~ s/^\s+//; $block =~ s/\s+$//; $block = $wntools->compoundify($block); while($block =~ /([\w_]+)/g) { WordNet::Similarity::FrequencyCounter::updateWordFrequency($1, \%offsetFreq, $wn, $opt_resnik) if(!defined $stopWords{$1}); } } # Subroutine to print detailed help sub printHelp { &printUsage(); print "\nThis program computes the information content of concepts, by\n"; print "counting the frequency of their occurrence in the Brown Corpus.\n"; print "Options: \n"; print "--outfile Specifies the output file OUTFILE.\n"; print "--stopfile STOPFILE is a list of stop listed words that will\n"; print " not be considered in the frequency count.\n"; print "--wnpath Option to specify WNPATH as the location of WordNet data\n"; print " files. If this option is not specified, the program tries\n"; print " to determine the path to the WordNet data files using the\n"; print " WNHOME environment variable.\n"; print "--resnik Option to specify that the frequency counting should\n"; print " be performed according to the method described by\n"; print " Resnik (1995).\n"; print "--smooth Specifies the smoothing to be used on the probabilities\n"; print " computed. SCHEME specifies the type of smoothing to\n"; print " perform. It is a string, which can be only be 'ADD1'\n"; print " as of now. Other smoothing schemes will be added in\n"; print " future releases.\n"; print "--help Displays this help screen.\n"; print "--version Displays version information.\n\n"; } # Subroutine to print minimal usage notes sub minimalUsageNotes { &printUsage(); print "Type brownFreq.pl --help for detailed help.\n"; } # Subroutine that prints the usage sub printUsage { print "brownFreq.pl [{--outfile OUTFILE [--stopfile STOPFILE]"; print " [--wnpath WNPATH] [--resnik] [--smooth SCHEME] [FILE...] | --help | --version }]\n"; } # Subroutine to print the version information sub printVersion { print "brownFreq.pl version 2.05\n"; print "Copyright (c) 2005-2008, Ted Pedersen, Satanjeev Banerjee and Siddharth Patwardhan.\n"; } __END__ =head1 NAME brownFreq.pl - Compute Information Content from the Brown Corpus =head1 SYNOPSIS brownFreq.pl [--outfile=OUTFILE [--stopfile=STOPFILE] [--wnpath=WNPATH] [--resnik] [--smooth=SCHEME] FILES | --help --version] =head1 DESCRIPTION This program reads the Brown Corpus and computes the frequency counts for each synset in WordNet. These frequency counts are used by various measures of semantic relatedness to calculate the information content values of concepts. The output is generated in a format as required by the WordNet::Similarity modules for computing semantic relatedness. There are various forms of the Brown corpus available. The one we use for this program comes from the ICAME Collection of English Language Corpora, Second Edition, 1999. L A more detailed description of how information content is calculated can be found in L. This program uses exactly the same techniques as described there. =head1 OPTIONS B<--outfile>=I The name of a file to which output should be written B<--stopfile>=I A file containing a list of stop listed words that will not be considered in the frequency counts. A sample file can be down- loaded from http://www.d.umn.edu/~tpederse/Group01/WordNet/words.txt B<--wnpath>=I Location of the WordNet data files (e.g., /usr/local/WordNet-3.0/dict) B<--resnik> Use Resnik (1995) frequency counting B<--smooth>=I Smoothing should used on the probabilities computed. SCHEME can only be ADD1 at this time B<--help> Show a help message B<--version> Display version information B Files for the Brown Corpus (e.g., /home/sid/Brown/BROWN1/*.TXT). =head1 BUGS Report to WordNet::Similarity mailing list : L =head1 SEE ALSO L ICAME Corpus : L WordNet home page : L WordNet::Similarity home page : L =head1 AUTHORS Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen, Satanjeev Banerjee, and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. =cut WordNet-Similarity-2.05/utils/wnDepths.pl0000755000175000017500000003262511020104754016564 0ustar sidsid#! /usr/bin/perl -w # # wnDepths.pl version 2.05 # (Last updated $Id: wnDepths.pl,v 1.36 2008/05/30 23:12:44 sidz1979 Exp $) # # --------------------------------------------------------------------- # Use external packages use strict; use warnings; use Getopt::Long; use WordNet::QueryData; use WordNet::Tools; use File::Spec; # Get the command-line arguments our ($opt_wnpath, $opt_outfile, $opt_help, $opt_version); our ($opt_depthfile, $opt_wps, $opt_verbose); my $result = GetOptions("wnpath=s","outfile=s","depthfile=s","help","version","wps","verbose"); # Nothing on command-line unless($result) { showUsage(); exit(1); } # Help requested if($opt_help) { showHelp(); exit(0); } # Version information requested if($opt_version) { showVersion(); exit(0); } # Output to STDOUT undef($opt_outfile) if $opt_outfile and ($opt_outfile eq "-"); # Check output file if($opt_outfile) { open OUTFH, ">$opt_outfile" or die "Cannot open $opt_outfile: $!"; } else { *OUTFH = *STDOUT; } # Check if path to WordNet Data files has been provided ... If so ... save it. my $wnPCPath; my $wnUnixPath; if(defined $opt_wnpath) { $wnPCPath = $opt_wnpath; $wnUnixPath = $opt_wnpath; } elsif (defined $ENV{WNSEARCHDIR}) { $wnPCPath = $ENV{WNSEARCHDIR}; $wnUnixPath = $ENV{WNSEARCHDIR}; } elsif (defined $ENV{WNHOME}) { $wnPCPath = $ENV{WNHOME} . "\\dict"; $wnUnixPath = $ENV{WNHOME} . "/dict"; } else { $wnPCPath = "C:\\Program Files\\WordNet\\3.0\\dict"; $wnUnixPath = "/usr/local/WordNet-3.0/dict"; } # I think the actual OS name for most versions of Windows is 'MSWin32', # even for 64-bit Windows. See here for why: # http://www.perlmonks.org/index.pl?node_id=315372 my $wnpath = ($^O =~ /^MSWin/i) ? $wnPCPath : $wnUnixPath; print STDERR "Loading WordNet::QueryData... "; my $wn = WordNet::QueryData->new($wnpath); unless ($wn) { print STDERR ("failed.\n"); exit(1); } my $wntools = WordNet::Tools->new($wn); unless ($wntools) { print STDERR ("failed.\n"); exit(1); } print STDERR "done\n"; # Find top-level nodes for nouns my %top_level; my $datafile = File::Spec->catfile($wnpath, "data.noun"); $datafile = File::Spec->catfile($wnpath, "noun.dat") if ($^O =~ m/^MSWin/i); open FH, "$datafile" or die "Cannot open $datafile: $!"; my $line; while ($line = ) { next if substr($line, 0, 2) eq " "; next if $line =~ m/\@/; my ($offset) = split /\s+/, $line; $top_level{n}->{$offset} = -1; } close FH; if ($opt_verbose) { print "Offsets of top-level nouns\n",join(", ", keys(%{$top_level{n}})),"\n"; print "There are ", scalar(keys %{$top_level{n}}), " nouns\n"; } # Find top-level nodes for verbs $datafile = File::Spec->catfile($wnpath, "data.verb"); $datafile = File::Spec->catfile($wnpath, "verb.dat") if ($^O =~ /MSWin/i); open FH, "$datafile" or die "Cannot open $datafile: $!"; while($line = ) { next if substr($line, 0, 2) eq " "; next if $line =~ m/\@/; my ($offset) = split /\s+/, $line; $top_level{v}->{$offset} = -1; } if ($opt_verbose) { print "Offsets of top-level verbs\n", join(", ", keys(%{$top_level{v}})),"\n"; print "There are ", scalar(keys(%{$top_level{v}})), " top-level verbs.\n"; } # Determine WordNet version my $wnver = $wntools->hashCode(); print OUTFH "wnver::$wnver\n"; # Find the leaf nodes my $noun_leafs_ref = findLeafs('n'); if ($opt_verbose) { print "There are ", scalar(@{$noun_leafs_ref}), " noun leafs.\n"; } my $verb_leafs_ref = findLeafs('v'); if ($opt_verbose) { print "There are ", scalar(@{$verb_leafs_ref}), " verb leafs.\n"; } # Find the depth of every taxonomy my %wpsDepths; print STDERR "Finding depths of noun taxonomies... "; foreach my $offset (@{$noun_leafs_ref}) { my ($depth, $root_offset) = findDepth($offset, 'n'); $root_offset = sprintf("%08d", $root_offset); if (!defined($top_level{n}->{$root_offset}) || $top_level{n}->{$root_offset} < $depth) { $top_level{n}->{$root_offset} = $depth; } } print STDERR "done.\n"; # Generate depth-file my %depth; if ($opt_depthfile) { print STDERR "Writing depths to $opt_depthfile... "; open DFH, '>', $opt_depthfile or die "Cannot open $opt_depthfile: $!"; print DFH "wnver::$wnver\n"; my $noundepth = 0; my @keys = sort keys %wpsDepths; foreach my $wps (@keys) { my $depth = 100; for (@{$wpsDepths{$wps}}) { $depth = $_->[0] if $depth > $_->[0]; } $noundepth = $depth if $depth > $noundepth; } $noundepth = 2 * ($noundepth + 1) - 1; foreach my $key (@keys) { my %tmp; foreach (@{$wpsDepths{$key}}) { if ($opt_wps) { $tmp{"$_->[0]:$_->[1]"} = 1; } else { my $offset = sprintf("%08d", $wn->offset($_->[1])); $tmp{"$_->[0]:$offset"} = 1; } } my @depths = sort keys %tmp; my $offset = sprintf("%08d", $wn->offset($key)); my $str = $opt_wps ? "n $key " : "n $offset "; $str .= join(" ", @depths) . "\n"; $depth{n}->{$offset} = $str; } print STDERR "done.\n"; } print STDERR "Cleaning junk from memory... "; undef %wpsDepths; print STDERR "done.\n"; # Find the depth of every taxonomy print STDERR "Finding depths of verb taxonomies... "; foreach my $offset (@{$verb_leafs_ref}) { my ($depth, $root_offset) = findDepth($offset, 'v'); $root_offset = sprintf("%08d", $root_offset); if (!defined($top_level{v}->{$root_offset}) || $top_level{v}->{$root_offset} < $depth) { $top_level{v}->{$root_offset} = $depth; } } print STDERR "done.\n"; # Generate depth-file if ($opt_depthfile) { print STDERR "Writing depths to $opt_depthfile... "; my $verbdepth = 0; my @keys = sort keys %wpsDepths; foreach my $wps (@keys) { my $depth = 100; for (@{$wpsDepths{$wps}}) { $depth = $_->[0] if $depth > $_->[0]; } $verbdepth = $depth if $depth > $verbdepth; } $verbdepth = 2 * ($verbdepth + 1) - 1; foreach my $key (@keys) { my %tmp; foreach (@{$wpsDepths{$key}}) { if ($opt_wps) { $tmp{"$_->[0]:$_->[1]"} = 1; } else { my $offset = sprintf("%08d", $wn->offset($_->[1])); $tmp{"$_->[0]:$offset"} = 1; } } my @depths = sort keys %tmp; my $offset = sprintf("%08d", $wn->offset($key)); my $str = $opt_wps ? "v $key " : "v $offset "; $str .= join(" ", @depths) . "\n"; $depth{v}->{$offset} = $str; } for my $pos (qw/n v/) { foreach my $key (sort keys %{$depth{$pos}}) { print DFH $depth{$pos}->{$key}; } } print STDERR "done.\n"; close DFH; } print STDERR "Cleaning junk from memory... "; undef %wpsDepths; print STDERR "done.\n"; # Print output my $deepest_n = 0; while (my ($off, $depth) = each %{$top_level{n}}) { $deepest_n = $depth if ($depth > $deepest_n); if ($opt_wps) { my $wps = $wn->getSense($off, 'n'); print OUTFH "n $wps $depth\n"; } else { print OUTFH "n $off $depth\n"; } } $deepest_n++; print OUTFH "n ", ($opt_wps ? '*Root*#n#1' : '00000000'), " $deepest_n\n"; my $deepest_v = 0; while (my ($off, $depth) = each %{$top_level{v}}) { $deepest_v = $depth if ($depth > $deepest_v); if ($opt_wps) { my $wps = $wn->getSense($off, 'v'); print OUTFH "v $wps $depth\n"; } else { print OUTFH "v $off $depth\n"; } } $deepest_v++; print OUTFH "v ", ($opt_wps ? '*Root*#v#1' : '00000000'), " $deepest_v\n"; exit; ########## subroutines follow ########## sub findLeafs { my $pos = shift || die "No pos specifed"; my $file; if ($pos eq "n") { $file = File::Spec->catfile($wnpath, "data.noun"); $file = File::Spec->catfile($wnpath, "noun.dat") if $^O =~ /MSWin/i; } elsif ($pos eq "v") { $file = File::Spec->catfile($wnpath, "data.verb"); $file = File::Spec->catfile($wnpath, "verb.dat") if $^O =~ /MSWin/i; } else { die "Invalid pos: $pos"; } open WN, $file or die "Cannot open $file"; my @rtr = (); while (my $line = ) { next if index($line, " ") == 0; # Was: next if $line =~ m/~/; # Failed on tilde#n#1 because the gloss contains ~. # Fix provided by Ben Haskell (03/04/08). next if $line =~ m/~.*\|/; my ($offset) = split /\s+/, $line; push @rtr, $offset; } close WN or warn "Cannot close $file"; return \@rtr; } sub findWPSDepths { my $wps = shift; my $curPath = shift; defined $wpsDepths{$wps} and return @{$wpsDepths{$wps}}; my @hypernyms = $wn->querySense($wps, "hypes"); $curPath->{$wn->offset($wps)} = 1; unless (scalar @hypernyms > 0) { $wpsDepths{$wps} = [[1, $wps]]; } else { my @all_paths = (); foreach my $hype (@hypernyms) { unless(defined($curPath->{$wn->offset($hype)})) { my %pathCopy = %{$curPath}; push @all_paths, findWPSDepths($hype, \%pathCopy); } } @all_paths = map {[$_->[0] + 1, $_->[1]]} @all_paths; push(@all_paths, [1, $wps]) if(scalar(@all_paths) <= 0); $wpsDepths{$wps} = \@all_paths; } return @{$wpsDepths{$wps}}; } sub findDepth { my ($offset, $pos) = @_; ($offset and $pos) or die "Internal error: bad input to findDepth($offset, $pos)"; my ($wps) = $wn->getSense($offset, $pos); $wps or die "Internal error: bad offset $offset"; #my ($depth, $root) = findWPSDepths ($wps); my @paths = findWPSDepths($wps, {}); my $mindepth = 1_000; my $root; foreach my $path (@paths) { if ($path->[0] < $mindepth) { $mindepth = $path->[0]; $root = $path->[1]; } } my $root_offset; eval {$root_offset = $wn->offset($root)}; if ($@) { die "$@ \t $root (depth $mindepth) has no offset, baseoffset is $offset"; } return ($mindepth, $wn->offset($root)); } sub showUsage { print "Usage: wnDepths.pl [[--wnpath=PATH] [--outfile=FILE] [--depthfile=FILE] [--wps] [--verbose]]\n"; print " | --help | --version]\n"; } sub showHelp { showUsage(); print "Options:\n"; print "\t--wnpath=PATH PATH is the path to WordNet. The default is\n"; print "\t /usr/local/WordNet-3.0/dict on Unix and\n"; print "\t C:\\WordNet\\3.0\\dict on Windows\n"; print "\t--outfile=FILE File to which the maximum depths of the taxon-\n"; print "\t omies should be output.\n"; print "\t--depthfile=FILE File to which the depth of every synset should\n"; print "\t be output\n"; print "\t--wps output is in 'word#part_of_speech#sense format\n"; print "\t instead of offset format\n"; print "\t--verbose be verbose\n"; print "\t--help show this help message\n"; print "\t--version show version information\n"; } sub showVersion { print "wnDepths.pl version 2.05\n"; print "Copyright (c) 2005-2008, Ted Pedersen, Jason Michelizzi and Siddharth Patwardhan\n\n"; print "This program comes with ABSOLUTELY NO WARRANTY. This program\n"; print "is free software, and you are welcome to redistribute it under\n"; print "certain conditions. See the file GPL.txt for warranty and\n"; print "copyright information.\n"; } 1; __END__ =head1 NAME wnDepths.pl - Find the depths of WordNet taxonomies =head1 SYNOPSIS wnDepths.pl [[--wnpath=PATH] [--outfile=FILE|-] [--depthfile=FILE] [--wps] [--verbose]] | --help | --version] =head1 DESCRIPTION B finds the depths of WordNet's noun and verb taxonomies; it can also find the depth of each synset in WordNet. This program was originally written for use with the WordNet::Similarity::lch and WordNet::Similarity::wup measures of semantic similarity, but it likely has other uses as well. =head1 OPTIONS B<--wnpath>=I The path to WordNet data files. The default is /usr/local/WordNet-3.0/dict on Unix and C:\WordNet\3.0\dict on Windows. B<--outfile>=I The file to which the maximum depths of the taxomomies should be output. If this option is not given, or if the file name is I<->, then output will be sent to the standard output. B<--depthfile>=I The file to which the depth of every noun and verb synset should be sent. B<--wps> The names of synsets in the output as "word#part_of_speech#sense" strings rather than as offsets. B<--verbose> Be verbose. B<--help> Show detailed help message. B<--version> Show version information. =head1 BUGS Report to WordNet::Similarity mailing list : L =head1 SEE ALSO L WordNet home page : L WordNet::Similarity home page : L =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Jason Michelizzi Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen, Jason Michelizzi and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/utils/wordVectors.pl0000755000175000017500000003525311020104754017311 0ustar sidsid#! /usr/bin/perl -w # # wordVectors.pl version 2.05 # (Last updated $Id: wordVectors.pl,v 1.19 2008/05/30 23:12:44 sidz1979 Exp $) # # ----------------------------------------------------------------------------- # Use external packages use strict; use Getopt::Long; use WordNet::QueryData; use WordNet::Tools; use WordNet::vectorFile; # Declarations! my $wn; # WordNet::QueryData object. my $wntools; # WordNet::Tools object. my $fh; # Filehandle, to hold data file handles. my $wnPCPath; # Path to WordNet data files (on Windows). my $wnUnixPath; # Path to WordNet data files (on Unix). my $documentCount; # Document Count (Gloss Count). my $saveDocCount; # The value to be written to output file. my $saveDims = {}; # The dimesion hash to be written to file. my $saveMatrix = {}; # The vector matrtix to be written to file. my %rows; # Hash holding the rows of the matrix. my %stopWords; # List of stop words. my %wordMatrix; # Matrix of co-occurrences. my %wordIndex; # List of words word -> index mapping. my %wordTF; # Term Frequency (for each word). my %wordDF; # Document Frequency (for each word). my %wFreq; # Word Frequency. # Get the options! our ($opt_version, $opt_help, $opt_wnpath, $opt_noexamples, $opt_stopfile, $opt_cutoff, $opt_rhigh, $opt_rlow, $opt_chigh, $opt_clow); &GetOptions("version", "help", "wnpath=s", "noexamples", "stopfile=s","cutoff=f", "rhigh=i", "rlow=i", "chigh=i", "clow=i"); # If the version information has been requested... if(defined $opt_version) { &printVersion(); exit; } # If detailed help has been requested... if(defined $opt_help) { &printHelp(); exit; } # If no database filename, error... if(@ARGV) { if(-e $ARGV[0]) { my $getuser; print STDERR "File $ARGV[0] already exists. Overwrite? [y/n] "; $getuser = ; $getuser =~ s/[\r\f\n]//g; $getuser =~ s/\s*//g; if(!($getuser =~ /^y$/ || $getuser =~ /^Y$/)) { print STDERR "Exiting.\n"; exit; } if(!(-w $ARGV[0])) { print STDERR "Unable to overwrite $ARGV[0]. Exiting.\n"; exit; } } } else { print "Specify name of database file.\n"; &printUsage(); exit; } # Load the stop words if specified if(defined $opt_stopfile) { print STDERR "Loading stoplist... "; open(WORDS, "$opt_stopfile") || die("Couldnt open $opt_stopfile.\n"); while () { s/[\r\f\n]//g; s/\s+//g; $stopWords{$_} = 1; } close WORDS; print STDERR "done.\n"; } # Check if path to WordNet Data files has been provided ... If so ... save it. print STDERR "Loading WordNet... "; if(defined $opt_wnpath) { $wnPCPath = $opt_wnpath; $wnUnixPath = $opt_wnpath; $wn = WordNet::QueryData->new($opt_wnpath); } else { if (defined $ENV{WNSEARCHDIR}) { $wnPCPath = $ENV{WNSEARCHDIR}; $wnUnixPath = $ENV{WNSEARCHDIR}; } elsif (defined $ENV{WNHOME}) { $wnPCPath = $ENV{WNHOME} . "\\dict"; $wnUnixPath = $ENV{WNHOME} . "/dict"; } else { $wnPCPath = "C:\\Program Files\\WordNet\\3.0\\dict"; $wnUnixPath = "/usr/local/WordNet-3.0/dict"; } $wn = WordNet::QueryData->new; } die "Unable to create WordNet::QueryData object.\n" if(!$wn); $wnPCPath = $wnUnixPath = $wn->dataPath() if($wn->can('dataPath')); $wntools = WordNet::Tools->new($wn); die "Unable to create WordNet::Tools object.\n" if(!$wntools); print STDERR "done.\n"; print STDERR "Creating word vectors... "; open(NIDX, $wnUnixPath."/data.noun") || open(NIDX, $wnPCPath."\\noun.dat") || die "Unable to open data file.\n"; open(VIDX, $wnUnixPath."/data.verb") || open(VIDX, $wnPCPath."\\verb.dat") || die "Unable to open data file.\n"; open(AIDX, $wnUnixPath."/data.adj") || open(AIDX, $wnPCPath."\\adj.dat") || die "Unable to open data file.\n"; open(RIDX, $wnUnixPath."/data.adv") || open(RIDX, $wnPCPath."\\adv.dat") || die "Unable to open data file.\n"; $documentCount = 0; foreach $fh (*NIDX, *VIDX, *AIDX, *RIDX) { my $line; my $word1; my $word2; my @parts; my @words; my @walk; my %tHash; while($line = <$fh>) { next if ($line =~ m/^\s/); $line =~ s/[\r\f\n]//g; $line =~ s/.*\|//; $line = lc($line); $line =~ s/\".*\"//g if(defined $opt_noexamples); @parts = split(/\;/, $line); @words = (); %tHash = (); while(@parts) { $line = shift(@parts); $line =~ s/\'//g; $line =~ s/[^a-z0-9]+/ /g; $line =~ s/^\s*//; $line =~ s/\s*$//; $line = $wntools->compoundify($line); @walk = split(/\s+/,$line); @walk = &_removeStopWords(@walk) if(defined $opt_stopfile); @walk = &_stem(@walk); @walk = &_removeStopWords(@walk) if(defined $opt_stopfile); push @words, @walk; } foreach $word1 (@words) { $tHash{$word1} = 1; $wordTF{$word1}++; foreach $word2 (@words) { $wordMatrix{$word1}{$word2}++; } } foreach $word1 (keys %tHash) { $wordDF{$word1}++; } $documentCount++; print STDERR "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"; printf STDERR "%6d (of approximately 118000) done.", $documentCount; } } print STDERR "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"; printf STDERR "%6d (of approximately 118000) done.", $documentCount; close(NIDX); close(VIDX); close(AIDX); close(RIDX); print STDERR "\n"; print STDERR "Inconsistent TERMFREQ and DOCFREQ hashes.\n" if(scalar(keys(%wordTF)) != scalar(keys(%wordDF))); # Pruning columns... %rows = %wordTF; if(defined $opt_chigh || defined $opt_clow) { print STDERR "Pruning columns... "; my $word; my %tmpTF = %wordTF; my %tmpDF = %wordDF; %wordTF = (); %wordDF = (); my $c = 0; foreach $word (sort {$tmpTF{$b} <=> $tmpTF{$a}} keys %tmpTF) { next if(defined $opt_chigh && $tmpTF{$word} > $opt_chigh); last if(defined $opt_clow && $tmpTF{$word} < $opt_clow); $wordTF{$word} = $tmpTF{$word}; $wordDF{$word} = $tmpDF{$word}; $c++; } print STDERR "done.\n"; } # Assigning indices to words... pruning cut-off words... print STDERR "Writing dimensions... "; my $word; my @words = keys %wordTF; my $final = scalar(@words); my $c = 0; foreach $word (@words) { if(defined $opt_cutoff && &_tfidf($word) > $opt_cutoff) { delete $wordTF{$word}; delete $wordDF{$word}; next; } $wordIndex{$word} = $c; $saveDims->{$word} = "$c $wordTF{$word} $wordDF{$word}"; print STDERR "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"; printf STDERR "%6d of %6d done.", $c, $final; $c++; } print STDERR "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"; printf STDERR "%6d of %6d done.", $c, $final; print STDERR "\n"; # Write out the document count... print STDERR "Writing the document count... "; $saveDocCount = $documentCount; print STDERR "done.\n"; # Writing out the Word Vectors to the database... print STDERR "Writing word vectors... "; $final = scalar(keys(%wordMatrix)); $c = 0; foreach $word (sort {$rows{$b} <=> $rows{$a}} keys %wordMatrix) { my $key; my $value; next if(defined $opt_rhigh && $opt_rhigh < $rows{$word}); last if(defined $opt_rlow && $opt_rlow > $rows{$word}); $value = ""; foreach $key (keys %{$wordMatrix{$word}}) { if(defined $wordIndex{$key}) { $value .= "$wordIndex{$key} ".($wordMatrix{$word}{$key})." "; } } $value =~ s/\s+$//; $saveMatrix->{$word} = $value; print STDERR "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"; printf STDERR "%6d of %6d done.", $c, $final; $c++; } print STDERR "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"; printf STDERR "%6d of %6d done.", $c, $final; print STDERR "\n"; # Now that everything has been computed, write to file... print STDERR "Writing data to file... "; my $errCode = WordNet::vectorFile->writeVectors($ARGV[0], $saveDocCount, $saveDims, $saveMatrix); if(!defined $errCode || $errCode == 0) { print STDERR "Error writing data to file.\n"; exit; } print STDERR "done.\n"; # ----------------- Subroutines Start Here ---------------------- # Subroutine to stem a list of words... # INPUT PARAMS : @words .. list of words. # RETURN VALUES : @stemmedWords .. Stemmed list. sub _stem { my $word; my @words = @_; my @stemmedWords = (); foreach $word (@words) { my @wnForms = $wn->validForms($word); my @tmp = @wnForms; my $tp; if($#wnForms > 0) { @wnForms = (); foreach $tp (@tmp) { push(@wnForms, $tp) if($tp =~ /\#n$/); } } $word = $wnForms[0] if($#wnForms == 0); $word =~ s/\#.*//; push @stemmedWords, $word; } return @stemmedWords; } # Subroutine to remove stop words... # INPUT PARAMS : @words .. list of words. # RETURN VALUES : @cleanWords .. clean list. sub _removeStopWords { my $word; my $prt; my @words = @_; my @comp; my @cleanWords = (); foreach $word (@words) { @comp = split(/_+/, $word); foreach $prt (@comp) { if(!$stopWords{$prt}) { push @cleanWords, $word; last; } } } return @cleanWords; } # Subroutine to filter words with information content below # a certain cutoff... sub _tfidf { my $word = shift; if(defined $wordTF{$word} && defined $wordDF{$word} && $wordDF{$word} > 0) { return 0 if(!defined $documentCount || $documentCount < 1); return $wordTF{$word}*log($documentCount/$wordDF{$word}); } return 0; } # Subroutine to print detailed help sub printHelp { &printUsage(); print "\nThis program writes out word vectors computed from WordNet glosses in\n"; print "a database file specified by filename DBFILE.\n"; print "Options: \n"; print "--stopfile Option specifying a list of stopwords to not be\n"; print " considered while counting.\n"; print "--wnpath WNPATH specifies the path of the WordNet data files.\n"; print " Ordinarily, this path is determined from the \$WNHOME\n"; print " environment variable. But this option overides this\n"; print " behavior.\n"; print "--noexamples Removes examples from the glosses before processing.\n"; print "--cutoff Option used to restrict the dimensions of the word\n"; print " vectors with an tf/idf cutoff. VALUE is the cutoff\n"; print " above which is an acceptable tf/idf value of a word.\n"; print "--rhigh RHIGH is the upper frequency cutoff of the words\n"; print " selected to have a word-vector entry in the database.\n"; print "--rlow RLOW is the lower frequency cutoff of the words\n"; print " selected to have a word-vector entry in the database.\n"; print "--chigh CHIGH is the upper frequency cutoff of words that form\n"; print " the dimensions of the word-vectors.\n"; print "--clow CLOW is the lower frequency cutoff of words that form\n"; print " the dimensions of the word-vectors.\n"; print "--help Displays this help screen.\n"; print "--version Displays version information.\n\n"; } # Subroutine to print minimal usage notes sub minimalUsageNotes { &printUsage(); print "Type wordVectors.pl --help for detailed help.\n"; } # Subroutine that prints the usage sub printUsage { print "Usage: wordVectors.pl [{ [--stopfile STOPLIST] [--wnpath WNPATH]"; print " [--noexamples] [--cutoff VALUE] [--rhigh RHIGH] [--rlow RLOW] [--chigh CHIGH] [--clow CLOW] DBFILE\n"; print " | --help \n"; print " | --version }]\n"; } # Subroutine to print the version information sub printVersion { print "wordVectors.pl version 2.05\n"; print "Copyright (c) 2005-2008, Ted Pedersen and Siddharth Patwardhan.\n"; } __END__ =head1 NAME wordVectors.pl - Write word vectors from WordNet glosses to a file for use by vector and vector_pairs measures =head1 SYNOPSIS wordVectors.pl [[--stopfile STOPLIST] [--wnpath WNPATH] [--noexamples] [--cutoff VALUE] [--rhigh RHIGH] [--rlow RLOW] [--chigh CHIGH] [--clow CLOW] DBFILE | --help | --version] =head1 DESCRIPTION This program writes out word vectors computed from WordNet glosses in a database file specified by filename DBFILE. The database file is intended for use by the WordNet::Similarity::vector Perl module, but if you can think of something else to do with it, then go ahead. =head1 OPTIONS B<--stopfile>=I Option specifying a list of stopwords to not be considered while counting. B<--wnpath>=I Specifies the path to the WordNet data files. Ordinarily, this path is determined from the $WNHOME environment variable. But this option overides this behavior. B<--noexamples> Removes examples from the glosses before processing. B<--cutoff>=I Option used to restrict the dimensions of the word vectors with an tf/idf cutoff. VALUE is the cutoff above which is an acceptable tf/idf value of a word. B<--rhigh>=I the upper frequency cutoff of the words selected to have a word-vector entry in the database. B<--rlow>=I the lower frequency cutoff of the words selected to have a word-vector entry in the database. B<--chigh>=I the upper frequency cutoff of words that form the dimensions of the word-vectors. B<--clow>=I the lower frequency cutoff of words that form the dimensions of the word-vectors. B<--help> Displays a detailed usage message B<--version> Displays version information. =head1 BUGS Report to WordNet::Similarity mailing list : L =head1 SEE ALSO L WordNet home page : L WordNet::Similarity home page : L =head1 AUTHORS Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to: The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. =cut WordNet-Similarity-2.05/utils/compounds.pl0000755000175000017500000001210711020104754016770 0ustar sidsid#! /usr/bin/perl -w # # compounds.pl version 2.05 # (Last updated $Id: compounds.pl,v 1.13 2008/05/30 23:12:44 sidz1979 Exp $) # # ----------------------------------------------------------------------------- use strict; use Getopt::Long; # Now get the options! our ($opt_version, $opt_help, $opt_wnpath); &GetOptions("version", "help", "wnpath=s"); # If the version information has been requested if(defined $opt_version) { &printVersion(); exit; } # If detailed help has been requested if(defined $opt_help) { &printHelp(); exit; } # Check if path to WordNet Data files has been provided ... If so ... save it. my ($wnPCPath, $wnUnixPath); if(defined $opt_wnpath) { $wnPCPath = $opt_wnpath; $wnUnixPath = $opt_wnpath; } elsif (defined $ENV{WNSEARCHDIR}) { $wnPCPath = $ENV{WNSEARCHDIR}; $wnUnixPath = $ENV{WNSEARCHDIR}; } elsif (defined $ENV{WNHOME}) { $wnPCPath = $ENV{WNHOME} . "\\dict"; $wnUnixPath = $ENV{WNHOME} . "/dict"; } else { $wnPCPath = "C:\\Program Files\\WordNet\\3.0\\dict"; $wnUnixPath = "/usr/local/WordNet-3.0/dict"; } open(NIDX, $wnUnixPath."/index.noun") || open(NIDX, $wnPCPath."\\noun.idx") || die "Unable to open index file.\n"; open(VIDX, $wnUnixPath."/index.verb") || open(VIDX, $wnPCPath."\\verb.idx") || die "Unable to open index file.\n"; open(AIDX, $wnUnixPath."/index.adj") || open(AIDX, $wnPCPath."\\adj.idx") || die "Unable to open index file.\n"; open(RIDX, $wnUnixPath."/index.adv") || open(RIDX, $wnPCPath."\\adv.idx") || die "Unable to open index file.\n"; my $line = ""; while($line = ) { next if " " eq substr $line, 0, 2; $line =~ s/[\r\f\n]//g; $line =~ s/^\s+//; $line =~ s/\s+$//; my ($word) = split(/\s+/, $line, 2); print "$word\n" if($word =~ /_/); } while($line = ) { next if " " eq substr $line, 0, 2; $line =~ s/[\r\f\n]//g; $line =~ s/^\s+//; $line =~ s/\s+$//; my ($word) = split(/\s+/, $line, 2); print "$word\n" if($word =~ /_/); } while($line = ) { next if " " eq substr $line, 0, 2; $line =~ s/[\r\f\n]//g; $line =~ s/^\s+//; $line =~ s/\s+$//; my ($word) = split(/\s+/, $line, 2); print "$word\n" if($word =~ /_/); } while($line = ) { next if " " eq substr $line, 0, 2; $line =~ s/[\r\f\n]//g; $line =~ s/^\s+//; $line =~ s/\s+$//; my ($word) = split(/\s+/, $line, 2); print "$word\n" if($word =~ /_/); } close(NIDX); close(VIDX); close(AIDX); close(RIDX); # Subroutine to print detailed help sub printHelp { &printUsage(); print "\nThis program generates a list of all compound words found\n"; print "in WordNet\n"; print "Options: \n"; print "--wnpath WNPATH specifies the path of the WordNet data files.\n"; print " Ordinarily, this path is determined from the \$WNHOME\n"; print " environment variable. But this option overides this\n"; print " behavior.\n"; print "--help Displays this help screen.\n"; print "--version Displays version information.\n\n"; } # Subroutine to print minimal usage notes sub minimalUsageNotes { &printUsage(); print "Type compounds.pl --help for detailed help.\n"; } # Subroutine that prints the usage sub printUsage { print "compounds.pl [{ --wnpath WNPATH | --help | --version }]\n"; } # Subroutine to print the version information sub printVersion { print "compounds.pl version 2.05\n"; print "Copyright (c) 2005-2008, Ted Pedersen, Satanjeev Banerjee, Siddharth Patwardhan and Jason Michelizzi.\n"; } __END__ =head1 NAME compounds.pl - extract compound words (collocations) from WordNet =head1 SYNOPSIS compounds.pl [--wnpath=PATH | --help | --version] =head1 DESCRIPTION B extracts compound words (collocations) from WordNet and writes the resultant list to the standard output. =head1 OPTIONS B<--wnpath>=I Location of the WordNet data files (e.g., /usr/local/WordNet-3.0/dict) =head1 BUGS Report to WordNet::Similarity mailing list : L =head1 SEE ALSO L WordNet::Similarity home page : L =head1 AUTHORS Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen, Satanjeev Banerjee, and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. =cut WordNet-Similarity-2.05/utils/semCorRawFreq.pl0000755000175000017500000003054111020104753017502 0ustar sidsid#! /usr/bin/perl -w # # semCorRawFreq.pl version 2.05 # (Last updated $Id: semCorRawFreq.pl,v 1.14 2008/05/30 23:12:43 sidz1979 Exp $) # # ----------------------------------------------------------------------------- # Some modules used use strict; use File::Find; use Getopt::Long; use WordNet::QueryData; use WordNet::Tools; use WordNet::Similarity::FrequencyCounter; # Variable declarations my %stopWords; my %offsetFreq; # First check if no commandline options have been provided... in which case # print out the usage notes! if($#ARGV == -1) { &minimalUsageNotes(); exit; } our (@opt_infiles, $opt_version, $opt_help, $opt_stopfile); our ($opt_outfile, $opt_wnpath, $opt_resnik, $opt_smooth, $opt_stdin); # Now get the options! my $okay = GetOptions("version", "help", "stopfile=s","outfile=s", "wnpath=s", "resnik", "smooth=s","stdin", "infile=s" => \@opt_infiles); unless ($okay) { minimalUsageNotes(); exit 1; } # If the version information has been requested if(defined $opt_version) { &printVersion(); exit; } # If detailed help has been requested if(defined $opt_help) { &printHelp(); exit; } # Output file must be specified unless (defined $opt_outfile) { minimalUsageNotes(); exit 1; } # An input source must be specified unless ((defined $opt_stdin or scalar @opt_infiles) and not(defined $opt_stdin and scalar @opt_infiles)) { minimalUsageNotes(); exit 1; } # Get the path to WordNet... my $wnPCPath; my $wnUnixPath; if(defined $opt_wnpath) { $wnPCPath = $opt_wnpath; $wnUnixPath = $opt_wnpath; } elsif (defined $ENV{WNSEARCHDIR}) { $wnPCPath = $ENV{WNSEARCHDIR}; $wnUnixPath = $ENV{WNSEARCHDIR}; } elsif (defined $ENV{WNHOME}) { $wnPCPath = $ENV{WNHOME} . "\\dict"; $wnUnixPath = $ENV{WNHOME} . "/dict"; } else { $wnPCPath = "C:\\Program Files\\WordNet\\3.0\\dict"; $wnUnixPath = "/usr/local/WordNet-3.0/dict"; } # Load the stop words if specified if(defined $opt_stopfile) { print STDERR "Loading stoplist... "; open(WORDS, "$opt_stopfile") || die("Couldn't open $opt_stopfile.\n"); while () { s/[\r\f\n]//g; $stopWords{$_} = 1; } close WORDS; print STDERR "done.\n"; } # Load up WordNet print STDERR "Loading WordNet... "; my $wn = defined $opt_wnpath ? (WordNet::QueryData->new($opt_wnpath)) : (WordNet::QueryData->new()); die "Unable to create WordNet object.\n" if(!$wn); $wnPCPath = $wnUnixPath = $wn->dataPath() if($wn->can('dataPath')); my $wntools = WordNet::Tools->new($wn); die "Unable to create WordNet::Tools object.\n" if(!$wntools); print STDERR "done.\n"; # Load the topmost nodes of the hierarchies print STDERR "Loading topmost nodes of the hierarchies... "; my $topHash = WordNet::Similarity::FrequencyCounter::createTopHash($wn); print STDERR "done.\n"; # Read the input, form sentences and process each if (scalar @opt_infiles) { # first we have to figure out what files to process. The files # are specified with in --infile option. The value of the option # can be a filename, a directory, or a pattern (as understood by # Perl's glob() function). my @infiles = getFiles(@opt_infiles); print STDERR "Computing frequencies... "; foreach my $i (0..$#infiles) { my $infile = $infiles[$i]; print STDERR (" Processing '$infile' (", $i + 1,"/", scalar @infiles, " files)... "); open(IFH, '<', $infile) or die "Cannot open '$infile': $!"; while (my $line = ) { $line =~ s/[\r\f\n]+//g; while ($line =~ /]+)>/g) { my $tagAttribs = $1; if ($tagAttribs =~ /cmd=done/) { if ($tagAttribs =~ /lemma=([^ ]+) /) { WordNet::Similarity::FrequencyCounter::updateWordFrequency($1, \%offsetFreq, $wn, $opt_resnik) unless defined $stopWords{$1}; } } } } close IFH or die "Cannot close file: $!"; print STDERR "done.\n"; } } else { print STDERR "Computing frequencies... "; my $sentence = ""; while(my $line = ) { $line =~ s/[\r\f\n]+//g; while($line =~ /]+)>/g) { my $tagAttribs = $1; if($tagAttribs =~ /cmd=done/) { if($tagAttribs =~ /lemma=([^ ]+) /) { WordNet::Similarity::FrequencyCounter::updateWordFrequency($1, \%offsetFreq, $wn, $opt_resnik) if(!defined $stopWords{$1}); } } } } } print STDERR "done.\n"; # Smoothing! if(defined $opt_smooth) { print STDERR "Smoothing... "; if($opt_smooth eq 'ADD1') { foreach my $pos ("noun", "verb") { my $localpos = $pos; if(!open(IDX, $wnUnixPath."/data.$pos")) { if(!open(IDX, $wnPCPath."/$pos.dat")) { print STDERR "Unable to open WordNet data files.\n"; exit; } } $localpos =~ s/(^[nv]).*/$1/; while() { last if(/^\S/); } my ($offset) = split(/\s+/, $_, 2); $offset =~ s/^0*//; $offsetFreq{$localpos}{$offset}++; while() { ($offset) = split(/\s+/, $_, 2); $offset =~ s/^0*//; $offsetFreq{$localpos}{$offset}++; } close(IDX); } print STDERR "done.\n"; } else { print STDERR "\nWarning: Unknown smoothing '$opt_smooth'.\n"; print STDERR "Use --help for details.\n"; print STDERR "Continuing without smoothing.\n"; } } # Propagating frequencies up the WordNet hierarchies... print STDERR "Propagating frequencies up through WordNet... "; my $newFreq = WordNet::Similarity::FrequencyCounter::propagateFrequency(\%offsetFreq, $wn, $topHash); print STDERR "done.\n"; # Print the output to file print STDERR "Writing output file... "; open(OUT, '>', $opt_outfile) or die "Unable to open '$opt_outfile' for writing."; print OUT "wnver::".$wntools->hashCode()."\n"; foreach my $pos ("n", "v") { foreach my $offset (sort {$a <=> $b} keys %{$newFreq->{$pos}}) { print OUT "$offset$pos $newFreq->{$pos}->{$offset}"; print OUT " ROOT" if($topHash->{$pos}->{$offset}); print OUT "\n"; } } close(OUT); print "done.\n"; # ----------------- Subroutines start Here ---------------------- sub getFiles { my @inpatterns = @_; my @infiles; # the options to pass to File::Find::find() my %options = ( wanted =>sub { unless (-d $File::Find::name) { push @infiles, $File::Find::name; } }, follow_fast => 1 ); foreach my $pattern (@inpatterns) { if (-d $pattern) { find(\%options, $pattern); } elsif (-e $pattern and not -d $pattern) { push @infiles, $pattern; } else { my @files = glob $pattern; foreach my $file (@files) { if (-d $file) { find(\%options, $pattern); } else { push @infiles, $file; } } } } return @infiles; } # Subroutine to print detailed help sub printHelp { &printUsage(); print "\nThis program computes the information content of concepts, by\n"; print "counting the frequency of their occurrence in the SemCor Corpus.\n"; print "Options: \n"; print "--outfile Specifies the output file OUTFILE.\n"; print "--stdin Read input from the standard input.\n"; print "--infile Specifies the name of an input file.\n"; print "--stopfile STOPFILE is a list of stop listed words that will\n"; print " not be considered in the frequency count.\n"; print "--wnpath Option to specify WNPATH as the location of WordNet data\n"; print " files. If this option is not specified, the program tries\n"; print " to determine the path to the WordNet data files using the\n"; print " WNHOME environment variable.\n"; print "--resnik Option to specify that the frequency counting should\n"; print " be performed according to the method described by\n"; print " Resnik (1995).\n"; print "--smooth Specifies the smoothing to be used on the probabilities\n"; print " computed. SCHEME specifies the type of smoothing to\n"; print " perform. It is a string, which can be only be 'ADD1'\n"; print " as of now. Other smoothing schemes will be added in\n"; print " future releases.\n"; print "--help Displays this help screen.\n"; print "--version Displays version information.\n\n"; } # Subroutine to print minimal usage notes sub minimalUsageNotes { printUsage(); print "Type semCorRawFreq.pl --help for detailed help.\n"; } # Subroutine that prints the usage sub printUsage { print <<'EOU'; Usage: semCorRawFreq.pl --outfile FILE {--stdin | --infile FILE [--infile FILE ...]} [--stopfile FILE] [--resnik] [--wnpath PATH] [--smooth SCHEME] | --help | --version EOU } # Subroutine to print the version information sub printVersion { print "semCorRawFreq.pl version 2.05\n"; print "Copyright (c) 2005-2008, Ted Pedersen, Satanjeev Banerjee and Siddharth Patwardhan.\n"; } __END__ =head1 NAME semCorRawFreq.pl - Compute Information Content from SemCor corpus (raw form, without using sense-tags) =head1 SYNOPSIS semCorRawFreq.pl --outfile FILE [--stopfile FILE] [--resnik] [--wnpath PATH] [--smooth SCHEME] {--stdin | --infile FILE [--infile FILE ...]} | --help | --version =head1 DESCRIPTION This program reads SemCor files and computes the frequency counts for each synset in WordNet, ignoring the sense tags in the corpus (treating it like a raw text corpus). These frequency counts are used by various measures of semantic relatedness to calculate the information content values of concepts. The output is generated in a format as required by the WordNet::Similarity modules for computing semantic relatedness. The SemCor data we use comes from Rada Mihalcea, who has mapped SemCor from its original version to each successive version of WordNet. In this program we ignore the SenseTags and simply treat SemCor as raw text. This is to allow for the comparison of the effect of counting from sense tags (as done in L) versus raw or plain word forms (as done here). A more detailed description of how information content is calculated can be found in L. This program uses exactly the same techniques as described there. =head1 OPTIONS B<--outfile>=I The name of a file to which output should be written B<--stopfile>=I A file containing a list of "stop" words that will not be considered during the frequency computation B<--resnik> Use Resnik (1995) frequency counting B<--wnpath>=I Location of the WordNet data files (e.g., /usr/local/WordNet-3.0/dict) B<--smooth>=I Smoothing should used on the probabilities computed. SCHEME can only be ADD1 at this time B<--stdin> Use this flag to provide input via the standard input B<--infile>=I Get the input from the specified file. B<--help> Show a help message B<--version> Display version information =head1 BUGS Report to WordNet::Similarity mailing list : L =head1 SEE ALSO L SemCor Download (from Rada Mihalcea): L WordNet home page : L WordNet::Similarity home page : L =head1 AUTHORS Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen, Satanjeev Banerjee, and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. =cut WordNet-Similarity-2.05/utils/similarity.pl0000755000175000017500000005046611020104753017160 0ustar sidsid#! /usr/bin/perl -w # # similarity.pl version 2.05 # (Last updated $Id: similarity.pl,v 1.30 2008/05/30 23:12:43 sidz1979 Exp $) # # --------------------------------------------------------------------- # Include external packages use strict; use WordNet::QueryData 1.40; use Getopt::Long; use WordNet::Similarity; # If no Command-Line arguments given ... show minimal help screen ... exit. if($#ARGV < 0) { # usage info moved to showUsage() showUsage(); exit(0); } # Get Command-Line options. our ($opt_help, $opt_version, $opt_wnpath, $opt_simpath, $opt_type); our ($opt_config, $opt_file, $opt_trace, $opt_allsenses, $opt_offsets); our ($opt_interact); &GetOptions("help", "version", "wnpath=s", "simpath=s", "type=s","config=s", "file=s", "trace", "allsenses", "offsets","interact"); # To be able to use a local install of similarity modules. if(defined $opt_simpath) { my @tmpINC = @INC; @INC = ($opt_simpath); push(@INC, @tmpINC); } # Check if help has been requested ... If so ... display help. if(defined $opt_help) { &showHelp; exit 0; } # Check if version number has been requested ... If so ... display version. if(defined $opt_version) { &showVersion; exit 0; } # Which similarity measure must be used ... if(!defined $opt_type) { print STDERR "Required switch '--type' missing.\n"; &showUsage; exit 1; } # If the file option has not been provided, then # the two words must be on the command line. # Get the two words if they have been provided. if(!(defined $opt_file) && !(defined $opt_interact) && $#ARGV < 1) { print STDERR "Required parameter(s) missing.\n"; &showUsage; exit 1; } # Initialize the WordNet::QueryData module. print STDERR "Loading WordNet... "; my $wn = (defined $opt_wnpath) ? WordNet::QueryData->new($opt_wnpath) : WordNet::QueryData->new(); if(!$wn) { print STDERR "Unable to create WordNet object.\n"; exit 1; } print STDERR "done.\n"; # Load the WordNet::Similarity module. print STDERR "Loading Module... "; my $type = $opt_type; $opt_type =~ s/::/\//g; $opt_type .= ".pm"; require $opt_type; my $measure = undef; if(defined $opt_config) { $measure = $type->new($wn, $opt_config); } else { $measure = $type->new($wn); } # If object not created. if(!$measure) { print STDERR "Unable to create WordNet::Similarity object.\n"; exit 1; } # If serious error... stop. my ($error, $errorString) = $measure->getError(); if($error > 1) { print STDERR $errorString."\n"; exit 1; } # Set the appropriate trace params. if(defined $opt_trace) { if ($measure->{trace} >= 2) { $opt_offsets = 1; } else { $measure->{'trace'} = ((defined $opt_offsets) ? 2 : 1); } } else { # JM 1-28-04 # The following code was causing problems if a user specifies a trace # level of 2 in a config file but not the --offsets option, then the user # still only sees level 1 traces. #if($measure->{'trace'}) # { # $opt_trace = 1; # $measure->{'trace'} = ((defined $opt_offsets) ? 2 : 1); # } $opt_offsets = 1 if $measure->{trace} >= 2; $opt_trace = $measure->{trace}; } print STDERR "done.\n"; # Get the module initialization parameters. if(defined $opt_trace) { my $loctr = $measure->getTraceString(); print "\n$loctr\n" if($loctr !~ /^\s*$/); } print STDERR $errorString."\n" if($error == 1); # Process the input data... if(defined $opt_interact) { my ($con1, $con2); print "Starting interactive mode (Enter blank fields to end session)...\n"; $con1 = $con2 = "x"; # Hack to start the interactive while loop. while($con1 ne "" && $con2 ne "") { print "Concept \#1: "; $con1 = ; $con1 =~ s/[\r\f\n]//g; $con1 =~ s/^\s+//; $con1 =~ s/\s+$//; last if($con1 eq ""); print "Concept \#2: "; $con2 = ; $con2 =~ s/[\r\f\n]//g; $con2 =~ s/^\s+//; $con2 =~ s/\s+$//; last if($con2 eq ""); print "$con1 $con2\n" if(defined $opt_trace); &process($con1, $con2); print "\n" if(defined $opt_trace); } } elsif(defined $opt_file) { open(DATA, $opt_file) || die "Unable to open file: $opt_file\n"; while() { # 12/9/03 JM (#2) # modified below to remove comments and ensure that each # line contains exactly two words s/[\r\f\n]//g; s/^\s+//; s/^\#.*//; # remove lines beginning with # (i.e., comments) s|//.*||; # remove anything following // (i.e., more comments) s/\s+$//; next if 0 == length; my @words = split /\s+/; unless (scalar(@words) == 2) { print STDERR "Warning: line $. of $opt_file does not contain exactly two words: skipping line.\n"; } else #(scalar(@words) && defined $words[0] && defined $words[1]) { print "$words[0] $words[1]\n" if(defined $opt_trace); &process($words[0], $words[1]); print "\n" if(defined $opt_trace); } } close(DATA); } else { &process(shift, shift); } ## ------------------- Subroutines Start Here ------------------- ## # Subroutine that processes two words (finds relatedness). sub process { my $input1 = shift; my $input2 = shift; my $word1 = $input1; my $word2 = $input2; my $wps; my @w1options; my @w2options; my @senses1; my @senses2; my %distanceHash; if(!(defined $word1 && defined $word2)) { print STDERR "Undefined input word(s).\n"; return; } $word1 =~ s/[\r\f\n]//g; $word1 =~ s/^\s+//; $word1 =~ s/\s+$//; $word1 =~ s/\s+/_/g; $word2 =~ s/[\r\f\n]//g; $word2 =~ s/^\s+//; $word2 =~ s/\s+$//; $word2 =~ s/\s+/_/g; @w1options = &getWNSynsets($word1); @w2options = &getWNSynsets($word2); if(!(scalar(@w1options) && scalar(@w2options))) { print STDERR "'$word1' not found in WordNet.\n" if(!scalar(@w1options)); print STDERR "'$word2' not found in WordNet.\n" if(!scalar(@w2options)); return; } @senses1 = (); @senses2 = (); foreach $wps (@w1options) { if($wps =~ /\#([nvar])\#/) { push(@senses1, $wps) if($measure->{$1}); } } foreach $wps (@w2options) { if($wps =~ /\#([nvar])\#/) { push(@senses2, $wps) if($measure->{$1}); } } if(!scalar(@senses1) || !scalar(@senses2)) { print STDERR "Possible part(s) of speech of word(s) cannot be handled by module.\n"; return; } %distanceHash = &getDistances([@senses1], [@senses2]); if(defined $opt_allsenses) { my $key; print "$input1 $input2 (all senses)\n"; foreach $key (sort {$distanceHash{$b} <=> $distanceHash{$a}} keys %distanceHash) { my ($op1, $op2) = split(/\s+/, $key); &printSet($op1); print " "; &printSet($op2); print " $distanceHash{$key}\n"; } } else { my ($key) = sort {$distanceHash{$b} <=> $distanceHash{$a}} keys %distanceHash; my ($op1, $op2) = split(/\s+/, $key); &printSet($op1); print " "; &printSet($op2); print " $distanceHash{$key}\n"; } } # Subroutine to get all possible synsets corresponding to a word(#pos(#sense)) sub getWNSynsets { my $word = shift; my $pos; my $sense; my $key; my @senses; return () if(!defined $word); # First separately handle the case when the word is in word#pos or # word#pos#sense form. if($word =~ /\#/) { if($word =~ /^([^\#]+)\#([^\#])\#([^\#]+)$/) { $word = $1; $pos = $2; $sense = $3; return () if($sense !~ /[0-9]+/ || $pos !~ /^[nvar]$/); @senses = $wn->querySense($word."\#".$pos); foreach $key (@senses) { if($key =~ /\#$sense$/) { return ($key); } } return (); } elsif($word =~ /^([^\#]+)\#([^\#]+)$/) { $word = $1; $pos = $2; return () if($pos !~ /[nvar]/); } else { return (); } } else { $pos = "nvar"; } # Get the senses corresponding to the raw form of the word. @senses = (); foreach $key ("n", "v", "a", "r") { if($pos =~ /$key/) { push(@senses, $wn->querySense($word."\#".$key)); } } # If no senses corresponding to the raw form of the word, # ONLY then look for morphological variations. if(!scalar(@senses)) { foreach $key ("n", "v", "a", "r") { if($pos =~ /$key/) { my @tArr = (); push(@tArr, $wn->validForms($word."\#".$key)); push(@senses, $wn->querySense($tArr[0])) if(defined $tArr[0]); } } } return @senses; } # Subroutine to compute relatedness between all pairs of senses. sub getDistances { my $list1 = shift; my $list2 = shift; my $synset1; my $synset2; my $tracePrinted = 0; my %retHash = (); return {} if(!defined $list1 || !defined $list2); my %errcache; LEVEL2: foreach $synset1 (@{$list1}) { foreach $synset2 (@{$list2}) { # modified 12/8/03 by JM # it is possible for getRelatedness to return a non-numeric value, # and this can cause problems in ::process() when the relatedness # values are sorted #$retHash{"$synset1 $synset2"} = $measure->getRelatedness($synset1, $synset2); my $score = $measure->getRelatedness($synset1, $synset2); $retHash{"$synset1 $synset2"} = $score; my ($err, $errString) = $measure->getError(); #end modifications if($err) { # 12/9/03 JM (#1) # cache error strings indicating that two words belong # to different parts of speech $errString =~ m/(\S+\#[nvar])(?:\#\d+)? and (\S+\#[nvar])(?:\#\d+)?/; my $keystr = "$1 $2"; print STDERR "$errString\n" unless $errcache{$keystr}; $errcache{$keystr} = 1; # JM 12/8/2003 # getRelatedness() can return a warning if the two concepts # are from different taxonomies, but we need to keep # comparing relatedness values anyways # # last LEVEL2; last LEVEL2 if ($err > 1); } if(defined $opt_trace) { my $loctr = $measure->getTraceString(); if($loctr !~ /^\s*$/) { print "$synset1 $synset2:\n"; print "$loctr\n"; $tracePrinted = 1; } } } } print "\n\n" if(defined $opt_trace && $tracePrinted); return %retHash; } # Print routine to print synsets... sub printSet { my $synset = shift; my $offset; my $printString = ""; if($synset =~ /(.*)\#([nvar])\#(.*)/) { if(defined $opt_offsets) { $offset = $wn->offset($synset); $printString = sprintf("$1\#$2\#%08d", $offset); $printString =~ s/\s+$//; $printString =~ s/^\s+//; } else { $printString = "$synset"; $printString =~ s/\s+$//; $printString =~ s/^\s+//; } } print "$printString"; } # Subroutine to show minimal help. sub showUsage { print "Usage: similarity.pl [{--type TYPE [--config CONFIGFILE] [--allsenses] [--offsets]"; print " [--trace] [--wnpath PATH] [--simpath SIMPATH] {--interact | --file FILENAME | WORD1 WORD2}\n"; print " |--help \n"; print " |--version }]\n"; } # Subroutine to show detailed help. sub showHelp { &showUsage; print "\nDisplays the semantic similarity between the base forms of WORD1 and\n"; print "WORD2 using various similarity measures described in Budanitsky Hirst\n"; print "(2001). The parts of speech of WORD1 and/or WORD2 can be restricted\n"; print "by appending the part of speech (n, v, a, r) to the word.\n"; print "(For eg. car#n will consider only the noun forms of the word 'car' and\n"; print "walk#nv will consider the verb and noun forms of 'walk').\n"; print "Individual senses of can also be given as input, in the form of\n"; print "word#pos#sense strings (For eg., car#n#1 represents the first sense of\n"; print "the noun 'car').\n\n"; print "Options:\n"; print "--type Switch to select the type of similarity measure\n"; print " to be used while calculating the semantic\n"; print " relatedness. The following strings are defined.\n"; print " 'WordNet::Similarity::path' Simple edge-counts (inverted).\n"; print " 'WordNet::Similarity::hso' The Hirst St. Onge measure.\n"; print " 'WordNet::Similarity::lch' The Leacock Chodorow measure.\n"; print " 'WordNet::Similarity::lesk' Extended Gloss Overlaps measure.\n"; print " 'WordNet::Similarity::lin' The Lin measure.\n"; print " 'WordNet::Similarity::jcn' The Jiang Conrath measure.\n"; print " 'WordNet::Similarity::random' A random measure.\n"; print " 'WordNet::Similarity::res' The Resnik measure.\n"; print " 'WordNet::Similarity::vector_pairs' Gloss Vector overlap measure.\n"; print " 'WordNet::Similarity::wup' Wu Palmer measure.\n"; print "--config Module-specific configuration file CONFIGFILE. This file\n"; print " contains the configuration that is used by the\n"; print " WordNet::Similarity modules during initialization. The format\n"; print " of this file is specific to each modules and is specified in\n"; print " the module man pages and in the documentation of the\n"; print " WordNet::Similarity package.\n"; print "--allsenses Displays the relatedness between every sense pair of the\n"; print " two input words WORD1 and WORD2.\n"; print "--offsets Displays all synsets (in the output, including traces) as\n"; print " synset offsets and part of speech, instead of the \n"; print " word#partOfSpeech#senseNumber format used by QueryData.\n"; print " With this option any WordNet synset is displayed as \n"; print " word#partOfSpeech#synsetOffset in the output.\n"; print "--trace Switches on 'Trace' mode. Displays as output on STDOUT,\n"; print " the various stages of the processing. This option overrides\n"; print " the trace option in the module configuration file (if\n"; print " specified).\n"; print "--interact Starts the interactive mode. Useful for demoes, for debugging\n"; print " and to play around with the measures.\n"; print "--file Allows the user to specify an input file FILENAME\n"; print " containing pairs of word whose semantic similarity needs\n"; print " to be measured. The file is assumed to be a plain text\n"; print " file with pairs of words separated by newlines, and the\n"; print " words of each pair separated by a space.\n"; print "--wnpath Option to specify the path of the WordNet data files\n"; print " as PATH. (Defaults to /usr/local/WordNet-3.0/dict on Unix\n"; print " systems and C:\\WordNet\\3.0\\dict on Windows systems)\n"; print "--simpath If the relatedness module to be used, is locally installed,\n"; print " then SIMPATH can be used to indicate the location of the local\n"; print " install of the measure.\n"; print "--help Displays this help screen.\n"; print "--version Displays version information.\n\n"; print "\nNOTE: The environment variables WNHOME and WNSEARCHDIR, if present,\n"; print "are used to determine the location of the WordNet data files.\n"; print "Use '--wnpath' to override this.\n\n"; print "ANOTHER NOTE: During any given session, only one of three modes of input\n"; print "can be specified to the program -- command-line input (WORD1 WORD2), file\n"; print "input (--file option) or the interactive input (--interact option). If more\n"; print "than one mode of input is invoked at a given time, only one of those modes\n"; print "will work, according to the following levels of priority:\n"; print " interactive mode (--interact option) has highest priority.\n"; print " file input (--file option) has medium priority.\n"; print " command-line input (WORD1 WORD2) has lowest priority.\n"; } # Subroutine to display version information. sub showVersion { print "similarity.pl version 2.05\n"; print "WordNet::Similarity version ".($WordNet::Similarity::VERSION)."\n"; # 12/8/2003 JM (#3) # Print version of module if the --type option was given. if ($opt_type) { my $module = $opt_type; $module =~ s/::/\//g; $module .= '.pm'; require $module; print "$opt_type version ".$opt_type->VERSION()."\n"; } print "Copyright (c) 2005-2008, Ted Pedersen, Siddharth Patwardhan, Satanjeev Banerjee and Jason Michelizzi.\n"; } __END__ =head1 NAME similarity.pl - Command line interface to WordNet::Similarity =head1 SYNOPSIS similarity.pl [--type=TYPE [--config=CONFIGFILE] [--allsense] [--offsets] [--trace] [--wnpath=PATH] [--simpath=SIMPATH] {--interact | --file=FILENAME | WORD1 WORD2} | --help | --version] =head1 DESCRIPTION This program is a command line interface to the WordNet::Similarity package, which is an implementation of semantic relatedness measures between words. This project began in an effort to replicate the measures described in Budanitsky and Hirst (1995) "Semantic distance in WordNet: An Experimental, application-oriented evaluation of five measures", and has since grown to include additional measures. The measures described and implemented are as follows (those included in Budanitksy and Hirst's work are denoted with a *): (1) Leacock and Chodorow (1998) * (2) Jiang and Conrath (1997) * (3) Resnik (1995) * (4) Lin (1998) * (5) Hirst St-Onge (1998) * (6) Wu and Palmer (1994) (7) Extended Gloss Overlaps (Banerjee & Pedersen, 2003) (8) Edge Counting (9) Gloss Vector (Patwardhan, 2003) (10) Random =head1 OPTIONS B<--type>=I the type of similarity measure. Valid values are WordNet::Similarity::path - simple edge counting WordNet::Similarity::hso - Hirst & St-Onge (1998) WordNet::Similarity::lch - Leacock & Chodorow (1998) WordNet::Similarity::lesk - Extended Gloss Overlaps (Pedersen & Banerjee 2003) WordNet::Similarity::lin - Lin (1998) WordNet::Similarity::jcn - Jiang & Conrath (1997) WordNet::Similarity::random - returns random numbers WordNet::Similarity::res - Resnik (1995) WordNet::Similarity::vector - Gloss Vector (Patwardhan 2003) WordNet::Similarity::wup - Wu & Palmer (1994) B<--config>=I the path to a module-specific configuration file B<--allsenses> Show the relatedness between every sense of the two input words B<--offsets> show all synsets as offsets and a part-of-speech letter B<--trace> switches on "Trace" mode. Output goes to stdout. B<--interace> starts the interactive mode (experimental) B<--file>=I input words are read from I. This file must contain a pair of words on each line. Comments are allowed: anything following // on a line is ignored. B<--wnpath>=I looks for WordNet in I. Usual values are /usr/local/WordNet/3.0/dict and C:\WordNet\3.0\dict. B<--simpath>=I look the relatedness module in I. This is useful if the module is locally installed. B<--help> show a detailed help message B<--version> show version information =head1 BUGS Report to WordNet::Similarity mailing list : L =head1 SEE ALSO L WordNet home page : L WordNet::Similarity home page : L =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Jason Michelizzi =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen, Siddharth Patwardhan, Satanjeev Banerjee and Jason Michelizzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. =cut WordNet-Similarity-2.05/utils/similarity_server.pl0000755000175000017500000004625111020104754020544 0ustar sidsid#!/usr/bin/perl -wT # # similarity_server.pl version 2.05 # (Last updated $Id: similarity_server.pl,v 1.7 2008/05/30 23:12:44 sidz1979 Exp $) # # --------------------------------------------------------------------- # Include external packages use strict; use Getopt::Long; use File::Temp; use File::Spec; use WordNet::Similarity; use POSIX ':sys_wait_h'; # for waitpid() and friends; used by reaper() use POSIX qw(setsid); # to daemonize # Get the command-line options our($opt_wnhome, $opt_port, $opt_logfile, $opt_maxchild, $opt_stoplist, $opt_version, $opt_help); &GetOptions("wnhome=s", "port=i", "logfile=s", "maxchild=i", "stoplist=s", "version", "help"); # Check for version if(defined($opt_version)) { print "similarity_server.pl version 2.05\n"; print "WordNet::Similarity version ".($WordNet::Similarity::VERSION)."\n"; print "Copyright (c) 2005-2008, Ted Pedersen, Siddharth Patwardhan and Jason Michelizzi.\n"; exit; } # Check for help if(defined($opt_help)) { print "Usage: similarity_server.pl [--port PORTNUMBER] [--logfile LOGFILE] [--maxchild NUM] [--stoplist STOPFILE]\n"; print " | --help\n"; print " | --version\n"; print "\nStarts the similarity server, which listens for requests on a predefined\n"; print "port. It presents a netork interface to the WordNet::Similarity moduels.\n\n"; print "Options:\n"; print "--port Specify the port PORTNUMBER for the server to listen on.\n"; print "--logfile The output LOGFILE where any error or warning messages\n"; print " should be written out.\n"; print "--maxchild Specify the maximum number NUM of the processes that should\n"; print " be forked to handle the requests.\n"; print "--stoplist A file STOPFILE of stop words to be provided to the lesk and\n"; print " vector modules.\n"; print "--help Display this help message and quit.\n"; print "--version Display the version information and quit.\n"; exit; } # Local variables my $localport = 31134; my $error_log = undef; my $stoplist; my $maxchild = 4; # max number of child processes at one time sub reaper; # Set the log file, if specified $error_log = $1 if(defined($opt_logfile) and $opt_logfile ne "" and $opt_logfile =~ /^(.*)$/); print STDERR "Error log = ".($error_log?$error_log:"")."\n"; # Set the stop list, if specified $stoplist = $1 if(defined($opt_stoplist) and $opt_stoplist ne "" and $opt_stoplist =~ /^(.*)$/); print STDERR "Stoplist = ".($stoplist?$stoplist:"")."\n"; # Set the port $localport = $opt_port if(defined($opt_port)); print STDERR "Local port = $localport\n"; # Set the maxchild $maxchild = $opt_maxchild if(defined($opt_maxchild)); print STDERR "Maxchild = $maxchild\n"; # Create the temporary lock file my $lockfh = File::Temp->new(); my $lock_file = $lockfh->filename(); die "Error: Unable to create temporary lock file.\n" if(!$lockfh); print $lockfh $$; close $lockfh or die "Error: Cannot close lock file: $! \n"; print STDERR "Loading modules... "; # prototypes: sub getAllForms ($); sub getlock (); sub releaselock (); sub timestamp ($); use sigtrap handler => \&bailout, 'normal-signals'; use IO::Socket::INET; use WordNet::QueryData; use WordNet::Tools; use WordNet::Similarity::hso; use WordNet::Similarity::jcn; use WordNet::Similarity::lch; use WordNet::Similarity::lesk; use WordNet::Similarity::lin; use WordNet::Similarity::path; use WordNet::Similarity::random; use WordNet::Similarity::res; use WordNet::Similarity::vector; use WordNet::Similarity::vector_pairs; use WordNet::Similarity::wup; my $wnlocation = undef; $wnlocation = File::Spec->catfile($opt_wnhome, "dict") if(defined($opt_wnhome)); my $wn = WordNet::QueryData->new($wnlocation); $wn or die "Error: Couldn't construct WordNet::QueryData object.\n"; my $wntools = WordNet::Tools->new($wn); $wntools or die "Error: Unable to create WordNet::Tools object.\n"; our $hso = WordNet::Similarity::hso->new($wn); our $jcn = WordNet::Similarity::jcn->new($wn); our $lch = WordNet::Similarity::lch->new($wn); my $leskfh = File::Temp->new(); my $leskcfg = $leskfh->filename(); die "Error: Unable to create temporary config file.\n" if(!$leskfh); print $leskfh "WordNet::Similarity::lesk\n"; print $leskfh "stem::1\n"; print $leskfh "stop::$stoplist\n" if(defined($stoplist) && -e $stoplist); close $leskfh or die "Error: Unable to close config file.\n"; our $lesk = WordNet::Similarity::lesk->new($wn, $leskcfg); my $vectorfh = File::Temp->new(); my $vectorcfg = $vectorfh->filename(); die "Error: Unable to create temporary config file.\n" if(!$vectorfh); print $vectorfh "WordNet::Similarity::vector_pairs\n"; print $vectorfh "stop::$stoplist\n" if(defined($stoplist) && -e $stoplist); print $vectorfh "stem::1\n"; close $vectorfh or die "Error: Unable to close config file.\n"; our $vector_pairs = WordNet::Similarity::vector_pairs->new($wn, $vectorcfg); my $vectorpfh = File::Temp->new(); my $vectorpcfg = $vectorfh->filename(); die "Error: Unable to create temporary config file.\n" if(!$vectorpfh); print $vectorpfh "WordNet::Similarity::vector\n"; print $vectorpfh "stop::$stoplist\n" if(defined($stoplist) && -e $stoplist); print $vectorpfh "stem::1\n"; close $vectorpfh or die "Error: Unable to close config file.\n"; our $vector = WordNet::Similarity::vector->new($wn, $vectorpcfg); our $lin = WordNet::Similarity::lin->new($wn); our $path = WordNet::Similarity::path->new($wn); our $random = WordNet::Similarity::random->new($wn); our $res = WordNet::Similarity::res->new($wn); our $wup = WordNet::Similarity::wup->new($wn); my @measures = ($hso, $jcn, $lch, $lesk, $lin, $path, $random, $res, $wup, $vector, $vector_pairs); foreach (@measures) { my ($err, $errstr) = $_->getError(); die "$errstr died.\n" if $err; } undef @measures; # reset (untaint) the PATH $ENV{PATH} = '/bin:/usr/bin:/usr/local/bin'; print STDERR "done.\n"; print STDERR "Starting server... going into background.\n"; # Daemonize open STDIN, '/dev/null' or die "Can't read /dev/null: $! \n"; open STDOUT, '>>/dev/null' or die "Can't write to /dev/null: $! \n"; # The is the socket we listen to my $socket = IO::Socket::INET->new( LocalPort => $localport, Listen => SOMAXCONN, Reuse => 1, Type => SOCK_STREAM ) or die "Could not bind to network port: $! \n"; print STDERR "Closing output to terminal.\n"; if(defined($error_log)) { print STDERR "All future messages will be routed to the log file.\n"; if(!open(STDERR, '>>', $error_log)) { print "Error: Could open error log.\n"; die "Error: Could not re-open STDERR.\n"; } chmod 0664, $error_log; } else { print STDERR "No more messages will be printed (even if the server dies).\n"; open STDERR, '>>/dev/null' or die "Can't write to /dev/null: $! \n"; } chdir '/' or die(×tamp("Can't chdir to /: $! \n")); defined(my $pid = fork) or die(×tamp("Can't fork: $! \n")); exit if $pid; setsid or die(×tamp("Can't start a new session: $! \n")); umask 0; # this variable is incremented after every fork, and is # updated by reaper() when a child process dies my $num_children = 0; ## SEE BELOW # automatically reap child processes #$SIG{CHLD} = 'IGNORE'; ## ## BETTER WAY: # handle death of child process $SIG{CHLD} = \&reaper; my $interrupted = 0; ACCEPT: while((my $client = $socket->accept) or $interrupted) { $interrupted = 0; next unless $client; # a SIGCHLD was raised # check to see if it's okay to handle this request if($num_children >= $maxchild) { print $client "busy\015\012"; $client->close; undef $client; next ACCEPT; } my $childpid; # fork; let the child handle the actual request if($childpid = fork) { # This is the parent $num_children++; # go wait for next request undef $client; next ACCEPT; } # This is the child process defined $childpid or die(×tamp("Could not fork: $! \n")); # here we're the child, so we actually handle the request my @requests; while(my $request = <$client>) { last if $request eq "\015\012"; push @requests, $request; } foreach my $i (0..$#requests) { my $request = $requests[$i]; my $rnum = $i + 1; my $type = 'UNDEFINED'; if($request =~ m/^(\w)\b/) { $type = $1; } else { $type = 'UNDEFINED'; } if($type eq 'v') { eval{ # get version information my $qdver = $wn->VERSION(); my $wnver = $wntools->hashCode(); my $simver = $WordNet::Similarity::VERSION; print $client "v WordNet $wnver\015\012"; print $client "v WordNet::QueryData $qdver\015\012"; print $client "v WordNet::Similarity $simver\015\012"; }; print(STDERR ×tamp("$@\n")) if($@); } elsif($type eq 's') { my (undef, $word) = split /\s+/, $request; if(!defined($word) || $word eq "") { print $client "! Usage: s WORD\015\012"; goto EXIT_CHILD; } my @senses = getAllForms($word); unless(scalar @senses) { print $client "! $word was not found in WordNet\015\012"; goto EXIT_CHILD; } getlock; foreach my $wps (@senses) { eval{ my @synset = $wn->querySense($wps, "syns"); print $client "$rnum $wps ", join(" ", @synset), "\015\012"; }; print(STDERR ×tamp("$@\n")) if($@); } releaselock; } elsif($type eq 'g') { my $word = undef; (undef, $word) = split /\s+/, $request; if(!defined($word) || $word eq "") { print $client "! Usage: g WORD\015\012"; goto EXIT_CHILD; } my @senses = getAllForms($word); unless(scalar @senses) { print $client "! $word was not found in WordNet\015\012"; goto EXIT_CHILD; } getlock; foreach my $wps (@senses) { eval{ my ($gloss) = $wn->querySense($wps, "glos"); print $client "$rnum $wps ${gloss}\015\012"; }; print(STDERR ×tamp("$@\n")) if($@); } releaselock; } elsif($type eq 'r') { my (undef, $word1, $word2, $measure, $trace, $gloss, $syns, $root)= split /\s+/, $request; unless(defined $word1 and defined $word2) { print $client "! Error: undefined input words\015\012"; sleep 2; goto EXIT_CHILD; } my $module; if($measure =~ /^(?:hso|jcn|lch|lesk|lin|path|random|res|wup|vector|vector_pairs)$/) { no strict 'refs'; $module = $$measure; unless(defined $module) { print $client "! Error: Couldn't get reference to measure\015\012"; sleep 2; goto EXIT_CHILD; } } else { print $client "! Error: no such measure $measure\015\012"; sleep 2; goto EXIT_CHILD; } my @wps1 = getAllForms($word1); unless(scalar @wps1) { print $client "! $word1 was not found in WordNet\015\012"; goto EXIT_CHILD; } my @wps2 = getAllForms($word2); unless(scalar @wps2) { print $client "! $word2 was not found in WordNet\015\012"; goto EXIT_CHILD; } if(defined($trace) and $trace eq 'yes') { $module->{trace} = 1; } $module->{rootNode} = (defined($root) and $root eq 'yes') ? 1 : 0; if((defined($gloss) and $gloss eq 'yes') or (defined($syns) and $syns eq 'yes')) { getlock; foreach my $wps ((@wps1, @wps2)) { if(defined($gloss) and $gloss eq 'yes') { eval{ my ($gls) = $wn->querySense($wps, 'glos'); print $client "g $wps $gls\015\012"; }; print(STDERR ×tamp("$@\n")) if($@); } if(defined($syns) and $syns eq 'yes') { eval{ my @syns = $wn->querySense($wps, 'syns'); print $client "s ", join(" ", @syns), "\015\012"; }; print(STDERR ×tamp("$@\n")) if($@); } } releaselock; } getlock; foreach my $wps1 (@wps1) { foreach my $wps2 (@wps2) { eval{ my $score = $module->getRelatedness($wps1, $wps2); my ($err, $errstr) = $module->getError(); if($err) { print $client "! $errstr\015\012"; } else { print $client "r $measure $wps1 $wps2 $score\015\012"; } if(defined($trace) and $trace eq 'yes') { my $tracestr = $module->getTraceString(); $tracestr =~ s/[\015\012]+//g; print $client "t $tracestr\015\012"; } }; print(STDERR ×tamp("$@\n")) if($@); } } releaselock; # reset traces to off $module->{trace} = 0; } else { print $client "! Bad request type `$type'\015\012"; } } # Terminate ALL messages with CRLF (\015\012). Do NOT use # \r\n (the meaning of \r and \n varies on different platforms). EXIT_CHILD: print $client "\015\012"; $client->close; $socket->close; # don't let the child accept: exit; } $socket->close; exit; sub getAllForms ($) { my $word = shift; # check if it's a type III string already: return $word if $word =~ m/[^#]+\#[nvar]\#\d+/; # it must be a type I or II, so let's get all valid forms getlock; my @forms = (); eval{@forms = $wn->validForms($word);}; print(STDERR ×tamp("$@\n")) if($@); releaselock; return () unless scalar @forms; my @wps_strings; # for each valid form, get all valid wps strings foreach my $form (@forms) { # form is a type II string getlock; my @strings = (); eval{@strings = $wn->querySense($form);}; print(STDERR ×tamp("$@\n")) if($@); releaselock; next unless scalar @strings; push @wps_strings, @strings; } return @wps_strings; } # A signal handler, good for most normal signals (esp. INT). Mostly we just # want to close the socket we're listening to and delete the lock file. sub bailout { my $sig = shift; $sig = defined $sig ? $sig : "?UNKNOWN?"; $socket->close if defined $socket; print(STDERR ×tamp("Bailing out (SIG$sig).\n")); releaselock if($lockfh); exit 1; } use Fcntl qw/:flock/; # gets a lock on $lockfh. The return value is that of flock. sub getlock () { open($lockfh, '>>', $lock_file) or die(×tamp("Cannot open lock file $lock_file: $! \n")); eval{flock $lockfh, LOCK_EX;}; print(STDERR ×tamp("$@\n")) if($@); } # releases a lock on $lockfh. The return value is that of flock. sub releaselock () { eval{ flock $lockfh, LOCK_UN; close $lockfh; }; print(STDERR ×tamp("$@\n")) if($@); } # attach a time stamp sub timestamp ($) { my $instring = shift; return $instring if(!defined($instring)); my @monthNames = qw(Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec); my ($second, $minute, $hour, $dayOfMonth, $month, $yearOffset, $dayOfWeek, $dayOfYear) = localtime(); my $year = 1900 + $yearOffset; return "["."$dayOfMonth/$monthNames[$month]/$year:$hour:$minute:$second"."] $instring"; } # sub to reap child processes (so they don't become zombies) # also updates the num_children variable # # Sub was loosely inspired by an example at # http://www.india-seo.com/perl/cookbook/ch16_20.htm sub reaper { my $moribund; if(my $pid = waitpid(-1, WNOHANG) > 0) { $num_children-- if WIFEXITED($?); } $interrupted = 1; $SIG{CHLD} = \&reaper; # cursed be SysV } __END__ =head1 NAME similarity_server.pl - [Web] The backend WordNet::Similarity server for the Web Interface =head1 SYNOPSIS Usage: similarity_server.pl [--port PORTNUMBER] [--logfile LOGFILE] [--maxchild NUM] [--stoplist STOPFILE] | --help | --version =head1 DESCRIPTION This script implements the backend of the web interface for WordNet::Similarity. This script listens to a port waiting for a request form similarity.cgi or wps.cgi. The client script sends a message to this script as series of queries (see QUERY FORMAT). After all the queries, the client sends a message containing only CRLF (carriage-return line-feed, or \015\012). The server (this script) responds with the results (see MESSAGE FORMAT) terminated by a message containing only CRLF. =head2 Example: Client: g car#n#1CRLF CRLF Sever responds: g car#n#1 4-wheeled motor vehicle; usually propelled by an internal combustion engine; "he needs a car to get to work"CRLF CRLF =head1 OPTIONS B<--port>=I Specify the port PORTNUMBER for the server to listen on. B<--logfile>=I The output LOGFILE where any error or warning messages should be written out. B<--maxchild>=I Specify the maximum number NUM of the processes that should be forked to handle the requests. B<--stoplist>=I A file STOPFILE of stop words to be provided to the lesk and vector modules. B<--help> Display the help message and quit. B<--version> Display the version information and quit. =head1 QUERY FORMAT means carriage-return line-feed "\r\n" on Unix, "\n\r" on Macs, \015\012 everywhere and anywhere (i.e., don't use \n or \r, use \015\012). The queries consist of messages in the following formats: s - server will return all senses of word1 and word2 g - server will return the gloss for each synset to which word belongs r - server will return the relatedness of wps1 and wps2 using measure. v - get version information =head1 MESSAGE FORMAT The messages sent from this server will be in the following formats: ! - indicates an error or warning g - the gloss of wps r - the relatedness score of wps1 and wps2 t - the trace output for the previous relatedness score s ... - a synset v - the version of 'package' being used =head1 BUGS Report to WordNet::Similarity mailing list : L =head1 SEE ALSO L WordNet home page : L WordNet::Similarity home page (provides link to web interface): L =head1 AUTHORS Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah sidd at cs.utah.edu Jason Michelizzi =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen, Siddharth Patwardhan and Jason Michelizzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to: The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. =cut WordNet-Similarity-2.05/utils/BNCFreq.pl0000755000175000017500000002775711021105346016221 0ustar sidsid#! /usr/bin/perl -w # # BNCFreq.pl version 2.05 # (Last updated $Id: BNCFreq.pl,v 1.18 2008/06/02 23:26:42 sidz1979 Exp $) # # ----------------------------------------------------------------------------- # Some modules used use strict; use Getopt::Long; use WordNet::QueryData; use WordNet::Tools; use WordNet::Similarity::FrequencyCounter; # Variable declarations my %stopWords; my %offsetFreq; # First check if no commandline options have been provided... in which case # print out the usage notes! if ($#ARGV == -1) { &minimalUsageNotes(); exit; } # Now get the options! our ($opt_version, $opt_help, $opt_stopfile, $opt_outfile, $opt_wnpath, $opt_resnik, $opt_smooth); &GetOptions("version", "help", "stopfile=s", "outfile=s", "wnpath=s", "resnik", "smooth=s"); # If the version information has been requested if(defined $opt_version) { &printVersion(); exit; } # If detailed help has been requested if(defined $opt_help) { &printHelp(); exit; } # Get the output filename... exit gracefully, if not specified. if(!defined $opt_outfile) { &minimalUsageNotes(); exit; } # Get the PATH of the BNC texts... my $rootPath; if($#ARGV < 0) { &minimalUsageNotes(); exit; } else { $rootPath = shift; if(!(-e $rootPath && -d $rootPath)) { print STDERR "Unable to open $rootPath.\n"; &minimalUsageNotes(); exit; } } # Get the path to WordNet... my ($wnPCPath, $wnUnixPath); if(defined $opt_wnpath) { $wnPCPath = $opt_wnpath; $wnUnixPath = $opt_wnpath; } elsif (defined $ENV{WNSEARCHDIR}) { $wnPCPath = $ENV{WNSEARCHDIR}; $wnUnixPath = $ENV{WNSEARCHDIR}; } elsif (defined $ENV{WNHOME}) { $wnPCPath = $ENV{WNHOME} . "\\dict"; $wnUnixPath = $ENV{WNHOME} . "/dict"; } else { $wnPCPath = "C:\\Program Files\\WordNet\\3.0\\dict"; $wnUnixPath = "/usr/local/WordNet-3.0/dict"; } # Load the stop words if specified if(defined $opt_stopfile) { print STDERR "Loading stoplist... "; open(WORDS, "$opt_stopfile") || die("Couldnt open $opt_stopfile.\n"); while () { s/[\r\f\n]//g; $stopWords{$_} = 1; } close WORDS; print STDERR "done.\n"; } # Load up WordNet print STDERR "Loading WordNet... "; my $wn=(defined $opt_wnpath)? (WordNet::QueryData->new($opt_wnpath)):(WordNet::QueryData->new()); die "Unable to create WordNet::QueryData object.\n" if(!$wn); $wnPCPath = $wnUnixPath = $wn->dataPath() if($wn->can('dataPath')); my $wntools = WordNet::Tools->new($wn); die "Unable to create WordNet::Tools object.\n" if(!$wntools); print STDERR "done.\n"; # Load the topmost nodes of the hierarchies print STDERR "Loading topmost nodes of the hierarchies... "; my $topHash = WordNet::Similarity::FrequencyCounter::createTopHash($wn); print STDERR "done.\n"; # Read the input, form sentences and process each print STDERR "Computing frequencies... \n"; opendir(ROOTDIR, $rootPath) || die "Unable to open the root path.\n"; my @levelOneFiles = map {"$rootPath/$_"} grep(!/^\.\.?\z/, readdir(ROOTDIR)); closedir(ROOTDIR); foreach my $levelOnePath (@levelOneFiles) { if(-d $levelOnePath && opendir(L1PATH, $levelOnePath)) { my @levelTwoFiles = map {"$levelOnePath/$_"} grep(!/^\.\.?\z/, readdir(L1PATH)); closedir(L1PATH); foreach my $levelTwoPath (@levelTwoFiles) { if(-d $levelTwoPath && opendir(L2PATH, $levelTwoPath)) { my @levelThreeFiles = map {"$levelTwoPath/$_"} grep(!/^\.\.?\z/, readdir(L2PATH)); closedir(L2PATH); foreach my $levelThreeFile (@levelThreeFiles) { if(-f $levelThreeFile) { &lineProcess($levelThreeFile); } } } } } } # Smoothing! if(defined $opt_smooth) { print STDERR "Smoothing... "; if($opt_smooth eq 'ADD1') { foreach my $pos ("noun", "verb") { my $localpos = $pos; if(!open(IDX, $wnUnixPath."/data.$pos")) { if(!open(IDX, $wnPCPath."/$pos.dat")) { print STDERR "Unable to open WordNet data files.\n"; exit; } } $localpos =~ s/(^[nv]).*/$1/; while() { last if(/^\S/); } my ($offset) = split(/\s+/, $_, 2); $offset =~ s/^0*//; $offsetFreq{$localpos}{$offset}++; while() { ($offset) = split(/\s+/, $_, 2); $offset =~ s/^0*//; $offsetFreq{$localpos}{$offset}++; } close(IDX); } print STDERR "done.\n"; } else { print STDERR "\nWarning: Unknown smoothing '$opt_smooth'.\n"; print STDERR "Use --help for details.\n"; print STDERR "Continuing without smoothing.\n"; } } # Propagating frequencies up the WordNet hierarchies... print STDERR "Propagating frequencies up through WordNet... "; my $newFreq = WordNet::Similarity::FrequencyCounter::propagateFrequency(\%offsetFreq, $wn, $topHash); print STDERR "done.\n"; # Print the output to file print STDERR "Writing output file... "; open(OUT, ">$opt_outfile") || die "Unable to open $opt_outfile for writing.\n"; print OUT "wnver::".$wntools->hashCode()."\n"; foreach my $pos ("n", "v") { foreach my $offset (sort {$a <=> $b} keys %{$newFreq->{$pos}}) { print OUT "$offset$pos $newFreq->{$pos}->{$offset}"; print OUT " ROOT" if($topHash->{$pos}->{$offset}); print OUT "\n"; } } close(OUT); print "done.\n"; # ----------------- Subroutines start Here ---------------------- # Open one of the data files and get each line of the file # for processing... preprocess it and send it to the process # function. sub lineProcess { my $fname = shift; if(open(DATFILE, $fname)) { print STDERR "$fname\n"; my $sentence = ""; my $line = ""; my $firstFlag = 0; while($line = ) { $line =~ s/[\r\f\n]//g; $line =~ s/&([a-z0-9]*?;)+//g; $line =~ s/\[.*?\]//g; next if($firstFlag == 0 and $line !~ /(<\/?s\s+)|(<\/?s>)/); $firstFlag = 1; my @parts = split(/<\/?s(?:\s+[^>]+)?>/, $line); foreach (1..$#parts) { $sentence .= shift(@parts)." "; &process($sentence); $sentence = ""; } $sentence .= shift(@parts)." " if(@parts); } &process($sentence); close(DATFILE); } } # Processing of each sentence # (1) Convert to lowercase # (2) Remove all unwanted characters # (3) Combine all consequetive occurrence of numbers into one # (4) Remove leading and trailing spaces # (5) Form all possible compounds in the words # (6) Get the frequency counts sub process { my $block; $block = lc(shift); $block =~ s/(<.*?>)+/ /g; $block =~ s/\'//g; $block =~ s/[^a-z0-9]+/ /g; while($block =~ s/([0-9]+)\s+([0-9]+)/$1$2/g){} $block =~ s/^\s+//; $block =~ s/\s+$//; $block = $wntools->compoundify($block); while($block =~ /([\w_]+)/g) { WordNet::Similarity::FrequencyCounter::updateWordFrequency($1, \%offsetFreq, $wn, $opt_resnik) if(!defined $stopWords{$1}); } } # Subroutine to print detailed help sub printHelp { &printUsage(); print "\nThis program computes the information content of concepts, by\n"; print "counting the frequency of their occurrence in the British\n"; print "National Corpus. PATH specifies the root of the directory tree\n"; print "containing the text of the BNC.\n"; print "Options: \n"; print "--outfile Specifies the output file OUTFILE.\n"; print "--stopfile STOPFILE is a list of stop listed words that will\n"; print " not be considered in the frequency count.\n"; print "--wnpath Option to specify WNPATH as the location of WordNet data\n"; print " files. If this option is not specified, the program tries\n"; print " to determine the path to the WordNet data files using the\n"; print " WNHOME environment variable.\n"; print "--resnik Option to specify that the frequency counting should\n"; print " be performed according to the method described by\n"; print " Resnik (1995).\n"; print "--smooth Specifies the smoothing to be used on the probabilities\n"; print " computed. SCHEME specifies the type of smoothing to\n"; print " perform. It is a string, which can be only be 'ADD1'\n"; print " as of now. Other smoothing schemes will be added in\n"; print " future releases.\n"; print "--help Displays this help screen.\n"; print "--version Displays version information.\n\n"; } # Subroutine to print minimal usage notes sub minimalUsageNotes { &printUsage(); print "Type BNCFreq.pl --help for detailed help.\n"; } # Subroutine that prints the usage sub printUsage { print "BNCFreq.pl [{--outfile OUTFILE [--stopfile STOPFILE]"; print " [--wnpath WNPATH] [--resnik] [--smooth SCHEME] PATH | --help | --version }]\n"; } # Subroutine to print the version information sub printVersion { print "BNCFreq.pl version 2.05\n"; print "Copyright (c) 2005-2008, Ted Pedersen, Satanjeev Banerjee and Siddharth Patwardhan.\n"; } __END__ =head1 NAME BNCFreq.pl - Compute Information Content based on British National Corpus (World Edition) =head1 SYNOPSIS BNCFreq.pl [--outfile=OUTFILE [--stopfile=STOPFILE] [--wnpath=WNPATH] [--resnik] [--smooth=SCHEME] PATH | --help --version] =head1 DESCRIPTION This program reads the British National Corpus (World Edition, December 2000) and computes the frequency counts for each synset in WordNet. These frequency counts are used by the Lin, Resnik, and Jiang & Conrath measures of semantic relatedness to calculate the information content values of concepts. The output is generated in a format as required by the L modules for computing semantic relatedness. A more detailed description of how information content is calculated can be found in L. This program uses exactly the same techniques as described there. =head1 OPTIONS B<--outfile>=I The name of a file to which output should be written B<--stopfile>=I A file containing a list of stop listed words that will not be considered in the frequency counts. A sample file can be down- loaded from http://www.d.umn.edu/~tpederse/Group01/WordNet/words.txt B<--wnpath>=I Location of the WordNet data files (e.g., /usr/local/WordNet-3.0/dict) B<--resnik> Use Resnik (1995) frequency counting B<--smooth>=I Smoothing should used on the probabilities computed. SCHEME can only be ADD1 at this time B<--help> Show a help message B<--version> Display version information B Path to the root of the texts of the corpus. (e.g., /home/sid/BNC-word/Texts). =head1 BUGS Report to WordNet::Similarity mailing list : L =head1 SEE ALSO L Please note that the BNC World Edition has been superceded by the BNC XML Edition. Please contact the BNC for more information about the continued availabiliy of BNC World: L WordNet home page : L WordNet::Similarity home page : L =head1 AUTHORS Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen, Satanjeev Banerjee, and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. =cut WordNet-Similarity-2.05/utils/treebankFreq.pl0000755000175000017500000002705011020104754017375 0ustar sidsid#! /usr/bin/perl -w # # treebankFreq.pl version 2.05 # (Last updated $Id: treebankFreq.pl,v 1.17 2008/05/30 23:12:44 sidz1979 Exp $) # # ----------------------------------------------------------------------------- # Some modules used use strict; use Getopt::Long; use WordNet::QueryData; use WordNet::Tools; use WordNet::Similarity::FrequencyCounter; # Variable declarations my %stopWords; my %offsetFreq; # First check if no commandline options have been provided... in which case # print out the usage notes! if ($#ARGV == -1) { &minimalUsageNotes(); exit; } # Now get the options! our ($opt_version, $opt_help, $opt_stopfile, $opt_outfile, $opt_wnpath, $opt_resnik, $opt_smooth); &GetOptions("version", "help", "stopfile=s", "outfile=s", "wnpath=s", "resnik", "smooth=s"); # If the version information has been requested if(defined $opt_version) { &printVersion(); exit; } # If detailed help has been requested if(defined $opt_help) { &printHelp(); exit; } # Get the output filename... exit gracefully, if not specified. unless(defined $opt_outfile) { &minimalUsageNotes(); exit; } # Get the path to WordNet... my ($wnPCPath, $wnUnixPath); if(defined $opt_wnpath) { $wnPCPath = $opt_wnpath; $wnUnixPath = $opt_wnpath; } elsif (defined $ENV{WNSEARCHDIR}) { $wnPCPath = $ENV{WNSEARCHDIR}; $wnUnixPath = $ENV{WNSEARCHDIR}; } elsif (defined $ENV{WNHOME}) { $wnPCPath = $ENV{WNHOME} . "\\dict"; $wnUnixPath = $ENV{WNHOME} . "/dict"; } else { $wnPCPath = "C:\\Program Files\\WordNet\\3.0\\dict"; $wnUnixPath = "/usr/local/WordNet-3.0/dict"; } # Get the PATH of the Treebank texts... my $rootPath; if($#ARGV < 0) { &minimalUsageNotes(); exit; } else { $rootPath = shift; if(!(-e $rootPath && -d $rootPath)) { print STDERR "Unable to open $rootPath.\n"; &minimalUsageNotes(); exit; } } # Load the stop words if specified if(defined $opt_stopfile) { print STDERR "Loading stoplist... "; open(WORDS, "$opt_stopfile") || die("Couldnt open $opt_stopfile.\n"); while () { s/[\r\f\n]//g; $stopWords{$_} = 1; } close WORDS; print STDERR "done.\n"; } # Load up WordNet print STDERR "Loading WordNet... "; my $wn=(defined $opt_wnpath)? (WordNet::QueryData->new($opt_wnpath)):(WordNet::QueryData->new()); die "Unable to create WordNet::QueryData object.\n" if(!$wn); $wnPCPath = $wnUnixPath = $wn->dataPath() if($wn->can('dataPath')); my $wntools = WordNet::Tools->new($wn); die "Unable to create WordNet::Tools object.\n" if(!$wntools); print STDERR "done.\n"; # Load the topmost nodes of the hierarchies print STDERR "Loading topmost nodes of the hierarchies... "; my $topHash = WordNet::Similarity::FrequencyCounter::createTopHash($wn); print STDERR "done.\n"; # Read the input, form sentences and process each print STDERR "Computing frequencies... \n"; opendir(ROOTDIR, $rootPath) || die "Unable to open the root path.\n"; my @levelOneFiles = map {"$rootPath/$_"} grep(!/^\.\.?\z/, readdir(ROOTDIR)); closedir(ROOTDIR); foreach my $levelOnePath (@levelOneFiles) { if(-d $levelOnePath && opendir(L1PATH, $levelOnePath)) { my @levelTwoFiles = map {"$levelOnePath/$_"} grep(!/^\.\.?\z/, readdir(L1PATH)); closedir(L1PATH); foreach my $levelTwoFile (@levelTwoFiles) { if(-f $levelTwoFile) { &lineProcess($levelTwoFile); } } } } # Smoothing! if(defined $opt_smooth) { print STDERR "Smoothing... "; if($opt_smooth eq 'ADD1') { foreach my $pos ("noun", "verb") { my $localpos = $pos; if(!open(IDX, $wnUnixPath."/data.$pos")) { if(!open(IDX, $wnPCPath."/$pos.dat")) { print STDERR "Unable to open WordNet data files.\n"; exit; } } $localpos =~ s/(^[nv]).*/$1/; while() { last if(/^\S/); } my ($offset) = split(/\s+/, $_, 2); $offset =~ s/^0*//; $offsetFreq{$localpos}{$offset}++; while() { ($offset) = split(/\s+/, $_, 2); $offset =~ s/^0*//; $offsetFreq{$localpos}{$offset}++; } close(IDX); } print STDERR "done.\n"; } else { print STDERR "\nWarning: Unknown smoothing '$opt_smooth'.\n"; print STDERR "Use --help for details.\n"; print STDERR "Continuing without smoothing.\n"; } } # Propagating frequencies up the WordNet hierarchies... print STDERR "Propagating frequencies up through WordNet... "; my $newFreq = WordNet::Similarity::FrequencyCounter::propagateFrequency(\%offsetFreq, $wn, $topHash); print STDERR "done.\n"; # Print the output to file print STDERR "Writing output file... "; open(OUT, ">$opt_outfile") || die "Unable to open $opt_outfile for writing.\n"; print OUT "wnver::".$wntools->hashCode()."\n"; foreach my $pos ("n", "v") { foreach my $offset (sort {$a <=> $b} keys %{$newFreq->{$pos}}) { print OUT "$offset$pos $newFreq->{$pos}->{$offset}"; print OUT " ROOT" if($topHash->{$pos}->{$offset}); print OUT "\n"; } } close(OUT); print "done.\n"; # ----------------- Subroutines start Here ---------------------- # Open one of the data files and get each line of the file # for processing... preprocess it and send it to the process # function. sub lineProcess { my $fname = shift; my $processed = 1; if(open(DATFILE, $fname)) { print STDERR "$fname\n"; my $sentence = ""; while(my $line = ) { $processed = 0; $line =~ s/[\r\f\n]/ /g; $line =~ s/\.START//g; $sentence = $sentence." ".$line; if($line =~ /^\s*$/) { &process($sentence); $sentence = ""; $processed = 1; } } &process($sentence) if(!$processed); close(DATFILE); } } # Processing of each sentence # (1) Convert to lowercase # (2) Remove all unwanted characters # (3) Combine all consequetive occurrence of numbers into one # (4) Remove leading and trailing spaces # (5) Form all possible compounds in the words # (6) Get the frequency counts sub process { my $block; $block = lc(shift); $block =~ s/\'//g; $block =~ s/[^a-z0-9]+/ /g; while($block =~ s/([0-9]+)\s+([0-9]+)/$1$2/g){} $block =~ s/^\s+//; $block =~ s/\s+$//; $block = $wntools->compoundify($block); while($block =~ /([\w_]+)/g) { WordNet::Similarity::FrequencyCounter::updateWordFrequency($1, \%offsetFreq, $wn, $opt_resnik) if(!defined $stopWords{$1}); } } # Subroutine to print detailed help sub printHelp { &printUsage(); print "\nThis program computes the information content of concepts, by\n"; print "counting the frequency of their occurrence in the Treebank Corpus.\n"; print "PATH specifies the root of the directory tree containing the text of\n"; print "the corpus.\n"; print "Options: \n"; print "--outfile Specifies the output file OUTFILE.\n"; print "--stopfile STOPFILE is a list of stop listed words that will\n"; print " not be considered in the frequency count.\n"; print "--wnpath Option to specify WNPATH as the location of WordNet data\n"; print " files. If this option is not specified, the program tries\n"; print " to determine the path to the WordNet data files using the\n"; print " WNHOME environment variable.\n"; print "--resnik Option to specify that the frequency counting should\n"; print " be performed according to the method described by\n"; print " Resnik (1995).\n"; print "--smooth Specifies the smoothing to be used on the probabilities\n"; print " computed. SCHEME specifies the type of smoothing to\n"; print " perform. It is a string, which can be only be 'ADD1'\n"; print " as of now. Other smoothing schemes will be added in\n"; print " future releases.\n"; print "--help Displays this help screen.\n"; print "--version Displays version information.\n\n"; } # Subroutine to print minimal usage notes sub minimalUsageNotes { &printUsage(); print "Type treebankFreq.pl --help for detailed help.\n"; } # Subroutine that prints the usage sub printUsage { print "treebankFreq.pl [{--outfile OUTFILE [--stopfile STOPFILE]"; print " [--wnpath WNPATH] [--resnik] [--smooth SCHEME] PATH | --help | --version }]\n"; } # Subroutine to print the version information sub printVersion { print "treebankFreq.pl version 2.05\n"; print "Copyright (c) 2005-2008, Ted Pedersen, Satanjeev Banerjee and Siddharth Patwardhan.\n"; } __END__ =head1 NAME treebankFreq.pl - Compute Information Content from Penn Treebank 2 =head1 SYNOPSIS treebankFreq.pl [--outfile=OUTFILE [--stopfile=STOPFILE] [--wnpath=WNPATH] [--resnik] [--smooth=SCHEME] PATH | --help --version] =head1 DESCRIPTION This program reads the Penn Treebank, Release 2, from the Linguistic Data Consortium, L, and computes the frequency counts for each synset in WordNet. These frequency counts are used by the Lin, Resnik, and Jiang & Conrath measures of semantic relatedness to calculate the information content values of concepts. The output is generated in a format as required by the L modules for computing semantic relatedness. A more detailed description of how information content is calculated can be found in L. This program uses exactly the same techniques as described there. =head1 OPTIONS B<--outfile>=I The name of a file to which output should be written B<--stopfile>=I A file containing a list of stop listed words that will not be considered in the frequency counts. A sample file can be down- loaded from http://www.d.umn.edu/~tpederse/Group01/WordNet/words.txt B<--wnpath>=I Location of the WordNet data files (e.g., /usr/local/WordNet-3.0/dict) B<--resnik> Use Resnik (1995) frequency counting B<--smooth>=I Smoothing should used on the probabilities computed. SCHEME can only be ADD1 at this time B<--help> Show a help message B<--version> Display version information B Path to the raw Wall Stree Journal portion of the Treebank corpus. This is usually in the /raw/wsj subdirectory of the Treebank installation. Thus, you might run this program as treebankFreq.pl [OPTIONS] /home/sid/treebank/raw/wsj =head1 BUGS Report to WordNet::Similarity mailing list : L =head1 SEE ALSO L Penn Treebank : L, WordNet home page : L WordNet::Similarity home page : L =head1 AUTHORS Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen, Satanjeev Banerjee, and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. =cut WordNet-Similarity-2.05/utils/semCorFreq.pl0000755000175000017500000002440511020104754017033 0ustar sidsid#! /usr/bin/perl -w # # semCorFreq.pl version 2.05 # (Last updated $Id: semCorFreq.pl,v 1.13 2008/05/30 23:12:44 sidz1979 Exp $) # # ----------------------------------------------------------------- # Include other packages use strict; use WordNet::QueryData; use WordNet::Tools; use Getopt::Long; use WordNet::Similarity::FrequencyCounter; # Global Variable declaration. my %offsetMnem; my %mnemFreq; my %offsetFreq; # Get Command-Line options. our ($opt_help, $opt_version, $opt_wnpath, $opt_outfile, $opt_smooth); &GetOptions("help", "version", "wnpath=s", "outfile=s", "smooth=s"); # Check if help has been requested ... If so ... display help. if(defined $opt_help) { &showHelp; exit; } # Check if version number has been requested ... If so ... display version. if(defined $opt_version) { &showVersion; exit; } # Check if path to WordNet Data files has been provided ... If so ... save it. my ($wnPCPath, $wnUnixPath); if(defined $opt_wnpath) { $wnPCPath = $opt_wnpath; $wnUnixPath = $opt_wnpath; } elsif (defined $ENV{WNSEARCHDIR}) { $wnPCPath = $ENV{WNSEARCHDIR}; $wnUnixPath = $ENV{WNSEARCHDIR}; } elsif (defined $ENV{WNHOME}) { $wnPCPath = $ENV{WNHOME} . "\\dict"; $wnUnixPath = $ENV{WNHOME} . "/dict"; } else { $wnPCPath = "C:\\Program Files\\WordNet\\3.0\\dict"; $wnUnixPath = "/usr/local/WordNet-3.0/dict"; } # Output file must be specified unless(defined $opt_outfile) { &showUsage; print "Type 'semCorFreq.pl --help' for detailed help.\n"; exit; } # Initialize POS Map. my %posMap; $posMap{"1"} = "n"; $posMap{"2"} = "v"; # Get a WordNet::QueryData object... print STDERR "Loading WordNet ... "; my $wn = ((defined $opt_wnpath) ? (WordNet::QueryData->new($opt_wnpath)) : (WordNet::QueryData->new())); die "Unable to create WordNet::QueryData object.\n" if(!$wn); $wnPCPath = $wnUnixPath = $wn->dataPath() if($wn->can('dataPath')); my $wntools = WordNet::Tools->new($wn); die "Unable to create WordNet::Tools object.\n" if(!$wntools); print STDERR "done.\n"; # Loading the Sense Indices. print STDERR "Loading sense indices ... "; open(IDX, $wnUnixPath."/index.sense") || open(IDX, $wnPCPath."\\sense.idx") || die "Unable to open sense index file.\n"; while() { chomp; my @line = split / +/; if($line[0] =~ /%([12]):/) { my $posHere = $1; $line[1] =~ s/^0*//; push @{$offsetMnem{$line[1].$posMap{$posHere}}}, $line[0]; } } close(IDX); print STDERR "done.\n"; # Loading the frequency counts from 'cntlist'. print STDERR "Loading cntlist ... "; open(CNT, $wnUnixPath."/cntlist") || open(CNT, $wnPCPath."\\cntlist") || die "Unable to open cntlist.\n"; while() { chomp; my @line = split / /; if($line[1] =~ /%[12]:/) { $mnemFreq{$line[1]}=$line[0]; } } close(CNT); print STDERR "done.\n"; # Mapping the frequency counts to offsets... print STDERR "Mapping offsets to frequencies ... "; my $unknownSmooth = 0; foreach my $tPos ("noun", "verb") { my $xPos = $tPos; my $line; $xPos =~ s/(^[nv]).*/$1/; open(DATA, $wnUnixPath."/data.$tPos") || open(DATA, $wnPCPath."\\$tPos.dat") || die "Unable to open data file.\n"; while($line=) { next if " " eq substr $line, 0, 2; $line =~ /^([0-9]+)\s+/; my $offset = $1; $offset =~ s/^0*//; if(exists $offsetMnem{$offset."$xPos"}) { foreach my $mnem (@{$offsetMnem{$offset."$xPos"}}) { if($offsetFreq{"$xPos"}{$offset}) { $offsetFreq{"$xPos"}{$offset} += ($mnemFreq{$mnem}) ? $mnemFreq{$mnem} : 0; } else { # [old] # Using initial value of 1 for add-1 smoothing. (added 06/22/2002) # $offsetFreq{$offset} = ($mnemFreq{$mnem}) ? $mnemFreq{$mnem} : 0; # [/old] # No more add-1 (09/13/2002) # Option for add-1 ! (05/01/2003) $offsetFreq{"$xPos"}{$offset} = ($mnemFreq{$mnem}) ? $mnemFreq{$mnem} : 0; if(defined $opt_smooth) { if($opt_smooth eq 'ADD1') { $offsetFreq{"$xPos"}{$offset}++; } else { $unknownSmooth = 1; } } } } } else { # Code added for Add-1 smoothing (06/22/2002) # Code changed... no more add-1 (09/13/2002) # Code changed... option for add-1 (05/01/2003) $offsetFreq{"$xPos"}{$offset} = 0; if(defined $opt_smooth) { if($opt_smooth eq 'ADD1') { $offsetFreq{"$xPos"}{$offset}++; } else { $unknownSmooth = 1; } } } } close(DATA); } print STDERR "done.\n"; print "Unknown smoothing scheme '$opt_smooth'.\nContinuing without smoothing.\n" if($unknownSmooth); # Removing unwanted data structures... print STDERR "Cleaning junk from memory ... "; undef %offsetMnem; undef %mnemFreq; print STDERR "done.\n"; # Determine the topmost nodes of all hierarchies... print STDERR "Determining topmost nodes of all hierarchies ... "; my $topHash = WordNet::Similarity::FrequencyCounter::createTopHash($wn); print STDERR "done.\n"; # Propagate the frequencies up... print STDERR "Propagating frequencies up through WordNet ... "; my $newFreq = WordNet::Similarity::FrequencyCounter::propagateFrequency(\%offsetFreq, $wn, $topHash); print STDERR "done.\n"; # Write out the information content file... print STDERR "Writing infocontent file ... "; open(DATA, ">$opt_outfile") || die "Unable to open data file for writing.\n"; print DATA "wnver::".$wntools->hashCode()."\n"; foreach my $offset (sort {$a <=> $b} keys %{$newFreq->{"n"}}) { print DATA $offset."n ".$newFreq->{"n"}->{$offset}; print DATA " ROOT" if($topHash->{"n"}->{$offset}); print DATA "\n"; } foreach my $offset (sort {$a <=> $b} keys %{$newFreq->{"v"}}) { print DATA $offset."v ".$newFreq->{"v"}->{$offset}; print DATA " ROOT" if($topHash->{"v"}->{$offset}); print DATA "\n"; } close(DATA); print STDERR "done.\n"; print STDERR "Wrote file '$opt_outfile'.\n"; # ---------------------- Subroutines start here ------------------------- # Subroutine to display Usage sub showUsage { print "Usage: semCorFreq.pl [{ --outfile FILE [--wnpath PATH] [--smooth SCHEME] | --help | --version }]\n"; } # Subroutine to show detailed help. sub showHelp { &showUsage; print "\nA helper tool Perl program for WordNet::Similarity.\n"; print "This program is used to generate the frequency count data\n"; print "files which are used by the Jiang Conrath, Resnik and Lin\n"; print "measures to calculate the information content of synsets in\n"; print "WordNet.\n"; print "\nOptions:\n"; print "--outfile Name of the output file (FILE) to write out the\n"; print " information content data to.\n"; print "--wnpath Option to specify the path to the WordNet data\n"; print " files as PATH.\n"; print "--smooth Specifies the smoothing to be used on the\n"; print " probabilities computed. SCHEME specifies the type\n"; print " of smoothing to perform. It is a string, which can be\n"; print " only be 'ADD1' as of now. Other smoothing schemes\n"; print " will be added in future releases.\n"; print "--help Displays this help screen.\n"; print "--version Displays version information.\n"; } # Subroutine to display version information. sub showVersion { print "semCorFreq.pl version 2.05\n"; print "Copyright (c) 2005-2008, Ted Pedersen and Siddharth Patwardhan.\n"; } 1; __END__ =head1 NAME semCorFreq.pl - Compute Information Content from SemCor sense-tagged corpus =head1 SYNOPSIS semCorFreq.pl [{ --outfile FILE [--wnpath PATH] [--smooth SCHEME] | --help | --version }] =head1 DESCRIPTION This program is used to generate the default information content file (ic-semcor.dat) that is used by WordNet::Similarity in the Jiang Conrath, Resnik and Lin measures. It uses the cntlist file as provided by WordNet as the source of frequency counts. These are derived from sense tagged corpora which include a portion of the Brown Corpus and the Red Badge of Courage. This collection of of sense tagged text is referred to as SemCor, and is not distributed by WordNet any longer. Also, note that the cntlist file is no longer officially supported by WordNet, so the information provided therein may not be reliable. The SemCor data we use comes from Rada Mihalcea, who has mapped SemCor from its original version to each successive version of WordNet. In this program we ignore the SenseTags and simply treat SemCor as raw text. This is to allow for the comparison of the effect of counting from sense tags (as done in L) versus raw or plain word forms (as done here). =head1 OPTIONS B<--outfile>=I The name of a file to which output should be written B<--wnpath>=I Location of the WordNet data files (e.g., /usr/local/WordNet-3.0/dict) B<--smooth>=I Smoothing should used on the probabilities computed. SCHEME can only be ADD1 at this time B<--help> Show a help message B<--version> Display version information =head1 BUGS Report to WordNet::Similarity mailing list : L =head1 SEE ALSO L SemCor Download (from Rada Mihalcea): L WordNet home page : L WordNet::Similarity home page : L =head1 AUTHORS Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. =cut WordNet-Similarity-2.05/CHANGES0000644000175000017500000013170511025550202014260 0ustar sidsidNAME CHANGES - Revision history for WordNet::Similarity DESCRIPTION Version 2.05 (Released 06/16/2008) * 05/30/08 (1) Created new module WordNet::Similarity::FrequencyCounter containing common support code for information content programs. (Sid) (2) Updated all the frequency counting programs in /utils (*Freq.pl) to use the common code in WordNet::Similarity::FrequencyCounter. (Sid) (3) Changed the default path to Perl from /usr/local/bin to /usr/bin in all scripts and tests in the package. (Sid) (4) Fixed incorrect handling of BNC header information. (Sid) (5) Modified the compoundify() method in WordNet::Tools to include compounds containing special characters (period, hyphen, forward-slash, single-quote). (Sid) (6) Updated compoundify() to handle larger compounds. (Sid) * 04/23/08 (1) Fixed the "excessive ROOTs" bug in *Freq.pl. (Sid) (2) Fixed the extra verb concept counts in *Freq.pl. (Sid) Version 2.04 (Released 04/19/2008) * 04/17/08 (1) Reorganized similarity_server initialization. (Sid) (2) The similarity server now prints more intuitive messages. (Sid) (3) Attached timestamps to log messages. (Sid) (4) Added additional checks to input strings from clients. (Sid) * 04/12/08 (1) Added more detailed description of information content to rawtextFreq.pl, and made minor copy editing and formatting changes to other /utils files (TDP) (2) Made minor copy editing and formatting changes to files in /doc (TDP) * 04/10/08 (1) Moved get_wn_info, stem and vectorFile modules under WordNet, i.e., they are now WordNet::get_wn_info, WordNet::stem and WordNet::vectorFile. (Sid) (2) Updated all the modules and programs using the above modules. (Sid) (3) Added copyright notices in all module and program headers. (Sid) (4) Added method getCompoundsList() to WordNet::Tools. (Sid) (5) Made a more distrtibutable version of simialrity_server. The similarity_server is now "daemonized", and is installed in /usr/bin along with the other utils. (Sid) * 03/23/08 (1) Added SIGNATURE to distrribution to enable package verification. (Sid) (2) Updated MANIFEST to reflect new SIGNATURE. (Sid) (3) Set the LICENSE to gpl in META.yml and Makefile.PL. (Sid) * 03/17/08 (1) Added NO_META option to Makefile.PL to prevent automatic generation of META.yml during 'make dist'. (Sid) (2) Removed unused variable "loaded" from Makefile.PL. (Sid) Version 2.03 (Released 03/11/2008) * 03/07/08 (1) Removed all references to WordNet::QueryData from Makefile.PL. This is based on the following advice present in the ExtUtils::MakeMaker documentation: "Module installation tools have ways of resolving unmet dependencies but to do that they need a Makefile". By checking for the presence of WordNet::QueryData during 'perl Makefile.PL', we are preventing any opportunity for automated dependency resolution. (Sid) (2) The WordNet path (if specified by the WNHOME option during 'perl Makefile.PL') is not checked for validity beforehand, and is now directly provided as-is to build/Infocontent.PL and build/Depthfiles.PL. In case of a WNHOME error, now 'make' should fail instead of 'perl Makefile.PL' (which is more appropriate). (Sid) (3) Corrected a typo in DepthFinder.pm synopsis that refered to getTaxonomyRoot rather than getTaxonomies. Removed some cut and paste documentation from the templated used for GlossFinder.pm and PathFinder.pm (Ted) (4) Made synopsis examples WordNet version independent by not hard coding offsets, etc. Did this in Depthfinder.pm, PathFinder.pm, ICFinder, and GlossFinder.pm (Ted) (5) Made minor changes in path names and file names in the /samples directory and the /config-files subdirectory. (Ted) Version 2.02 (Released 03/04/2008) * 03/04/08 (1) Applied patch from Ben Haskell to fix a bug report (submitted by Quang Do Xuan) about failing self-similarity of tilde#n#1 using wup and lch measures. (Sid) (2) Added tests for above bug to t/wup.t and t/lch.t. (Sid) (3) Added WordNet::Similarity package version info to similarity.pl --version. (Sid) * 01/31/08 (1) Changed some default options in the similarity_server.conf configuration. (Sid) (2) Reformatted some of the similarity_server code. (Sid) * 01/10/08 (1) Reduced version requirements of some of the PREREQ_PM modules. (Sid) (2) Changed WordNet::QueryData requirements to v1.40 in the documentation. (Sid) Version 2.01 (Released 10/14/2007) * 10/13/07 (1) Fixed error in loading WordNet::Tools for similarity_server.pl. (Sid) (2) Removed the use of default (hardcoded) stoplist and word-vectors file for similarity_server.pl. (Sid) (3) Print WordNet hash-code instead of WordNet version, for similarity.cgi WordNet version information. (Sid) * 10/09/07 (1) Updated the Pathfinder code to handle loops in the WordNet is-a hierarchy (like the one in WN3.0). (Sid) (2) Updated MANIFEST, changelog and documentation to reflect the new changes. (Sid) * 10/08/07 (1) The modules now are not dependent on the version() method of WordNet::QueryData (which is no longer reliable). Instead they now use a 'hash-code' representing a specific WordNet distribution. (Sid) (2) Added module WordNet::Tools which provides the hashCode and compoundify methods used by most of the other modules and utilities. (Sid) (3) Completely modified the build procedure to generate data files during the 'make' step instead of the 'perl Makefile.PL' step. (Sid) (4) Removed the WordNet version numbers appended to synsetdepths.dat and treedepths.dat. (Sid) (5) Added two "build" utilities -- build/Infocontent.PL and build/Depthfiles.PL -- which are run during the 'make' step to generate data files. (Sid) (6) The default WordNet version is now v3.0. Changed all documentation, code and examples to reflect this. (Sid) (7) The package now requires WordNet::QueryData version 1.46 or above. (Sid) (8) Revised all tests and test-keys for the new code and new version of WordNet and QueryData. (Sid) (9) Removed the multiple pieces of code implementing "compoundify" and moved it all into a single method in WordNet::Tools. (Sid) * 10/04/07 (1) Included a default word vectors file in the distribution and eliminated the creation of a default word vectors file at install time. (Sid) * 02/25/07 (1) Fixed documentation where module WordNet::Similarity::path was referred to as WordNet::Similarity::edge (old name). (Sid) * 01/30/07 (1) Fixed wnDepths.pl man-page to display the wnpath option consistently in the usage and the description. (Sid) (2) Fixed the "deep recursion" error (only with WN3.0) in the findWPSDepths() subroutine in the wnDepths.pl script. (Sid) Version 1.04 (Released 12/13/2006) * 12/13/06 (1) Fixed major bug reported in vector_pairs, where every alternate function is skipped because of a loop variable being incremented twice. (Sid) * 04/21/06 (1) The web-interface was still not working for the vector measure, because only one side of the client-server interface had been updated. Updated the similarity server with code to support both, vector and vector_pairs measures. (Sid) (2) Updated the description of the Gloss Vector measure in measures.html (web interface). (Sid) Version 1.03 (Released 04/14/2006) * 04/14/06 (1) Applied Ben Haskell's patch to ICFinder.pm (to make the behaviour of the probability() and IC() functions consistent with their comments). * 04/05/06 (1) Updated the names for the Extended Gloss Overlaps measure and the Gloss Vector measure in the documentation. (Sid) * 02/19/06 (1) Updated PODs for all modules. (Sid) (2) Added tests for POD errors and for POD coverage. (Sid) * 03/31/06 (1) Changed "hash-style" constants (Perl v5.8) to single line constants (Perl v5.6) for compatibility with Perl v5.6.0. (Sid) Version 1.02 (Released 02/07/2006) * 02/06/06 (1) Added utility rankFormat.pl for ranking the output of similarity.pl and making the output suitable for input to rank.pl (to compute Spearman's correlation coefficient) of the Text::NSP package. (Sid) * 01/15/06 (1) Fixed issue in lesk.pm where undefined values for $wc1 and $wc2 caused errors with the normalize option. (Sid) (2) Fixed minor UI issues in wnDepths.pl. (Sid) Version 1.01 (Released 12/21/2005) * 12/09/05 (1) Modified get_wn_info.pm with Wybo Wiersma's changes. (Sid) (2) Modified lesk.pm, vector.pm and vector_pairs.pm to be compatible with above changes. (Sid) * 12/07/05 (1) Updated all utilities to use WordNet 2.1 (WordNet::QueryData 1.39 or above). (Sid) (2) Updated all modules and test cases for WordNet 2.1. (Sid) * 12/05/05 (1) Changed order of authors in package documentation. (Sid) Version 0.16 (Released 12/12/2005) * 12/01/05 (1) Added Wybo Wiersma's super-gloss caching code to GlossFinder.pm. (Sid) (2) Updated documentation to reflect above changes. (Sid) Version 0.15 (Re-released 12/11/2005) * 12/11/05 (1) tar file unpacked as WordNet-Similarity for June 12, v 0.15, now unpacks as WordNet-Similarity-0.15, which is consistent with all previous versions. (Ted) (2) Similarity.pm version was shown as 0.14, is now 0.15. Our general convention for modules is that their version number only change when the module itself changes, so the module version number can tell you when was the last time a module changed. However, for Similarity.pm this is needlessly confusing, so it will always carry the same version number as the release. (Ted) Version 0.15 (Released 6/12/2005) * 06/10/05 (1) Fixed a minor bug in MANIFEST. (Sid) (2) Updated modules.pod and developers.pod to reflect new software architecture. (Jason) Version 0.14 (Released 6/9/2005) * 06/08/05 (1) Re-introduced the previous (non-pairwise-comparison) vector. (Sid) (2) Updated documentation and test cases to support the new vector measure. (Sid) (3) Added default relation file for new vector measure. (Sid) (4) Expunged erroneous references to LCSFinder, esp. in test scripts. (JM) Version 0.13 (Released 5/9/2005) * 04/21/05 (1) removed LCSFinder module; moved LCS methods to DepthFinder, ICFinder, and PathFinder (JM) (2) renamed vector measure vector_pairs (JM) * 03/24/05 (1) Modified the documentation to reflect the relation file format for vector and for lesk. (Sid) * 03/02/05 (1) Set up selective test cases for "make test", depending upon the default data files installed by user. (Sid) * 02/24/05 (1) Reinstated default relation files for vector and lesk. In case the default relation files (vector-relation.dat and lesk-relation.dat) are missing, both modules would default to the glosexample-glosexample relation. (Sid) (2) Modified Makefile.PL to query the user before installing default data files. (Sid) (3) Removed infocontent file generation code from Makefile.PL. Now Makefile.PL simply calls utilities from the /utils directory (wnDepths.pl, semCorFreq.pl and wordVectors.pl) to generate the all default data files. (Sid) (4) Installation process now generates a default word vectors file. The vectordb configuration variable for vector is now optional. (Sid) (5) Earlier, the WNHOME option was given to Makefile.PL as --WNHOME , whereas the PREFIX option was written as PREFIX=. This inconsistent (and potentially confusing) notation has now been fixed. Now, the WNHOME option is provided to Makefile.PL as WNHOME=. (Sid) (6) Added some basic tests for vector in t/vector.t. * 12/11/04 (1) Created WordNet::Similarity::GlossFinder.pm, a super-class of WordNet::Similarity::vector and WordNet::Similarity::lesk. (Sid) (2) Removed default relation file for lesk. Vector and lesk both default to glosexample-glosexample. (Sid) Version 0.12 (Released 10/29/04) * 10/29/04 (1) Added vector to the CGI interface. (JM) (2) Incorporated a configuration file into similarity_server.pl. (JM) * 10/28/04 (1) Removed readDB.pl. (JM) * 10/27/04 (1) Modified string overlap finding in lesk to use the Text::OverlapFinder module. Removed string_compare.pm. This fixed an old bug where the relatedness of word1 and word2 wasn't always equal to the relatedness of word2 and word1. (JM) (2) Updated Makefile.PL, INSTALL, and doc/install.pod to reflect new dependency on Text::OverlapFinder. (JM) (3) Removed lib/dbInterface.pm and lib/string_compare.pm from MANIFEST. (JM) * 10/19/04 (1) Word vectors no longer stored in a BerkeleyDB database, a plain text file is now used. Modified wordVectors.pl, WordNet::Similarity::vector to use the plain text word vectors file. New module vectorFile.pm now used to access this plain text database. Module dbInterace.pm is obsolete. (Sid) (2) Modified Makefile.PL to no longer check for BerkeleyDB dependency. All modules are installed. (Sid) Version 0.11 (Released 09/23/04) * 09/23/04 (1) Fixed bug in wup that allowed some relatedness scores to be greater than 1. This bug is discussed in the archives of the mailing list. (JM) Version 0.10 (Released 09/03/04) * 09/01/04 (1) Modified vector to look like the other measures. It now is derived from WordNet::Similarity.pm. (Sid) (2) Updated the MANIFEST. (Sid) (3) Fixed some minor typos in Makefile.PL. (Sid) (4) Added single test case (for vector) to t/access.t. (Sid) (5) Fixed config option name conflict in WordNet::Similarity.pm. (JM) (6) Fixed WNHOME and WNSEARCHDIR related bugs. (JM) (7) Updated documentation for the web interface. (JM) Version 0.09 (Released 05/19/04) * 05/19/04 (1) Fixed over-counting problem in *Freq.pl programs. Under certain conditions, word senses would sometimes get counted twice. (JM) (2) Updated *Freq.pl programs to use WordNet 2.0. (JM) (3) Input files to rawtextFreq.pl are now specified with the --infile option. (JM) (4) Improved speed of compound identification in rawtextFreq.pl by adding ',', ';', and ':' to the list of characters that we consider to be the end of a sentence (compound identification time is proportional to the square of the length of the sentence). (JM) Version 0.08 (Released 04/28/04) * 04/28/2004 (1) Created a CGI-based web interface for the relatedness modules. (JM) * 04/19/2004 (1) Fixed problem with path to Perl interpreter in Makefile.PL. This was causing problems during installation if there was no /usr/local/bin/perl. (JM) (2) wnDepths.pl had forgotten that on Windows some filenames are different; for example, data.noun is noun.dat. (JM) Version 0.07 (Released 03/24/04) * 03/23/2004 (1) In /t, save diff files between 0.06 and 0.07. Make sure to run diff tests for path/0.07 and edge/0.06. * 03/16/2004 (1) make sure that every .pm and .pl file has the same GNU copyleft language. Use PathFinder.pm as a template. (2) make sure that documentation is clear that vector and lesk require different format relation files (ie they are not interchangeable). (3) convert README into a series of pod documents in doc directory. In the intro.pod, provide a table of contents like structure (much like perldoc perl does). Make sure that each pod documents follows the cpan style (name, synopsis, etc.) This should be true of any pod documentation in the package. (4) Modify INSTALL to describe local install correctly. In particular, the description of how to do a 'use lib' or -I may need adjustment. * 03/12/2004 (1) Make developers.pod into a self contained document that provides a step by step tutorial on how to write a measure of relatedness. The file NewStats.txt in NSP provides an example of the style of presentation that is expected. (2) developers.pod should be a tutorial that explains how to create a new measure. It should take the reader through a complete example, such as creating a measure that returns the sum of the information content of the concpets found in the shortest path between two concepts. This should include an example of how to use all of the available configuration options, and also adding a new one. * 03/11/2004 (1) document measure modules (lch.pm, wup.pm, etc.) with information about effect of hypo root node. (Take discussion from email explaining why it has an effect, and why it doesn't have an effect) and make it a part of the .pm perldoc. This will eventually be used in thesis writing, so it should be complete and detailed. Of particular important is the behavior of lch.pm, but all of the modules should have their expected behaviour with and without the hypo root node clearly documented. Also, you should note what the behavior was in 0.06 for both nouns and verbs, and if this has changed. * 03/09/2004 (1) lch.pm does not yet support not having a hypo root. Remember that the lack of hypo root will change (potentially) the max path length found for each taxonomy. * 03/08/2004 (1) depth finding code should be contained with DepthFinder.pm. We should not do any depth finding on the fly, rather that should all be precomputed (like we do info content). That includes the depth of individual concepts, and the max depths of taxonomies. (2) When wup.pm encounters two or more paths to the root, the trace output "condenses" those paths into a single path. It would be better to show all paths in the trace (as res does, for example). Also, make sure that the depth reported in such cases is always the minimum (shortest path to root). * 03/05/2004 (1) Modify wnDepths such that it shows both the depths of individual concepts, as well as the max distance from a root node. In the case of multiple inheritance, wndepths should show the depth of the concept in each case, and also the relevant root node. wnDepths should sort these depths from shortest to longest. The output of wndepths should be formatted like infocontent.dat, anticipating an eventual merger. * 03/02/2004 (1) in docs, update/replace current discussion of modules. Include example usage as well. Make sure that path length is clearly defined for lch, edge, and wup. * 02/25/2004 (1) In PathFinder.pm, Infocontent.pm, Similarity.pm, and LCSFinder.pm each function should be documented in perldoc form such that their input, output and basic functionality is described. This should then appear in the DESCRIPTION portion of the perldoc. The SYNOPSIS should contain examples or templates of each function being used. * 02/23/2004 (1) redo random pairs testing such that we have 60 noun-noun pairs, 25 verb-verb pairs, and 15 mixed pairs. * 02/20/2004 (1) Revisit the distance versus similarity issue in jcn.pm. It maybe be that simply inverting the distance is too extreme a solution. One possibility is to make it a linear transformation via maxdist - dist instead. (JM - we'll stick with inverting the distance, but added a discussion of this issue to the documentation) * 02/18/2004 (1) document all multiple inheritance issues that are being handled for measures. * 02/16/2004 (1) validateSynset should check wps format fairly closely, and issue descriptive errors if the wps is ill formed. Words can apparently be about anything (except #) but pos should be lower case nvra, and senses should be digits. Error messages should point out which field is the problem, or if there are too few or too many fields. (2) place all hypo root handling node code in PathFinder.pm. The measures should not have any hypo root handling code in them. (3) PathFinder.pm should include a function getAllPaths.pm that returns all paths between two concepts, their length, and their "tops" (the candidate LCSs). This should be used as the main source of input for the getLCS* functions, and for getShortestPath. (4) remove all "input verifcation" code from the measures. That should be inherited from Similarity.pm. (5) There is replicated code in the measure modules that checks validity of input. This should be removed to a common module that can be called by all of the measures. Any other replicated code should be removed as well. The goal of 0.07 is to largely eliminate replicated code via the use of inheritance, and to make the writing of new measures simpler. * 02/13/2004 (1) add pod/perldoc to lib/ICFinder.pm. Should also be done for all other files as they are modified for other reasons. In particular, introductory material that appears in source code comments, author information, GPL, etc. should be moved into pod and removed from source code comments. See similarity.pl for an example. (2) path should use getShortestPath from PathFinder.pm. * 02/09/2004 (1) getLCSDepth, getLCSInfo, getLCSPath should appear in LCSFinder.pm, which should inherit from both ICFinder and Pathfiner. (2) The measures (lch, path, jcn, lin, res, wup) should default to having the hypo root node turned on (for both nouns and verbs). This will eventually be true of hso, but is not currently. hypo root nodes could also be used for lesk and vector, although they are not currently. * 02/04/2004 (1) Wps and offsets will be supported internally. The user can request either mode via an option to getRelatedness. offset is our default. profiling has shown wps to be somewhat faster, in that it makes fewer calls to getSense, although it does make some. For input, we only support wps. For trace output we support wps and offset. For output we support wps and offset. * 01/29/2004 (1) modify option in config files such that an option without a value reverts to the default in all cases (except vectordb). * 01/24/2004 (1) Provide support for undefined values in the path finding and info content measures (path, wup, lch, res, lin, jcn). If two concepts are not in the same taxonomy then an error should be issued and a large negative integer should be returned. This can occur in two cases, between the same part of speech (noun-noun, verb-verb), or between nouns and verbs. Distinct error messsages should be indicated in both cases. * 01/20/2004 (1) Clean up configuration file examples (in samples). Make them consistent by having a master list (all-options.conf) that is what we make changes to. Then specific example files can be created via copy and paste. Make sure all possible options for a measure are included, and that the explanations describe all possible values as well as default handling. (TDP updated all-options.conf on 12/10/03, use this as source of cut and paste). * 01/19/2004 (1) Create test scripts that can be run to verify the correctness of output - they should include "correct" answers that can be compared to (automatically) and rerun as the system changes. We should use the CPAN module Test::More, and create .t files in a /t directory that test specific situations/problems, etc. The .t files themselves should be documented with an explanation of what is being tested. We should have lots of smaller, specific .t tests (rather than a few big test files). Whenever a bug is found and fixed, a .t file should be created that tests the fix, and this should be mentioned in the source code comments where the fix is made (this fix is tested by t/xyz.t). Make sure that the testing system can be easily extended/modified, and that it can support the use of multiple input files and configuration files. We should have multiple *.t files to run our tests, and each module and utility should have at least its own *.t file (maybe more than one in some cases). We should also have *.t files that are dedicated to particular situations that affect a number of measures (like what happens when info content is zero for one concept, what happens if one of the concepts being compared is the lcs of the other, what if the two concepts are the same (self similarity), and so forth. (2) Test cases for configuration file handling should include: repeated options in configuration file, as in trace::0 trace::1 bad values in configuration file, as in trace::nothankyou bad options in configuration file, as in tracer::0 (3) Test cases for similarity.pl should include: ill formed file input for similarity.pl, as in cat#dog#1 cat#n#2 cat#n#n cat#n#2 cat (4) Test cases for measures should include: show that wps and offset methods of path finding are equivalent check trace output for each of the measures. use wps format, as that is subject to fewer changes than offsets. a "big" file of word pairs (maybe 100 pairs) that run all the measures and compare values to what is obtained in 0.6. If there are differences, let's see what they are. (5) Test cases for information content programs should include: an information content file based on one of our resident text files that is large enough to be interesting (readme, gpl, etc.) as computed in 0.6/0.7 (should be the same). This can be used as a reference point when we make changes in future. Information content computed with a very small number of concepts, to expose the counting problem that ted mentions below. (6) Test cases for wnDepth... Generate output for 0.07 to use as a point of reference. A few specific manual checks would be good too (leather_carp, entity, etc.) (7) run tests to determine where the system now provides different results from version 0.06 - make sure to document these cases (that are different). * 01/12/2004 (1) document configuration options extensively in a separate pod called doc/config.pod. Organize such that you have options that are used with all measures, and then those that are used with certain classes of measures. Then, use this as a master copy to update .pm files with. * 01/09/2004 (1) modify option handling such that multiple occurrences of an option in a config file cause an error. For example trace:: trace::1 should cause an error. * 12/17/2003 (1) SemCor1.7Freq.pl and SemTagFreq.pl need to be renamed. They are now called semCorRawFreq.pl and SemCorFreq.pl. semCorRawFreq.pl counts without sense tags and SemCorFreq.pl counts the sense tags. (TDP) * 12/09/2003 (1) In similarity.pl cache error strings that indicate that two input synsets are from different parts of speech so that we only print out a warning once for each unique word1#pos1 word2#pos2 combination (JM) (2) (a) Enhance similarity.pl file handling (for input files). Comments should be allowed - this will help in creation of test data (we can explain in the comment what "case" is being tested by a particular set of pairs. Use standard perl commenting style line starting with a # is a comment. Note that I don't think we can use the convention of # anywhere in a line as being the start of a comment (due to w#p#s) but I think any line that starts with a # can be safely treated as a comment. (JM -- we are using // to indicated the start of a comment) (b) Enhance similarity.pl file handling (for input files). At present if a single word (not a pair) appears on a line, no error is issued. It silently ignores this case. This should result in an error to the effect that the input format is invalid, only one word. Also, I'm not sure what happens if you have more than two words on a line. An error of some sort would also be necessary in that case. Also, I am not sure if similarity.pl checks to see that the words pairs are "well formed", that is to say do they adhere to the word, word#pos, or word#pos#number format. It would be good to have a simple check that verifies we have alphanumeric words, pos of n, v, a, or r, and numeric numbers. (JM) * 12/08/2003 (1) Clean up configuration file examples (in samples). Make them consistent by having a master list (all-options.conf) that is what we make changes to. Then specific example files can be created via copy and paste. Make sure all possible options for a measure are included, and that the explanations describe all possible values as well as default handling. (JM)(TDP updated all-options.conf on 12/10/03, use this as source of cut and paste). (2) Determine if it is feasible (not too difficult or time consuming) to modify --version option so it can display both the version of similarity.pl and the version of the module used when --type is specified. (JM -- version will show module version as well if a module is specified) * 12/05/2003 (1) all configuration options are now printed to traceString after module initialization. (JM) (2) explain the distinction between compounds and collocations raised in sample README. (Drop the distinction, and clarify what we mean by Wordnet compounds. TDP Dec 3). (JM) * 12/04/2003 (1) document caching for random (normally random uses an unlimited cache size) (JM -- random now uses the same default as all other measures) (2) determine a reasonable default cache size. Should not be unlimited. Current default is 1000, maybe it can be increased to 5000 or 10000. Let lesk with trace be the standard as to what is reasonable. (JM -- default is now 5,000). (3) Improve error handling when processing config files. Make sure the values specified are valid and that filenames refer to extant files. All options should allow the value to be omitted, in which case the default is used. (JM) * 12/01/2003 (1) Adjust Makefile.PL to account for new contents of samples directory. Added entries to MANIFEST as well. (JM) (2) update samples/sample.pl to run with the new files (and organization) provided in the samples directory. This was also a problem in 0.06, where it did not run for hso properly due to a mismatch in the name specified in sample.pl and the configuration file. (3) Rename infocontent.dat in Makefile.PL to use our standard name for semcor information content files. Name should reflect options used in computing information content values (if any). JM (4) relation.dat is in lib/WordNet. Should be referred to as lesk-relation.dat. Should also have vector-relation.dat I would think. (if not, what does vector do?). JM (vector doesn't try finding a default relation file--it fails silently). (5) /sample/vector-relation.dat is wrong. Calls itself LeskRelationFile. JM (6) In intro.pod, provide instruction on how to convert to html or whatever if user wishes (just point them to documentation that describes this elsewhere even). JM * 11/28/2003 (1) remove wordnet 1.7.1 compounds from samples directory. (TDP) (2) change comment in Similarity.pm to explain the pluses and minuses of using/not using a unique root node. (JM) * 11/26/2003 (1) added info content files in samples/Infocontent (2) changed version numbers to 0.07 in all modules and utils (3) fixed bug in wup: if user supplies car#n#1 and auto#n#1, the LCS found by wup is motor_vehicle#n#1, not car#n#1 (4) added POD to all programs in /samples * 11/24/2003 (1) added documentation (in the form of POD) to /doc * 11/21/2003 (1) added /doc directory to contain documentation * 11/18/2003 (1) ensured that each measure initializes a part-of-speech list in _initialize (2) all measures (except vector) now use fetchFromCache and storeToCache (3) updated README: (a) Replaces most references to WordNet 1.7.1 with 2.0 (b) Add some documentation on how to write a new measure (4) added an INSTALL file (5) cleaned up /samples. relation.dat is now named lesk-relation.dat and added vector-relation.dat. A sample config file is also provided for each measure (in /samples/config-files) * 11/15/2003 (1) updated jcn, hso, random, and lesk to use the funcitions that have been moved to Similarity.pm (such as the cache management functions). (2) cleaned up the /samples directory. Removed outdated files. Put sample config files in samples/config-files. Added README in /samples. * 11/12/2003 (1) Added fetchFromCache() and storeToCache() to Similarity.pm to make caching easier and cleaner. (2) Updated wup, edge, lch, res, and lin to use fetchFromCache() and storeToCache(). * 10/25/2003 (1) Reduced the amount of duplication code in the measure modules by moving some common code to WordNet::Similarity. WordNet::Similarity is now a base class for all the measures. Also added a module called infocontent.pm from which all information content measures are descended (i.e., res, lin, jcn). (2) Removed @ symbol from all email addresses in all files (I think). This might help keep spammers from harvesting our email addresses. Version 0.06 * 10/18/2003 (1) Removed dependence of the vector measure on PDL. Implemented "in-house" sparse vector manipulation functions. (2) Modified the README with updated documentation of similarity.pl (--interact option) and wordVectors.pl. * 10/15/2003 (1) Changed Makefile.PL so that it checks for version 1.30 of QueryData * 10/13/2003 (1) Added "maxCacheSize" option to all measures. (2) Added "maxCacheSize" option info to the man/pod documentation. (3) Used the new dataPath() method of QueryData 1.31 in all the utilities to obtain the path of the WordNet data files. (4) Modified Makefile.PL to check for PDL and BerkeleyDB dependency during installation. vector.pm is not installed on failed dependencies. * 10/11/2003 (1) Replaced instances of deprecated WordNet::QueryData::query with WordNet::QueryData::queryWord in hso.pm (2) made hso.pm check QueryData version. queryWord was broken in QueryData 1.29 and earlier (3) added support for new relations in WordNet 2.0 to get_wn_info.pm (4) updated test scripts to work with WN 2.0 (and WN 1.7.1) * 10/06/2003 (1) Added rootNode option to wup.pm * 09/27/2003 (1) Fixed syntax error in wordVectors.pl. (2) Added readDB.pl to utils. (3) Changed contact information in docs. (4) Re-organized the samples subdirectory. (5) Fixed typo in random.pm. (6) Updated the MANIFEST. * 09/21/2003 (1) Updated POD for WordNet::Similarity::wup (2) Added option to wup to specify a cache size in a configuration file. (3) similarity.pl now 'use's QueryData 1.30 or later. Previous versions of QueryData will not work. t/access.t also 'use's QueryData 1.30. get_wn_info.pm and lesk.pm both check for QueryData 1.30 and will die if it not found. (4) Reorganized the bibliography in README and slightly re-worded part of the introduction. * 09/18/2003 (1) Added new Wu Palmer measure of similarity (lib/WordNet/Similarity/wup.pm) (2) Updated README to mention wup (3) Added t/wup.t (4) Updated POD for WordNet::Similarity to mention wup (5) Updated the help message of similarity.pl to mention wup (6) Added t/wup.t and lib/WordNet/Similarity/wup.pm to MANIFEST * 09/05/2003 (1) Added '--interact' option to similarity.pl. (2) Changed the structure of the Vector Relation File. (3) Fixed a minor bug in similarity.pl. (s///g) (4) Updated the perldocs for the measures. (5) Incorporated some new features into the 'wordVectors.pl' utility. These features were used for thesis experiments. (6) Added documentation about the Lesk and Vector relation files (they have different formats now). Version 0.05 * 06/03/2003 (1) Added new measure of semantic relatedness, based on co-occurrence vectors of WordNet glosses. (2) Set up the package so that similarity.pl and the other perl utilities get installed in "/usr/local/bin". (3) Complete rewrite of similarity.pl with cleaner code and added functionality: (a) Multiple parts of speech can be specified as car#nv (noun and verb forms of car) or cool#nar (noun, adjective and adverb forms of cool). (b) Word senses can now be specified as car#n#2, jump#v#2, etc. (c) Added functionality to similarity.pl to use a local install of WordNet::Similarity modules (in non-standard directories). (d) Output of similarity.pl now specifies the senses that represent the relatedness of two words. (4) Enforced limit on the cache size of modules. (5) Updated README to reflect the changes and to specify options for local installs of similarity.pl and the other utilities. (6) Fixed the perl docs (remove leading spaces). (7) Added mailing list address to documentation -- (http://groups.yahoo.com/group/wn-similarity). (8) Improved jcn and lin tracing ("bird-crane" problem obvious now). (9) Added new utility wordVectors.pl required for WordNet::Similarity::vector module. Version 0.04 * 05/02/2003 (1) *Fixed* newline in traces. (2) *Fixed* blank line bug in brownFreq.pl. (3) *Fixed* "--offset" option bug in similarity.pl. (4) *Fixed* lin measure non-normalized scores... added zero infocontent handling in jcn and lin. (5) New utility rawtextFreq.pl, to generate information content files from plain text. (6) similarity.pl supports option to specify part-of-speech of input words while measuring relatedness. (7) Added option to specify (conifuration / information content) file in similarity.pl. (8) Added Resnik counting option to the information content generation utilities. (9) More documentation on information content utilities. (10) Added Add-1 smoothing option to the information content generation utilities. Version 0.03 * 03/10/2003 (1) Removed trace bug in hso.pm. (2) Added test cases for all modules. Version 0.01 * 02/10/2003 (1) Created CPAN modules from distance ver 0.11. (2) Modules are completely object oriented. (3) Added Adapted Lesk semantic relatedness measure -- lesk.pm. (4) Added simple edge counting semantic relatedness measure -- edge.pm. (5) Added a random relatedness measure -- random.pm. (6) jcn, res and lin measures now support verb hierarchies. (7) Information content files can now be specified as parameters to the modules. (8) Tools provided to build information content files from various publicly available corpora. (9) Various parameters now control the behavior of the modules. These parameters are passed to the modules through 'configuration files'. AUTHORS Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Jason Michelizzi SEE ALSO todo.pod COPYRIGHT Copyright (c) 2005, Ted Pedersen, Siddharth Patwardhan, Satanjeev Banerjee and Jason Michelizzi Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. Note: a copy of the GNU Free Documentation License is available on the web at and is included in this distribution as FDL.txt. WordNet-Similarity-2.05/lib/0000755000175000017500000000000011025552366014041 5ustar sidsidWordNet-Similarity-2.05/lib/WordNet/0000755000175000017500000000000011025552366015423 5ustar sidsidWordNet-Similarity-2.05/lib/WordNet/vectorFile.pm0000644000175000017500000001575111015336622020066 0ustar sidsid# WordNet::vectorFile.pm version 2.04 # (Last updated $Id: vectorFile.pm,v 1.1 2008/03/27 05:13:01 sidz1979 Exp $) # # Package used by WordNet::Similarity::vector module that # computes semantic relatedness of word senses in WordNet # using gloss vectors. This module provides a read/write # interface into the word vectors file. # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota, Duluth # tpederse at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. package WordNet::vectorFile; =head1 NAME WordNet::vectorFile - Provides access to the word vectors database (used by the vector and vector_pairs WordNet::Similarity measures). =head1 SYNOPSIS use WordNet::vectorFile; my ($dCount, $dims, $vecRef) = WordNet::vectorFile->readVectors($filename); WordNet::vectorFile->writeVectors($fname, $dCount, $dims, $vecRef); =head1 DESCRIPTION This module provides a read/write interface into the word vectors data file. This module is used by WordNet::Similarity::vector and WordNet::Similarity::vector_pairs as an interface into the word vectors database. This module abstracts the format of the data file away from the user. =head2 Methods =over =cut use strict; use Exporter; use vars qw($VERSION @ISA @EXPORT @EXPORT_OK %EXPORT_TAGS); @ISA = qw(Exporter); %EXPORT_TAGS = (); @EXPORT_OK = (); @EXPORT = (); $VERSION = '2.04'; =item readVectors This method reads in a word vectors file, and returns the document count, the words corresponding to the dimensions of the vectors, and a reference to an array containing the list of word vectors. Parameters: $file -- the file name of the word vectors file. Returns: ($dCount, $dims, $vecRef) =cut # Read the word vectors from a file. sub readVectors { my $className = shift; my $fname = shift; my $state = 0; my $docCount = 0; my $dimensions = {}; my $vectors = {}; my @parts = (); # Check that input values are defined. return (undef, undef, undef) if(!defined $className || !defined $fname || ref $className); # Read the data. open(IPFILE, $fname) || return (undef, undef, undef); while() { s/[\r\f\n]//g; s/^\s+//; s/\s+$//; if($state == 0) { if(/DOCUMENTCOUNT\s*=\s*([0-9]+)/) { $docCount = $1; } elsif(/--Dimensions Start--/) { $state = 1; } elsif(/--Vectors Start--/) { $state = 2; } } elsif($state == 1) { if(/--Dimensions End--/) { $state = 0; } elsif(/^--Dimensions/ || /^--Vectors/) { return (undef, undef, undef); } elsif($_ ne "") { @parts = split(/\s+/, $_, 2); $dimensions->{$parts[0]} = $parts[1]; } } elsif($state == 2) { if(/--Vectors End--/) { $state = 0; } elsif(/^--Dimensions/ || /^--Vectors/) { return (undef, undef, undef); } elsif($_ ne "") { @parts = split(/\s+/, $_, 2); $vectors->{$parts[0]} = $parts[1]; } } else { return (undef, undef, undef); } } close(IPFILE); # Return the data read. return ($docCount, $dimensions, $vectors); } =item writeVectors This method writes out a list of word vectors to the word vectors file. Parameters: $fname, $dCount, $dims, $vecRef Returns: none =back =cut # Write the word vectors to a file. sub writeVectors { my $className = shift; my $fname = shift; my $documentCount = shift; my $dimensions = shift; my $vectors = shift; # Check that all input values are defined. return 0 if(!defined $className || !defined $fname || !defined $documentCount || !defined $dimensions || !defined $vectors); # Check that the className and filename aren't references. return 0 if(ref $className || ref $fname); # Check that document count is numeric. return 0 if($documentCount !~ /^[0-9]+$/); # Write the data to the file... # WARNING: No integrity check of data is performed. open(OPFILE, ">$fname") || return 0; print OPFILE "DOCUMENTCOUNT=$documentCount\n"; print OPFILE "--Dimensions Start--\n"; foreach my $key (keys %{$dimensions}) { print OPFILE "$key ".($dimensions->{$key})."\n"; } print OPFILE "--Dimensions End--\n"; print OPFILE "--Vectors Start--\n"; foreach my $key (keys %{$vectors}) { print OPFILE "$key ".($vectors->{$key})."\n"; } print OPFILE "--Vectors End--\n"; close(OPFILE); # Success. return 1; } 1; __END__ =head1 SEE ALSO perl(1), WordNet::Similarity(3), WordNet::QueryData(3) http://www.cs.utah.edu/~sidd http://wordnet.princeton.edu http://www.ai.mit.edu/~jrennie/WordNet http://groups.yahoo.com/group/wn-similarity =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 BUGS None. To report bugs, go to http://groups.yahoo.com/group/wn-similarity/ or e-mail "S". =head1 COPYRIGHT AND LICENSE Copyright (c) 2005, Ted Pedersen and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/wordvectors.dat0000644000175000017500006376732211015336623020523 0ustar sidsidDOCUMENTCOUNT=111223 --Dimensions Start-- expletive 0 6 6 measles 1 7 5 unsure 8152 7 6 inept 8151 6 6 issue 8153 212 206 harp 2 7 7 dwelling 3 69 68 pike 8154 14 14 nearest 8155 33 33 taken 8156 218 214 load 8157 57 55 sod 4 5 5 colorado 5 38 37 agitation 8158 32 32 lumbar 6 14 14 potentially 8159 15 14 venice 8160 10 10 trump 7 18 17 cut_of_meat 8 15 15 atomic_number 9 7 6 corn 10 83 79 descend 11 42 42 settling 8161 7 7 malaysian 8162 11 11 cerebrum 12 11 11 gloves 13 13 13 artistic 8163 58 58 algebraic 8164 9 9 exercise 14 150 149 a_lot 15 65 64 batting 16 8 8 lunar 8165 6 6 favor 8167 90 89 pawn 17 9 9 the_true 8166 37 36 rarely 8169 16 16 fibrinogen 8168 6 6 wealthy 8170 16 16 merchant 18 35 35 silky 8171 67 66 pons 8172 5 5 diseased 8173 13 13 satisfying 19 11 11 equator 8174 33 32 crest 20 26 26 siege 21 15 14 mongol 8175 6 6 feline 22 8 8 impersonal 23 7 6 unrealistic 25 9 8 substituting 24 8 8 adrenal 26 9 9 oath 8176 29 26 naturalist 8177 12 12 misrepresentation 27 8 8 estimate 8178 48 47 spikelike 28 8 8 magazine 8179 57 56 characterized 8180 761 759 satisfactorily 8181 5 5 booth 29 11 11 proof 8182 48 47 indirect 31 28 21 appreciable 30 5 5 documentary 32 8 8 verbal 33 45 42 enigmatic 34 5 3 meteoric 35 6 5 northern_europe 36 26 26 unlawful 37 24 22 fit_to 38 21 18 dramatically 8183 7 7 invited 8184 17 16 owned 39 50 49 central_american 8185 12 12 tinted 40 7 7 miserable 42 11 9 nonprofessional 41 6 5 recurrence 43 5 4 refill 8186 7 4 protection 44 75 73 julius_caesar 45 9 9 recover 46 15 15 cola 47 5 5 contested 48 5 5 valentine 8187 5 3 musk 49 9 9 employ 50 29 29 economic_system 52 8 8 peter 51 7 7 callus 53 9 6 permian 54 9 9 humerus 8188 9 8 rupture 8189 10 10 transcribed 55 7 7 hear 56 61 61 truck 57 57 56 joseph 58 6 6 hops 59 17 17 expected_value 8190 5 5 disclosed 60 6 6 show 62 330 321 reducing 61 34 34 planetary 8191 13 11 chicago 63 18 18 lack 8192 304 303 australasian 64 17 17 wheel 8193 88 83 suffragist 8194 8 8 dissension 65 7 7 immediately 66 78 78 cooked 67 139 137 acceptance 8195 44 43 fresh 68 141 138 attendant 8196 32 32 honorary 69 11 11 gather 8197 55 54 bean_plant 70 7 7 handling 8198 28 28 widen 71 11 11 mite 72 26 25 explode 8199 22 22 thoughtless 73 8 8 jupiter 74 26 26 elation 75 7 7 circumscribed 76 6 6 defensive_structure 8200 7 7 stirring 77 18 18 1980s 8201 7 7 lubricant 78 19 19 supply 8202 159 157 final 8203 102 100 urn 79 8 8 rhythmical 80 12 12 for_sale 81 45 45 irreversible 8204 8 8 thickly 8205 19 18 surprise 82 45 45 head_of_state 83 14 13 excess 85 77 77 globose 84 24 24 fleece 8206 7 7 subvert 8207 5 5 orion 86 9 9 concave 87 23 23 immortal 8208 9 9 semblance 88 6 6 ghost 89 16 15 knowledge 8209 207 204 ceramics 8210 12 12 victory 8211 82 82 beet 90 18 18 exhale 8212 9 9 heavily 8213 70 69 square 8214 144 135 attractive 91 92 88 psychic 92 9 9 triumph 93 13 13 tap 94 20 16 capacitance 95 10 10 seeker 8215 5 5 drafting 8216 5 5 degenerate 8217 8 8 public_utility 96 6 6 excitability 8218 5 5 laxative 97 9 9 envy 98 16 16 molecular_weight 8219 6 6 esophagus 8220 21 21 act_on 8221 8 8 unstable 99 48 46 quota 8222 6 6 deal 8223 187 181 gospel 8224 21 21 military_unit 100 23 23 whistling 8225 22 20 owner 101 85 78 syphilis 8226 16 14 subsidy 102 6 6 sumatra 8227 10 10 smudge 103 6 5 seductive 104 5 5 banker 8228 8 8 put_in 107 41 41 hesitation 105 9 9 adjective 106 36 33 wart 8229 17 15 pearl 8230 27 25 beautifully 8231 18 18 voltaic_cell 8232 6 6 sentimental 8233 21 19 mecca 108 6 6 trauma 8234 19 18 inconvenience 109 6 6 grammatical_relation 110 10 10 compelled 111 10 10 dipped 112 18 18 hagfish 8235 11 10 kilogram 114 14 14 earring 113 5 5 order_scrophulariales 8236 6 6 furnish 8237 36 36 suspension 115 25 25 gambling 8238 43 43 whenever 8239 7 7 adultery 116 12 12 exaggerate 8240 6 6 disrespect 117 7 7 overlapping 8241 24 23 neurological 118 6 6 inhale 119 11 10 exposure 120 86 81 radical 121 71 69 stiffly 8242 5 5 web 8243 36 35 neoplasm 8244 9 9 starter 122 6 6 inedible 8245 21 21 set 8246 466 428 teach 123 43 41 navel 124 6 6 architect 125 76 73 tapestry 8247 7 6 job 126 256 245 delinquent 8248 10 10 pious 8249 6 6 stained 127 11 10 strengthening 8250 10 10 midge 128 5 4 resigned 8251 12 12 trillium 8252 5 5 financial_obligation 8253 5 5 unintentional 129 7 6 capture 8254 90 87 wonder 8255 43 39 aorta 8256 26 24 hebrew_alphabet 130 23 23 vanilla 8257 16 15 fidelity 131 8 8 susceptible 8258 40 39 liliaceae 133 25 25 leavening 132 8 8 violet 8259 101 98 gracefully 134 11 11 disobedient 8260 9 9 coordination 135 17 17 detached 136 22 18 hummingbird 8261 5 5 anticipate 8262 9 9 visitor 8264 25 25 brow 137 6 6 doll 8263 11 11 evoked 138 5 5 deeds 139 19 19 crib 140 5 5 religion 141 130 129 gentleman 8265 20 20 brisk 8266 12 11 protected 142 43 43 antisocial 8267 5 4 oppression 143 5 5 perfect 144 65 56 colonies 145 69 69 advertised 8268 8 8 driven 8269 63 63 high_quality 8270 8 8 clarifying 8271 5 5 breastbone 146 14 14 sailor 8272 49 48 go_far 147 5 5 dug 148 22 22 rivet 149 7 6 oyster 8273 37 36 accipitridae 8274 6 6 vaccine 8275 11 11 transit 8276 14 14 protective_covering 8277 38 38 spouse 8278 26 25 collapsible 150 5 5 bordered 8279 9 9 siliceous 8280 5 5 mainly 151 92 92 attach 8281 49 49 drinking 8282 63 60 regular 152 165 156 diligent 153 11 10 mining 154 32 32 inequality 8283 6 6 unfavorable 8284 19 16 relief 8285 50 50 vitamin_d 8286 5 5 telegraph 8287 10 10 accommodation 8288 27 26 dissemination 8289 6 6 countryside 8290 19 19 eventual 155 7 6 cursive 8291 7 7 object 8292 473 461 debased 8293 5 5 invent 156 8 8 pinch 157 8 7 distort 8294 6 6 hindu 158 56 56 marching 8295 27 26 whipped_cream 8296 8 8 northeastern 159 188 188 copious 8297 5 5 personal_property 160 12 12 anesthetize 161 6 6 adapt 8298 22 21 line_of_latitude 162 5 5 register 163 55 54 outfield 8299 8 8 censorship 164 9 9 globulin 8300 7 7 mania 8301 6 6 emperor 8302 45 41 relic 8303 5 4 premature 165 11 11 dumped 8304 8 6 water_wheel 166 7 7 malignant_neoplasm 167 11 11 govern 168 13 13 defy 8305 6 6 discriminating 8306 7 7 crosswise 169 12 11 oblivious 8307 6 5 side_by_side 8308 20 20 disease 8309 533 519 adorned 170 17 17 casting 8310 16 16 geological_formation 8311 5 5 thunder 8313 33 32 stole 8312 6 5 bad 8314 173 145 occasion 171 60 60 blackbird 8315 6 6 gall 172 21 20 pulling 173 50 49 genetics 8316 53 53 mortar 175 20 20 jones 174 13 13 sri_lanka 176 23 23 eloquence 177 7 7 uto 178 7 7 visual_impairment 8317 7 7 put_down 8318 8 8 hot 179 209 188 close_to 8319 38 38 piety 180 8 8 invested_with 8320 9 9 protoctista 181 17 17 tailored 182 8 7 intoxicating 183 8 8 lilies 8321 13 13 enter 8322 177 173 foreign 8323 103 100 unfair 184 18 16 waterfall 8324 22 21 age 8325 209 201 bodied 185 82 81 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stemmed 8359 30 29 hawk 204 35 34 refugee 8360 12 12 extent 8361 141 138 ours 205 5 5 symphony 8363 16 16 reprehensible 8362 13 12 berth 206 7 7 doughnut 8364 11 11 caryophyllaceous 207 5 5 warty 208 18 18 sick 8365 41 38 dun 210 8 8 diving_duck 209 8 8 moss 8366 51 51 manhattan 8367 23 22 introduce 211 181 180 emptied 212 16 16 suction 8368 18 16 fire 213 312 285 vertebral_column 8369 17 17 whirling 215 11 11 reversal 214 11 11 barbed_wire 217 8 8 georgian 216 8 5 marmot 8370 7 7 hadrian 8371 5 5 undomesticated 8372 9 9 graceful 8373 41 41 dreamy 218 8 8 omission 8374 16 16 low_temperature 8375 7 7 experience 219 232 222 legislative 8376 42 42 tonsil 8378 8 7 hydraulic 8377 5 5 topic 8379 56 56 squad 220 8 8 good_sense 8380 9 9 transformer 221 6 6 intervening 8381 26 25 luck 222 14 14 diplomat 223 31 31 negatively 224 6 6 desired 8382 74 74 drainage 8383 9 9 beach 225 65 64 pharaoh 226 6 6 hair 227 443 409 inflamed 229 19 18 strive 228 10 10 surprise_attack 8384 8 8 pneumonia 230 21 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advocate 265 135 134 utility 266 18 18 whalebone_whale 8405 5 5 stolen 8406 16 16 recovering 8407 6 6 college 267 114 111 provide 8408 638 631 supreme 268 43 40 appreciated 269 8 8 contraceptive 270 10 10 sliver 8409 6 6 neighbor 8410 42 40 bertrand_russell 271 6 6 cuticle 272 6 6 commentary 273 7 7 social_event 274 6 6 modern_english 275 6 6 cubist 8411 5 5 molded 276 21 21 noxious 8412 12 11 stooped 277 6 6 downright 8413 5 4 anothers 8414 22 22 colonel 8415 6 6 understanding 278 85 84 untied 8416 5 5 sheer 8417 16 14 damaged 8418 39 37 calvinist 279 5 5 imported 8419 10 10 identified 8421 75 75 warming 8420 8 8 aeronautics 280 7 7 original_sin 8422 5 5 collaborate 281 31 31 software 282 33 32 inquiries 283 5 5 damn 8423 5 5 fast 284 152 139 peeling 8424 12 10 tonal 285 7 7 french_polynesia 8425 7 7 instinct 8426 15 14 dare 286 8 7 poet 288 166 165 microscopy 287 6 6 friction 289 38 38 mischief 290 5 5 continuation 8427 21 21 guatemala 8429 18 18 twilight 8428 8 7 flexibility 291 8 8 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27 27 annoyance 8651 21 21 world_war_ii 540 92 92 prefix 541 9 8 confused 542 53 50 remainder 543 13 13 park 8652 42 39 ridden 544 10 9 firing 8653 35 35 dialectic 8654 5 5 utopia 8655 5 5 lookout 8656 7 7 disagree 545 14 14 armed 546 62 60 explosion 547 30 30 axon 8657 9 8 align 548 11 10 moslem_calendar 549 12 12 frankly 8658 7 7 lessen 8659 19 18 eighty 8660 12 12 slimy 8661 17 17 humor 8662 32 32 seven 8663 82 78 penalties 8664 5 5 documentation 550 6 6 emetic 551 7 7 food_poisoning 552 9 8 solved 553 15 15 computer_file 8665 9 9 alloy 8666 74 72 faulty 8667 15 15 thickness 8668 25 25 farmer 554 32 31 screen 556 69 63 textured 555 17 16 entanglement 8669 5 5 deplorable 8671 5 5 clinker 8670 5 3 harpoon 8672 8 7 pipe 557 81 78 convinced 8673 9 9 bordeaux 558 12 12 torpid 8674 6 6 fallout 559 6 6 sand 8675 119 116 rub 561 23 22 anthropoid 8676 7 7 malay 560 5 5 defense 562 68 67 beethoven 563 13 12 inoffensive 564 11 10 delayed 8677 22 21 tobacco 8678 70 67 slime 8679 14 14 annoyed 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humidity 10014 8 8 roughly 1859 21 21 radius 10015 18 17 underwear 10016 9 9 crowded 1860 43 40 burlesque 10017 5 4 point 10018 632 592 dredge 10019 5 5 assumed 1861 60 58 confirming 1862 5 5 corpses 1863 7 7 west_african 1864 11 11 preoccupied 10020 14 14 rosy 1865 24 24 pinned 1866 7 7 mozambique 1867 7 7 yiddish 10021 6 6 trap 1868 44 42 dissatisfaction 10022 7 7 bulb 10023 68 66 shellfish 10024 10 10 green_manure 10025 9 9 simple_machine 1869 5 5 country_music 1870 5 5 consecrated 10026 11 10 twin 1871 13 13 extortion 10027 5 5 cox 1872 11 8 expectation 1873 47 46 insectivorous 10029 30 30 clumsy 10028 16 16 conviction 1875 26 26 amplitude 10030 21 20 spinal 1874 16 16 speech_act 10031 14 14 pipes 1877 50 50 inquiring 1876 6 5 embody 10032 10 10 complimentary 1878 5 5 visible 10033 118 113 sandal 10035 10 10 free_will 10034 7 7 a_hundred 10036 6 6 geneticist 1879 9 9 glide 10037 7 7 click 1880 23 20 commemorating 10038 16 16 ozone 1881 6 6 scripture 10039 8 8 joule 1882 5 5 belongings 1883 5 5 florentine 10040 8 8 motorized 1884 6 6 coordinate_system 1885 9 9 heraldic 10041 7 7 vice 1886 23 22 vaulting 10042 5 5 siren 1887 5 5 insubstantial 1888 6 5 dozen 10043 7 7 sparrow 1889 13 13 sociable 1890 11 8 premises 10044 24 22 voyage 1891 21 21 polymer 1892 22 21 low 1893 479 455 railway 1894 28 27 classic 1895 9 9 downfall 1896 8 8 fifty 1897 22 19 transistor 10045 8 8 furnishings 1898 11 11 sixth 10046 19 19 modification 10047 12 12 cradle 1900 13 12 revert 10048 6 6 sleet 1899 7 7 refrigerator 10050 20 19 invoke 10049 16 15 dazed 10051 6 6 tangible 1901 19 18 divorce 1902 29 29 gene 1903 91 82 incest 10052 5 5 pierced 1904 15 13 floral 10054 8 8 truncated 10053 7 7 asia_minor 10055 37 37 dingy 1905 16 16 trot 10056 17 16 ivory 1906 18 18 developed 1907 223 218 come_into 1908 29 29 inhabit 1909 85 85 parish 10057 10 10 official_document 1910 5 5 sophistication 10058 9 9 linear_unit 10059 10 10 presumptuous 1911 5 5 salamander 1913 43 42 worked_up 1912 6 6 lisp 1914 5 5 soundly 10060 7 7 formal 1915 189 180 heroin 10061 8 8 extravagance 10062 9 9 largeness 10064 6 6 simplified 10063 6 6 family_nymphaeaceae 10065 5 5 undifferentiated 10066 7 7 finally 10067 76 75 suffer 1916 66 63 germ 1917 10 10 guyana 1918 11 11 congenital_abnormality 1919 5 5 try 10068 146 145 lake_ontario 10069 5 5 spoken 10070 455 449 circumstances 1920 84 83 stealthy 1921 5 5 ceylon 1923 9 9 vain 10071 10 10 worshipper 1922 6 6 photosynthesis 10072 11 11 fell 1924 116 102 surrounded 1925 44 44 direct_object 10073 6 6 patio 10074 5 5 indefinitely 10075 22 22 cursed 1926 9 6 enemy 10077 189 175 courage 10076 52 51 home_plate 10078 8 8 brazen 1927 5 5 snap 1928 19 18 silicon 1929 9 9 pod 1932 142 140 sunday 1931 48 47 conveyed 1930 13 13 psychedelic 1933 6 5 granite 1934 21 20 tuberous 10079 55 54 craniometric_point 1935 22 22 shes 10080 22 22 pelvic 1936 27 26 health_care 10081 18 16 sunburn 10082 5 5 exoskeleton 1937 7 7 leaflike 10083 13 13 apparition 1938 8 8 genital 10085 19 18 in_reality 10084 8 7 statement 1939 171 163 decimal_system 1940 8 8 secretly 1941 31 29 abstain 10086 14 14 dune 1942 14 14 joined 1943 75 74 interrupted 10087 12 12 hatred 1944 21 20 ate 10088 18 18 abrasive 10089 16 16 colorado_river 1945 16 16 as_expected 1946 5 5 vegetative 10090 7 7 exhibit 1947 99 99 untidy 10091 11 9 detect 10092 47 45 raceme 10094 180 180 retina 10093 32 32 drown 1948 10 10 regent 10095 8 7 photography 10096 44 43 correct 1949 86 83 values 10097 112 105 alkalosis 10098 5 4 rehabilitation 1950 5 5 keep_in 1951 16 16 interpreted 1952 11 11 filming 10099 5 5 artery 1953 57 56 come_out 10100 10 9 negotiable 10101 7 7 tent 10102 49 48 profusely 10103 9 9 communist_party 1954 12 12 iroquoian_language 10104 7 7 viewing 10105 11 11 removable 1955 15 14 agreed 10106 53 52 germination 10107 6 6 invested 1956 6 6 flexible 1957 85 84 fastening 10108 20 20 artistic_movement 1958 5 5 touchdown 10109 6 6 closer 10110 17 17 relaxation 1959 25 25 hornet 10111 5 5 crank 10112 12 12 vowel_sound 10113 10 8 bun 1960 6 6 modem 10114 5 5 propose 10115 18 18 bends 1961 14 14 macedonia 1962 7 7 defect 10116 58 56 adult 1963 113 113 turkic 10117 11 11 accident 1964 82 82 nerve_impulse 10118 5 4 germany 10119 136 134 jewish 1965 62 58 west_africa 1966 24 24 cockney 1967 7 5 glance 1968 47 44 antibodies 10120 29 26 gambling_game 10121 9 9 circulatory_system 10122 7 7 supper 10123 12 12 benefice 10124 6 6 specialist 1969 55 54 bunting 1970 9 9 wrestle 10125 10 10 corymb 1971 6 6 proton 10126 24 21 complain 1972 23 23 fallopian 10127 9 9 rounded 10128 130 127 in_working_order 10129 5 5 imaginative 10130 14 14 publish 1973 10 10 tunnel 1975 18 18 side 10131 723 672 ascetic 1974 8 7 extraterrestrial 10132 7 7 persuade 1976 48 47 reciprocal 10133 31 27 ax 1978 20 19 melting_point 1977 7 7 positive_charge 10134 6 5 delirium 10135 5 5 cloud 10136 104 97 combust 1979 6 5 reckless 1980 15 14 membership 10137 29 29 ridged 1981 14 14 modern 1982 145 140 draperies 10138 5 4 south_carolina 1983 20 19 kennedy 10139 9 8 angina_pectoris 1984 9 9 amusement 1985 14 14 safety 10140 42 42 translucent 10141 38 38 physiological 1986 35 35 forage 10142 60 58 pacify 1987 6 6 poster 1988 11 11 fraud 1989 16 15 retain 10143 46 44 doctrine 10144 136 136 scaled 10145 7 7 seventy 10146 12 12 federal_department 1990 15 15 eve 10147 23 23 scoop 1991 13 13 grouper 10148 5 5 harmless 10149 31 30 nationalist 10150 6 6 favored 1992 26 26 imagery 1993 11 10 devi 1994 7 7 enlisted_man 1995 6 6 tannin 10151 10 10 educator 1996 24 24 instance 1997 62 62 treasure 10152 21 21 simply 1998 31 31 commit 10153 47 45 tube 1999 171 163 assign 10154 49 49 irradiate 10155 5 5 guilt 10156 39 38 searobin 2000 5 4 reconstruction 10157 10 10 objection 2001 29 29 pinnate 10158 86 86 edition 2002 16 15 conjunction 10159 22 22 rigidity 10160 8 8 existing 10161 111 108 incisive 2003 6 5 pretentious 10162 15 13 arab 2005 24 24 comprehensible 2004 13 13 set_out 10163 14 14 confinement 2006 18 18 vest 10164 7 7 resourceful 2007 6 4 glasses 10165 24 24 precursor 2008 22 22 mouse 10167 72 69 guardian 10166 14 14 sister 2009 64 64 lilac 10168 40 38 branchlet 10169 11 11 broaden 2010 5 5 footwear 10170 9 9 relativity_theory 10171 6 5 microscopic 10172 26 26 hand_tool 10173 31 31 explosive 2011 77 72 waxing 10174 5 5 stomach 2012 123 118 boldness 10175 12 12 brought_about 2013 10 10 cream 10176 87 87 genius 10177 16 16 hang 10178 56 54 vasodilator 2014 7 7 base_runner 2015 6 6 cosmetics 10179 15 15 lactose 10180 5 5 thing 2016 186 178 jargon 10181 6 6 reproduction 10182 44 42 birth_canal 2017 5 5 homeland 2018 5 5 butter 2019 78 77 leavened 2020 14 12 horseshoes 10183 5 5 arterial_blood 10184 11 11 sheathing 10185 5 5 sunny 2021 10 9 infringe 2022 5 5 folk 2023 29 29 flush 10186 24 23 sporangium 2024 23 23 incomparable 10187 5 2 mucous 2025 29 28 circular 2026 100 98 jacket 2027 77 76 football 10189 100 98 lac 10188 9 8 standard_time 10190 11 11 lap 10191 20 20 ridicule 2029 16 15 purgative 2028 10 10 kilometer 10192 12 12 contractor 2030 7 7 daniel 2031 6 6 dainty 10193 5 5 cask 10194 16 15 severe 10195 171 166 worshipped 10196 30 30 work_at 2032 5 5 nucleic_acid 10197 13 13 disintegration 10198 14 14 gleba 2033 7 7 itching 10199 16 16 fountain 2034 7 7 counter 10200 35 34 times 2036 211 208 great_britain 2035 61 61 sergeant 10201 10 10 soul 10202 49 48 mormons 2037 5 5 instrument_of_execution 10203 6 6 decisive 10204 25 22 cypress 10205 13 13 checked 10206 17 17 point_of_view 2038 21 21 inconstant 10207 5 3 wheeled_vehicle 2039 15 15 privilege 10208 38 38 deaf 2040 13 12 flower_head 10209 10 10 delphi 10210 5 4 atmospheric_pressure 2041 13 12 decay 2042 83 80 saw 10211 102 100 ancient_greek 10212 43 43 consumed 10213 14 14 painless 10214 8 8 seriously 10215 18 18 analyze 10216 26 22 reddish 2043 92 91 totally 10218 31 31 bureaucratic 10217 5 4 martin_luther 10219 5 5 calm 10220 43 42 sole 10221 78 76 debase 10222 5 5 coal 10223 82 77 chieftain 2044 5 5 infantile 10224 6 6 rigid 2045 72 70 guitar 2046 25 25 rust_fungus 2047 5 5 sluggish 10225 18 18 threshold 2048 5 5 trust 2049 79 74 ctenophore 2050 13 13 cylinder 10226 68 67 contribute 2051 45 44 bay 2052 38 37 fauna 10227 9 9 terrifying 2053 7 7 condemned 2055 39 39 silly 2054 22 22 hairdresser 10228 6 6 purplish_blue 10229 7 7 apostle 2056 16 16 qualification 2057 31 30 coerce 2058 8 8 atypical 10230 6 5 shapeless 2059 5 5 arc 10231 26 26 virgin 2060 9 9 crop 2062 92 87 mentally 2061 53 53 dissolute 10232 5 5 arboreal 2063 57 57 tenderness 2064 5 5 shin 2065 6 6 elasticity 2067 18 18 dyne 2066 8 6 favorite 10233 27 27 interesting 10234 64 64 fiercely 10235 6 6 intelligence_test 2068 5 5 industrial 2070 125 122 gull 2069 16 15 biochemistry 10236 10 10 smith 10237 21 21 surrender 2071 29 29 swarm 2072 10 10 farther 10238 24 17 crescent 10240 13 13 argentine 10239 5 5 ashore 10241 11 11 in_large_quantities 2073 8 8 conducting 10242 25 25 breeches 10243 7 7 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finish 2099 73 67 eligible 10270 11 9 parade 2100 19 18 diatonic_scale 2101 9 9 airforce 2102 7 7 dutch 10271 45 44 seacoast 2103 12 12 intimacy 2104 9 9 mundane 2105 5 5 mucilaginous 10272 5 5 national_park 2106 47 47 bandage 10273 25 25 hatch 2107 10 10 dedicate 2108 6 6 restriction 2110 38 35 handcart 2109 6 6 serious 2111 133 125 midrib 10274 6 6 drunk 10275 31 30 curiously 2112 6 6 latticework 10276 5 5 rename 2113 8 8 better 2114 145 131 precious 10277 37 37 episode 2115 34 34 canonical 2116 9 9 proponent 10278 6 6 obstacle 10279 34 33 fracture 2117 22 22 cerebral 2118 18 17 collide 10280 14 13 cutter 10281 7 7 protuberance 10282 19 19 foster 2119 13 12 insurance_company 10283 7 7 regulate 2121 46 45 breeding 2120 30 29 accusation 2122 34 33 dismiss 10284 8 8 proficient 10285 9 9 mediterranean 10286 178 178 marshal 2123 9 9 plankton 2124 14 14 exploitation 2125 6 6 abolitionist 10287 9 9 nodule 10288 19 17 allusion 10289 7 7 grounds 10290 33 33 close_together 10292 20 19 tramp 10291 6 6 implant 2126 10 10 tightly 2127 55 55 climbing 2128 107 107 blood_plasma 10293 11 11 wavelength 2129 33 31 injudicious 10294 5 4 approximately 2131 56 56 kneeling 2130 5 5 flash 2132 30 29 fold 10295 78 78 binary 2133 11 10 champion 10296 21 21 balder 10297 5 5 gloomy 10298 24 21 evening 10299 72 70 relieve 10300 57 57 affiliation 10301 13 13 vegetable 2134 140 140 shortest 10302 11 11 fungous 10303 10 10 bounds 10304 33 32 rent 2135 63 57 holding 2136 214 214 drain 10305 79 79 lowest 10306 81 79 disgust 2137 8 8 unified 2139 14 14 uncommon 2138 11 5 egg_laying 10307 5 5 shortcoming 2140 9 9 to_the_contrary 2141 5 5 exaggeration 10308 5 5 happy 2142 60 58 scientific_instrument 10309 10 10 recognize 2143 46 44 green 10310 457 442 wyoming 2144 32 32 brown_sugar 10311 8 8 cut_of_beef 10312 13 13 replacing 2145 19 19 in_addition 10313 29 29 yuman 10314 12 12 trowel 10315 6 6 combustible 10316 6 6 paralyzed 2147 12 10 timbre 2146 5 5 pulse 2148 30 27 partner 10318 51 49 brim 10317 24 23 vast 2149 40 38 lengthwise 10319 14 14 outfit 2150 10 10 spat 10320 5 5 bearing 10321 390 388 luggage 10322 20 19 fungus 2152 174 174 reflection 2151 36 36 burden 2153 25 23 riot 10323 17 16 descriptive 2154 13 12 real 10324 125 110 country 10325 426 411 obvious 2155 36 36 yellow_green 10326 31 31 symbolizing 2156 6 6 honeybee 10327 10 10 conceited 10328 14 13 sports_implement 2157 5 5 loin 2158 11 11 streak 10329 13 12 agent 2159 162 161 counting 10330 75 74 david 2160 14 14 1960s 2161 23 23 unprincipled 10331 5 5 deviation 2162 18 18 flared 2163 12 12 dragon 10332 18 16 outline 2164 45 43 seine 2165 8 7 expression 10333 253 244 grudging 2166 5 4 inheritable 2167 6 5 order_coraciiformes 2168 5 5 from_the_heart 10334 5 5 phoenix 2169 8 6 port 10335 210 204 chore 2170 13 13 sincerity 2171 10 9 singing_voice 10336 11 11 allocate 2172 13 12 polite 2173 28 28 handicap 2174 7 7 ten_commandments 10337 7 7 learning 2175 56 56 colloidal 10338 11 11 formulate 2176 11 11 attendance 2177 10 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14 nasty 10362 23 18 would 10363 240 233 wrongly 2205 8 8 holland 2207 10 10 adapted 10364 60 59 textile_machine 2206 5 5 liner 10365 11 10 lamb 2208 37 36 roundish 2209 9 9 dishware 2210 5 5 mash 2211 25 24 indicative_of 2212 15 15 waste_matter 10366 9 9 rectifier 10367 6 6 barge 10368 11 10 hold_in 2213 12 12 brittle 10369 43 42 get 2214 457 439 respected 10370 9 9 jay 2215 10 10 huge 10372 55 52 attest 10371 6 6 volatile 2216 29 26 valence 2218 18 16 global 2217 6 5 adopted 10374 54 54 stimulating 10373 26 26 deliberately 10376 66 65 consistency 10375 36 36 exorbitant 2219 7 7 turning_point 10377 5 5 navigation 2220 30 30 steadfast 10378 13 13 monday 2221 20 20 geological 10379 18 18 attractively 2222 13 13 boy 10380 149 144 submission 2223 18 16 kenya 2224 18 18 falcon 10382 17 16 plucked 10381 9 9 predacious 10383 10 10 get_in 2225 6 6 bargaining 10384 5 5 mate 2226 10 10 digit 2227 44 37 prose 10385 33 33 kidney 10387 84 82 put_out 10386 16 14 crevice 2228 7 7 constituent 2229 92 91 spray 10389 37 36 hypertension 10388 32 31 immerse 10390 21 21 afford 2231 54 53 preserving 2230 13 13 discernment 10391 9 9 bottom 10392 146 141 anal 10393 11 10 expand 2232 35 35 anticonvulsant 10394 7 7 approach 10395 96 95 batteries 2233 7 7 efficacy 2234 7 7 revealing 10396 15 15 reasonableness 2235 6 6 atlantic_ocean 10398 32 32 defending 10397 15 15 tiger 10399 26 24 elsewhere 2236 37 36 bonito 10400 5 5 scotch 2237 6 5 lacrimal 2238 6 5 cpu 2239 5 5 threat 2240 41 41 restaurant 2241 88 88 smear 10401 10 9 prevention 2242 17 17 aquatic_bird 2243 6 6 plantation 10402 6 6 mat 2244 53 53 district 10403 61 60 chess 2246 46 45 shortening 2245 15 15 sexual 2247 149 142 hydrolyze 10404 5 5 parched 10405 7 6 infinite 10406 15 14 incorporate 10407 24 24 subclavian_vein 10408 5 5 breathless 2248 5 3 tapioca 10409 5 5 decoy 2249 7 7 quotient 10410 7 7 smell 10411 61 58 inn 2250 13 13 frugally 10412 5 5 blistered 10413 5 4 petition 2251 17 17 van 10414 12 12 coleridge 2252 5 5 want 10415 104 96 chemically 2253 18 18 marijuana 2254 15 15 valid 2255 35 32 gripping 2256 6 6 likelihood 2257 9 9 veloute 2258 6 6 yolk 10416 13 13 dynamic 10417 9 7 go_to_bed 2259 6 6 appear 10418 188 179 rectangular 2260 56 56 axillary 10419 26 26 derive 2261 24 24 usual 2262 76 73 gas 10421 302 286 salvation 10420 20 19 area 2263 763 744 snug 2265 8 5 squid 2264 7 7 hope 2266 129 125 machine_readable 2267 5 5 ulcer 2269 21 20 imply 2268 21 21 bath 2270 25 24 tactile 2271 8 8 maxim 10422 9 9 cook 2273 61 59 face_mask 2272 5 5 velvety 10423 22 22 ingenious 2274 9 7 impede 2275 11 11 exist 10424 103 100 diverge 10425 11 10 preliminary 10426 27 27 supreme_court 10427 18 18 today 10428 57 54 insulation 2276 7 7 sport 2277 214 211 sorcery 2278 9 9 chile 2279 36 34 charming 2280 13 11 conditioning 2281 16 16 directing 10429 8 8 natural 10430 253 241 handled 10431 41 41 drilling_bit 10432 5 5 slapstick 10433 8 8 stretching 2282 19 19 dane 2283 7 7 released 2284 53 53 unprepared 10434 10 8 poised 2285 6 5 affectation 10435 7 7 biochemist 10436 20 20 obnoxious 2286 6 6 sinking 10437 12 11 grey 10438 9 9 flaunt 2287 5 5 insult 2288 30 30 opportunities 10439 16 16 publicly 2290 16 15 cult 2289 9 9 selective 10440 14 14 religious_order 10441 16 16 crystal 10442 94 89 resume 10443 17 15 dawn 10444 30 30 prickly 2291 52 52 cm 2292 12 12 culm 10445 6 6 slat 2294 9 8 primordial 2293 5 4 exhaling 10446 5 5 hungry 2296 11 11 incompetence 2295 8 8 broadcasting 10447 12 12 spoiling 2297 6 5 morbid 10448 40 38 one_at_a_time 10449 6 6 cottony 10450 6 6 handshake 10451 7 7 absorb 2298 40 40 narrative 10452 24 23 undergo 10453 132 129 entire 2300 113 111 displace 2299 16 16 wick 10454 7 7 lane 10455 27 23 represented 2301 74 74 securities 10456 54 51 differentiation 10457 12 12 furniture 2303 101 100 echinoderm 2302 13 13 burdened 2304 11 11 overlying 10458 5 5 daub 10459 5 5 featured 2305 9 8 sea 2306 332 316 deadly 2307 24 22 participation 10460 20 20 sharing 2308 34 34 private_property 2309 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109 106 tincture 10565 5 5 angiosperm 10566 10 10 mauve 10568 16 16 privately 10567 11 9 braised 10569 9 9 letters 10570 137 129 cabinetwork 10571 23 23 contrast 10572 48 47 windowpane 2406 6 6 tax 2407 92 86 dice 10573 22 22 illinois 10574 23 23 button 2408 51 45 grandeur 10575 5 5 one_hundred 10576 11 11 gathering 10577 38 38 in_accord 2409 21 21 generosity 10578 22 22 droplet 2410 6 6 distal 10579 8 8 clinical_depression 2411 6 6 convoy 10580 5 5 dusty 2412 12 11 undergraduate 10581 11 11 layered 2413 5 5 drupe 2414 11 11 singly 10583 13 13 chemical_bond 10582 7 7 perceptible 10584 23 22 zone 10585 32 32 partridge 10586 8 8 notice 2415 52 50 compression 10587 15 15 masculine 10588 10 9 unintelligible 10589 10 10 impose 10590 20 19 common_sense 2416 14 14 furnished_with 10591 19 19 cartridge 2417 21 21 incompletely 10592 8 7 dropping 2418 13 12 soon 2419 54 54 buyer 2420 18 16 risky 10593 5 5 paying 10594 45 45 naturally_occurring 10595 6 6 oppressive 2421 15 12 permit 2422 58 57 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joseph_smith 2479 5 4 illusory 2481 13 13 suffice 2480 5 5 untempered 2482 5 4 exam 2483 39 39 midwestern 10687 14 13 needlessly 10688 6 6 innocent 10689 24 23 skiing 10690 14 14 bullfight 2484 5 5 ban 2485 11 11 seedpod 2486 5 5 prospective 2487 15 12 communication 2488 101 95 automotive 10691 7 7 colonized 10692 11 11 technically 10693 14 14 navigable 10694 9 8 latitude 10695 14 14 punished 10696 13 13 garlic 2489 27 27 boisterous 10697 14 14 start 2490 188 182 spattered 2491 5 4 variable 10698 112 99 bram_stoker 2492 6 6 young_bird 2493 6 6 lagging 2494 6 6 patriotic 10699 6 6 victorious 10700 7 7 electrolyte 2495 17 17 occurrence 2496 80 79 japan 10701 149 143 spacious 2497 6 5 crouch 10702 5 5 profile 2498 10 10 electric 2499 74 71 actually 10703 37 30 berry 10704 155 152 wrench 2500 25 23 stores 10705 27 27 arteries 10706 68 68 nod 10707 15 15 leprosy 2501 14 14 packing 10708 14 14 timed 10709 6 6 triglyceride 2502 10 9 punctuation_mark 2503 12 12 busted 10711 9 5 interstate 10710 6 4 describe 2504 71 70 wildflower 2505 17 17 distract 2506 5 5 itch 2507 8 8 payable 2508 27 27 daughter 2509 77 77 chains 2510 32 32 cloudy 10712 10 10 punishment 2511 96 95 stricken 10713 6 6 chromosomal 10714 10 10 flask 10715 16 16 build_up 2512 6 6 compass_point 10716 28 28 graphite 10717 8 8 antique 2513 20 18 corrugated 10718 5 5 probability 2514 21 21 fluoride 2515 6 6 electric_charge 2516 24 24 leafless 2517 38 38 sober 2518 11 10 voracious 10719 11 11 pisces 10720 6 6 freshness 10721 11 10 semester 2519 6 6 technological 10722 10 10 polarize 2520 15 15 imprint 2521 9 9 haddock 2522 7 7 warehouse 2524 9 9 discoveries 2523 8 8 enforced 10723 15 14 acquired 2525 63 62 organ_stop 10724 5 5 exodus 2526 7 7 soot 2527 10 9 irony 2528 12 12 gastropod 2529 28 28 headlong 10725 17 17 find_out 10726 9 8 freely 10727 79 77 pallid 2530 9 9 peanut 2531 12 12 bedspread 10728 7 7 trend 2532 24 24 tilted 10729 6 6 substrate 10730 7 7 sheath 2533 26 26 respiratory 2534 36 36 disturbed 10731 19 18 ten 2535 112 107 eager 10732 25 21 pygmy 2536 6 6 heating_system 10733 6 6 propagation 2537 11 10 suffrage 10734 7 7 prosperity 10735 20 20 trademarked 2538 5 5 outlying 10737 7 7 sulfide 2539 16 15 identifiable 10736 6 6 residential_area 2540 6 6 businessman 10738 17 17 tee 10739 11 10 railroad_track 10740 12 12 mathematics 10741 103 101 sweet 10742 219 213 gruff 2541 7 5 rna 2542 33 29 fence 10743 46 45 accelerator 2544 7 7 envious 2543 6 6 freshwater_fish 2545 17 17 authorities 10744 25 25 monster 10745 25 25 submerged 10746 31 29 fir 10747 16 16 elementary_particle 10748 14 14 palatable 10749 5 4 offering 10750 53 53 roof 10751 129 121 look 2546 253 243 passenger 2547 83 83 cloth_covering 10752 14 14 biography 10753 16 16 migration 10754 14 12 stop 10755 137 134 waist 2548 41 40 live_in 10756 61 60 immunology 2549 10 10 weaverbird 2550 8 8 retire 2551 14 14 bully 2552 6 6 chronic 10757 60 56 stamp 2553 51 48 storm 10758 94 93 marine_corps 10759 5 5 completing 2554 7 7 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cleared 2607 19 16 open_to 10809 42 41 lungfish 10810 5 5 psychoanalytic 10811 7 7 axis 10812 105 103 in_check 10813 6 6 hysterical 10815 6 4 antagonism 10814 5 5 atom 10816 184 165 garnet 10817 8 8 john 2608 50 49 x_ray 2609 23 21 registered 10818 19 18 featherlike 2610 9 9 capillary 10819 8 8 seventeen 2611 5 5 circumstance 10820 15 15 detach 2612 7 7 mountain 2613 271 270 trusting 10821 6 6 inanimate 10822 13 12 diverted 10823 6 6 projector 2614 7 7 thyroid 2615 17 15 conception 2617 18 17 inductive 2616 5 5 military_force 2619 15 15 adorn 2618 9 9 fly 10824 93 90 communist 10825 46 44 neurologist 10827 6 6 plaintive 10826 5 5 starch 10828 47 45 forcibly 10829 17 17 alkali 2620 16 16 casually 2621 18 18 brand_name 2622 5 5 narrowly 10830 8 8 scenery 2623 26 26 hull 2625 29 29 percentage 2624 26 26 ultraviolet_radiation 2626 10 9 analysis 2627 73 73 cornea 10831 38 36 the_virgin 2628 19 19 meaningless 10832 15 13 ink 10833 56 51 cargo 10834 32 32 analogy 2629 7 6 harness 2630 16 16 accent 10836 27 26 disengage 10835 6 5 political_orientation 10837 13 13 wig 10838 9 9 pinworm 10839 5 5 paper 10840 245 239 smash 2631 7 7 stadium 2632 11 11 stuck 2633 21 21 lecture 10841 39 38 uniform 2634 61 61 identity 2635 39 37 charcoal 2636 19 19 calculating 10842 11 11 update 10843 7 7 authoritatively 2637 7 7 bid 2638 42 34 devise 2639 22 22 fbi 10844 5 5 lately 2640 5 5 oral 2641 64 60 cooperation 2643 22 21 insoluble 2642 22 22 solitude 10845 8 8 duty 10846 98 93 displacement 10847 21 20 consolidation 10848 6 4 dictate 10849 6 6 random_variable 10850 5 5 aspiration 2644 9 9 frightened 10851 17 17 rainy 10852 7 6 concealing 2645 17 16 intended 2646 284 280 red_blood_cell 2647 10 10 venus 10853 10 10 dentist 2648 27 27 hair_style 2649 9 9 attacking 2650 26 25 tulip 2651 11 11 industry 2652 96 94 arabic 10854 9 9 hoofed 10855 13 13 roll_up 2653 5 5 shoddy 10856 5 5 malleable 2654 13 13 webbing 10857 5 5 hanging 2655 70 69 departed 10858 5 5 reticulate 2656 6 6 closed_circuit 10859 7 6 agenda 10860 5 5 utilization 10861 5 5 ideological 2657 10 8 middle 2658 102 100 greek_alphabet 2659 27 27 ciliary_body 2660 5 5 kitchen_utensil 10862 9 9 younger 10863 24 24 springing 10864 7 7 sophisticated 2661 24 21 fabric 2662 278 271 eat 10866 76 72 ragged 10865 20 18 function 10867 348 333 pasta 2663 31 31 scorched 2664 7 7 tuition 2665 7 7 defeated 10868 135 131 cell_division 10869 10 10 lipped 2666 10 9 plunge 10870 21 21 pony 2667 6 6 cat 10871 99 97 soak 2668 7 7 unintentionally 10872 6 6 flex 10873 16 15 concert 2669 36 35 deceive 10874 46 46 impression 10875 67 66 lush 10876 5 5 cymose 10877 11 11 untrue 2670 12 12 united_states_army 10878 16 16 halfway 2671 10 10 financial 10879 103 100 wake 10880 18 18 nominal 2672 9 7 revolt 2673 18 18 beaufort_scale 2674 14 14 instructions 10881 50 49 excrement 2675 6 6 sweetener 2676 8 7 slam 10883 9 9 reassuring 10882 5 5 practicing 10884 32 31 mosque 2677 5 5 forward_pass 10885 6 6 inversely 2678 10 10 rest_on 2679 5 5 location 2680 138 135 everlasting 10886 9 9 sandstone 2681 13 13 tomorrow 2682 20 19 hiding 10887 10 7 working_day 2683 6 6 label 2684 16 14 craving 2685 6 6 busy 2686 36 27 methodical 10888 11 11 underfur 2687 5 5 iodine 10891 35 35 disliked 2688 10 10 livingroom 10889 6 6 stilt 10890 8 7 contented 2689 9 9 rejoice 2690 7 6 begin 2691 155 134 token 10892 15 14 mechanic 2692 6 6 derogatory 10893 11 11 infectious 2693 20 20 profit 2694 69 66 slow_down 10894 7 7 buttress 2695 11 10 prosody 2696 8 8 psalm 10895 8 8 pun 10897 6 6 frightening 10896 5 5 keen 2697 30 30 dough 2698 55 54 elastic 2699 40 39 trailer 10898 14 14 louisiana 2701 22 21 dissipated 2700 6 6 yew 2702 15 14 too_much 2703 56 56 political 10899 225 219 dig 2704 14 14 positive 2705 89 83 fallopian_tube 2706 5 5 illustrated 2707 6 6 rings 10900 29 29 satellite 2708 36 36 ordered 10901 43 42 hotel_room 2709 6 6 fullback 2710 5 5 lexicographer 10902 13 13 ill_will 2712 11 11 antiparticle 2711 11 11 pope 10903 58 54 bulbil 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genesis 10997 8 8 genus_chrysanthemum 2819 14 14 inexperienced 2820 12 12 insurance 10998 43 42 silica 10999 17 17 forefinger 2821 5 5 lanceolate 11000 22 22 counteract 2822 27 26 peacefully 2823 9 9 caught_up 11001 5 4 world 11002 374 360 flux 11003 13 12 skirt 11004 62 61 fig 11005 11 11 breach 11006 12 12 bring_to 2824 26 26 cherished 2825 5 5 road 2826 159 152 plato 11007 10 8 meadow 11008 46 46 humane 2827 10 10 three_times 2828 12 12 addiction 11009 15 15 artful 11010 9 8 journalist 11011 29 29 rook 2830 10 9 evil_spirit 2829 6 6 pint 2831 6 6 supplement 11012 21 19 nonpasserine 2832 5 5 gaze 2833 25 24 hint 2834 9 8 smooth 2836 230 223 irreparable 2835 6 3 precision 11013 16 15 equilibrium 2838 37 36 baggage 2837 7 7 magnetic_north 2839 5 4 easter 2840 27 27 molding 2841 25 25 overcoat 2842 6 6 possessed 2843 50 49 leek 2844 9 8 preset 2845 5 5 accuse 11014 5 5 hourly 11015 5 4 cant 2846 61 60 persuasive 11016 15 14 derived 11017 224 219 continental 11018 8 6 yam 2847 7 7 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friendliness 3017 12 12 acrobatic 3016 6 6 shovel 3018 13 11 clarity 3019 23 23 swampy 3020 12 12 seedling 3021 11 11 consequence 3022 143 141 shooting 11200 31 31 identifying 11201 24 23 filler 11202 8 8 picidae 3023 7 7 tidal 11203 5 5 filling 11204 43 43 noun 11205 86 77 shameful 11206 5 5 paraguay 3024 16 16 no_longer 3025 97 97 unaccustomed 3026 7 5 bacillus 3027 7 6 junior 3028 9 8 bidder 11207 5 5 solidified 11208 7 7 seats 11209 55 52 offending 11210 9 8 drawer 11211 8 8 queen 11212 66 64 lodge 3029 14 14 enclose 3030 40 40 sinus 11213 20 20 commute 11214 7 7 chowder 3031 8 8 israel 11215 53 47 korea 11216 23 23 appeal 3032 56 54 clover 3033 16 16 northern_hemisphere 11217 97 97 cut_through 3034 8 8 infective 11218 8 7 chemical_analysis 3035 7 7 aim 3036 71 68 zodiacal 11219 14 14 effigy 11220 5 5 trinidad 3037 5 5 bill 3038 156 154 armyworm 3039 5 5 winglike 3040 8 8 sung 3041 24 24 natural_object 11221 5 5 clinton 3042 5 5 threatened 3043 22 22 altering 3044 12 12 periodic 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5 4 cervix 3068 8 7 rail 3069 21 19 actress 3070 58 58 injurious 11248 18 18 softly 11249 21 18 curiosity 11250 29 29 optical_device 11251 18 18 beheaded 11252 6 6 efficiency 3071 15 15 steaming 11253 7 6 taste 3072 169 153 survivor 3073 13 13 informer 11254 6 6 set_in 3074 26 26 portuguese 11255 16 16 root 11256 125 122 bruise 11257 14 14 silvery 11259 85 84 relaxant 11258 5 5 appreciative 3075 6 4 carbon_atom 11260 5 4 burns 11261 46 46 greasy 11262 11 11 decimal 11264 11 11 draft_horse 11263 8 8 boll 11265 5 5 piano 3076 54 54 search 3077 105 98 applaud 11266 14 14 aerial 11267 30 26 exploit 3078 19 19 calibrated 3079 7 7 perspective 3080 13 13 excavate 3081 15 14 trying 11268 56 56 guarded 11269 13 13 gain 3082 165 160 romania 11270 10 9 unjustly 3083 8 8 anterior_pituitary 11272 9 8 normalize 11271 5 3 rightful 11273 11 11 major 3084 234 224 millipede 11274 6 5 loss_of_consciousness 3085 10 10 wholly 3086 27 26 forget 11275 32 31 absent 3087 28 27 expedition 3088 28 27 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measuring 3138 113 109 artwork 3137 5 5 baptist 3139 7 7 occur 11327 640 623 crinkled 11328 5 5 destiny 3140 15 15 roller 3141 26 26 hyaline 11329 6 5 papilla 3142 6 6 destination 3143 23 23 cello 3144 9 9 police 11330 111 111 marked 3145 461 458 jagged 11331 13 13 demonstrable 3146 5 5 marsh_plant 3147 7 7 systematically 3148 9 9 correlation_coefficient 11332 7 7 airliner 3149 5 5 lowland 3150 11 11 rejected 11333 26 26 reverse 3152 33 33 paniculate 3151 5 5 impassive 11335 6 5 coherence 11334 5 5 assistant 3153 35 35 on_time 11336 9 9 corrective 11337 6 5 celebrity 11338 8 8 price 11339 291 269 interest_rate 3154 6 5 soviet_union 3155 15 15 in_place 3156 55 55 radioactive_decay 11340 5 5 succeeding 11341 6 6 collapse 11343 33 32 appetizer 11342 5 5 plane 3158 291 272 insurance_policy 3157 8 8 ex 3159 16 16 chili 11344 7 7 fringed_gentian 3160 5 5 calculated 3161 31 31 tack 11345 11 9 reptile 11346 70 70 phylum 3162 17 17 selected 11347 30 30 gifted 3163 12 12 river 3164 366 352 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11426 7 7 margin 11427 78 77 difficult 3238 161 157 distinguishing 11428 28 27 policeman 11429 29 28 melody 11430 31 31 halloween 3239 5 5 nutmeg 3240 11 11 statistics 3241 34 33 widely_distributed 11431 150 150 program 3242 213 201 manufactured 3243 20 20 hazardous 3244 8 8 calculus 11432 18 17 arranging 3245 14 14 graduate_school 11434 10 10 filing 11433 5 5 fascism 3246 7 7 go_away 11435 12 12 concord 3247 7 6 tears 3248 58 55 pineapple 11436 11 11 spectacular 11437 23 22 hotness 11438 7 7 baptism 11439 20 20 dispersion 3249 7 7 sluice 3250 9 7 slab 3251 10 10 delaware_river 11440 8 8 exotic 11441 8 7 motley 11442 5 5 enclosing 11443 42 42 hilly 3252 10 10 swiftly 11444 10 10 forwarding 11445 5 4 tailless 3253 12 12 uric_acid 3254 9 9 loire 3255 7 7 tolerable 11446 6 5 selfishness 3256 5 5 speed_limit 11447 5 5 side_effect 3257 5 5 hallucinogenic_drug 11448 5 5 red 3258 722 689 infrequent 3259 5 5 eared 11449 11 10 exchangeable 3260 5 5 poland 11450 45 44 bee 11451 75 69 pay_for 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psychologically 11471 17 17 probable 11472 20 19 upwards 3292 8 7 innervate 11473 7 7 shaped 11474 781 764 isomer 11475 11 10 equally 11476 29 28 recreational 11477 11 10 shroud 11478 10 8 tastelessly 3293 6 6 darkness 11479 37 35 imprecise 3294 5 4 recently 3295 35 35 laying 3296 16 16 postal 3297 7 7 military_uniform 11480 11 11 moses 11481 14 14 revised 3298 8 8 musical_accompaniment 11482 5 5 systemic 3299 6 6 lady 11483 26 26 unchanging 3300 8 8 northeastern_united_states 3301 21 21 seabird 3302 12 12 faint 3303 52 44 divisor 11484 5 5 trait 11485 147 146 larch 3304 7 7 crete 3305 11 11 skillfully 11486 10 10 discriminate 3306 7 6 day_of_the_week 11487 8 8 coastline 3307 8 8 equidistant 11488 5 5 rising 11489 57 54 distributed 11490 46 45 plural 11491 75 73 educational 3308 23 23 baseball_team 3309 15 15 waters 3310 195 192 appendix 3311 6 6 clasp 3312 11 10 unnecessary 11492 13 13 order_gentianales 3313 5 5 assemble 3314 15 15 ferric_oxide 3315 6 6 furthest 3317 10 7 stencil 3316 5 5 disprove 3318 12 12 puritan 3319 7 7 open 3320 424 397 duct 11493 48 45 portugal 3321 30 28 dissimilar 3322 20 17 the_calculus 11494 5 5 depicted 3323 10 10 argument 11495 156 149 spadix 3324 11 11 tentacle 11496 30 29 amazed 11497 5 5 litmus 3325 5 5 ethereal 3326 6 6 endangered 11498 11 10 recompense 11499 6 6 assets 3327 49 45 ask_for 11500 15 15 putt 11501 5 4 splash 3328 6 6 alternatively 11502 5 5 revise 11503 6 6 greek_mythology 3329 177 177 bundle 11504 33 32 extraction 3330 10 10 odoriferous 11505 5 5 plumbing 11506 10 10 fullness 3331 8 8 sprinkling 3332 10 9 trimming 3333 18 18 deities 3334 10 10 plentiful 3335 9 9 pig_iron 3336 9 9 receptacle 11507 33 32 bedridden 3337 5 5 muted 3338 5 5 ferocious 3339 8 8 shipbuilding 11508 6 6 campania 3340 5 5 pack 11509 39 39 boiler 11510 10 9 alexander 3341 6 6 cruelty 11511 13 12 closest 3342 15 15 war 11512 317 307 factories 11513 6 6 monosaccharide 3343 17 17 sedge 11514 22 22 embrace 11515 17 16 bang 3344 16 15 revolutionaries 3345 5 5 swelling 3346 60 59 memorial 11516 14 14 acidity 3347 7 7 lip 3348 126 118 russia 11517 146 142 disguised 3349 5 5 international_affairs 11518 5 5 troops 3350 141 136 swedish 11519 23 20 earthy 11520 10 9 poll 3351 5 5 bonding 3352 10 10 palmlike 3353 8 8 exhalation 11521 5 4 fingertip 11522 5 5 clinic 11523 15 14 blazing 11524 6 6 grass 3354 271 260 myanmar 3355 11 11 collagen 3356 6 6 aggregation 11525 9 9 brakes 3357 12 12 monthly 11526 10 9 convene 3358 6 6 preserves 11527 26 26 predicted 3359 11 11 helping 11528 13 13 mandate 3360 5 5 webbed 3361 15 15 specialized 3362 60 59 colonial 11530 30 30 satisfied 11529 21 21 novelty 11531 5 5 inhibiting 3363 11 11 artificially 3364 37 37 panting 3365 5 5 fins 3366 65 62 olive_oil 11532 7 7 manufacturer 11533 28 28 overt 11534 9 7 husky 11535 5 5 emotional 3367 124 121 phobia 3368 5 5 good_humor 11536 8 8 vacant 11537 6 6 bowl 3371 51 51 segregated 3370 8 7 extraordinarily 11538 11 11 promotional 3369 6 6 christian_theology 11539 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3456 10 10 troy 11605 24 22 stressed 11606 20 18 grassland 3457 20 20 inappropriate 11607 25 23 foretell 3458 5 5 tickle 3459 5 5 cerebellum 3460 10 10 trick 3461 49 49 cooperate 3462 20 19 smoked 3463 24 24 lighter 11608 18 18 debtor 3464 8 8 mauritania 3465 5 4 high_blood_pressure 11609 6 6 large_number 3466 14 14 scatter 3467 12 12 oracle 3468 7 7 milton 11610 6 6 lipoprotein 3469 12 10 pithy 3470 8 8 animal 11612 912 884 sewing 11611 33 32 drive_in 3471 5 5 hover 11613 5 5 observant 3472 6 6 primitive 11614 132 128 emit 3473 78 78 ringing 11615 15 14 password 3474 5 5 dreary 3475 5 5 beggar 3476 16 15 braces 11616 8 8 untouched 11617 6 6 rope 3477 179 166 contemporary 11618 13 13 east 11619 304 300 geologic 3478 9 9 growth_hormone 3479 5 5 improved 11620 27 25 security 3480 92 90 neuroglia 11621 6 6 roar 11622 13 13 groove 3481 53 50 mending 11623 6 6 malayan 11624 7 7 hamburger 3482 8 8 english 3483 582 569 alphabetically 3484 5 5 twofold 11625 7 7 living_together 11626 12 12 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24 24 outspoken 11702 9 9 aquatic 3555 142 137 structural_member 11705 9 9 sliding 11706 27 27 comparable 11704 8 7 depart 11707 19 19 plate 3556 165 160 catholic_church 11708 14 14 feature 11709 154 151 approximate 11710 14 14 burst_out 3557 5 4 kite 3558 12 12 emphasize 3559 21 21 maximal 3560 9 8 embodiment 11711 11 11 prolonged 11712 46 44 camphor 3563 11 11 infirm 3562 5 5 liquefy 3561 5 5 stable 11714 69 64 unhappy 11713 17 13 harpsichord 11715 5 5 equal_to 11716 292 287 welfare 3564 27 27 coarsely 3565 14 14 pugnacious 3566 5 5 par 11717 13 12 recruit 11718 19 19 decomposing 11719 6 6 vulnerability 3567 6 6 discussion 3568 84 83 sotho 11720 6 5 reedlike 3569 8 8 chief_executive 3570 7 7 refractive 11721 6 6 bacteria 3571 179 176 conserved 11722 5 5 anarchist 3572 8 8 depreciation 3573 7 7 burundi 3574 7 5 sheltered 3575 11 11 slovenia 3576 5 5 confidential_information 3577 9 9 punishable 11723 8 8 typeface 11724 8 8 encourage 11725 15 15 dorsal 11726 30 29 celery 3578 27 27 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41 41 responsibilities 3952 40 40 python 3953 6 6 new_york 3954 84 83 daisylike 3955 20 20 threadlike 12148 17 17 insist 12149 14 14 hydrochloric_acid 12150 7 7 territorial 12152 16 14 at_issue 12151 7 7 peel 3956 8 7 fertilized 3957 20 20 resignation 12153 12 12 balloon 3958 30 28 ginger 12154 15 14 spill 3959 25 23 typing 12155 9 8 charged 12156 103 100 fractional_monetary_unit 3961 10 10 evasive 3960 8 7 circumpolar 3962 5 5 rag 12157 11 11 admire 3963 8 8 wing 3964 50 50 legal_system 3965 6 6 suitability 3966 14 14 krishna 12158 9 8 tradition 3967 48 46 ossicle 3968 5 5 inflammatory_disease 3969 6 6 metaphorical 3970 9 9 machine 3971 164 162 musical 12159 135 133 shave 12160 7 7 centuries 3972 68 68 capacity 12161 135 134 leaflet 12162 41 39 counterfeit 3973 14 11 westward 12163 12 12 heart_valve 3974 5 5 minority 3975 19 18 persecution 3976 9 9 dravidian 3977 7 7 shade_tree 3978 14 14 loop 3979 50 47 shades_of 12164 7 7 tributary 3980 54 54 theology 12165 38 36 recede 3981 8 7 realization 3982 10 10 instrumentality 12166 6 6 egg 3983 106 104 wagon 12167 35 35 associative 12168 7 7 sexual_pleasure 3984 7 7 catfish 3985 20 20 throne 3986 45 44 intimately 3987 12 12 athletic_competition 12169 9 9 monkey 3988 48 48 analytical 12170 5 4 therapeutic 3989 23 22 sinister 12171 10 9 contemplation 3990 8 8 found 3992 705 699 twig 3991 37 37 radio_receiver 12172 7 7 plantain 12173 8 8 pistil 3993 10 10 struggling 3994 6 5 diverse 3996 30 29 jerk 3995 12 12 association 3997 90 89 theory_of_evolution 12174 6 6 cascade 3998 8 7 elicit 12175 10 10 onward 3999 9 9 boarding 12176 5 5 whiplike 4000 8 8 instead 4001 86 84 sort 12177 19 19 crowned 4002 23 21 exposed 12178 47 47 staring 4003 10 10 fulfilling 12179 7 7 counterclockwise 12180 5 5 canadian 4004 36 35 solicitous 4005 7 5 former 12181 248 246 extort 4006 6 6 perfumery 12182 10 10 caraway 12183 11 11 bland 4007 8 8 studio 4008 13 11 tzu 12184 8 6 slang 4009 75 74 swallow 12186 43 42 cocoa 12185 8 8 sharper 12187 8 8 new_caledonia 4010 10 10 appearing 12188 44 44 near_east 12189 5 5 palpable 12190 8 6 lending 12191 9 9 crystalline 12192 141 141 indulgent 4011 13 12 shabby 4012 7 7 stiffen 12193 11 11 military_action 12194 9 9 client 4013 35 34 theoretical 4014 22 22 inevitable 12195 12 12 papule 12196 6 6 allergies 12197 8 8 truthful 4015 7 6 torn 12198 28 23 clear 4017 235 211 oxidation 4016 39 39 jewish_calendar 12199 10 10 sociology 12200 12 12 spastic 4018 5 5 audible 12202 18 18 temptation 12201 10 9 spatial_property 12203 11 11 articulate 4019 23 21 loading 4020 18 16 inclination 4021 38 38 jet_plane 12204 6 6 minimize 12205 10 10 feminine 4022 9 9 impossible 12206 100 98 occasions 4023 29 29 elegant 12207 34 30 infestation 4024 25 24 credits 12208 11 11 octopus 4025 5 5 oar 4026 20 20 kettle 12209 9 9 pornographic 12210 5 5 pluck 12211 10 10 patented 4027 6 6 mercurial 12212 6 5 retaliatory 4028 5 5 persian 12213 31 31 ossified 12214 9 9 officer 4029 117 114 president_of_the_united_states 12215 44 44 amendment 12216 37 34 wings 4030 174 168 shrinking 12217 5 5 cave 12218 41 38 debut 12219 6 5 surrounding 4031 110 109 unity 12220 21 21 trumpets 4033 12 12 alleviate 4032 8 8 natural_phenomenon 12221 5 5 nathaniel_hawthorne 4034 7 7 ulceration 12222 7 7 paint 4035 138 136 replaceable 4036 8 8 at_length 4037 6 6 guillotine 4038 7 6 anesthesia 4039 12 12 window 4040 175 166 purpose 4041 377 374 bird_of_prey 12223 8 8 expired 4042 5 4 incantation 4043 6 6 copulation 12224 5 4 prospect 4044 21 21 stroke 12225 80 75 backstage 4045 8 6 accumulation 12226 38 38 assimilate 12227 9 9 water_vapor 4046 9 9 present 12228 237 228 roundworm 4047 17 17 ape 4048 22 22 floor 12229 156 153 essential_amino_acid 12230 5 5 heavy 12231 383 356 intervention 4049 18 18 homely 4050 9 5 airmail 4051 5 4 persuasion 4054 19 19 susceptibility 4053 11 11 short_story 4052 5 5 bosomed 4055 5 4 isolation 4056 18 18 landed 12232 19 18 relaxed 4057 34 33 nectar 4058 11 11 gauze 4059 8 8 mediate 12234 22 22 audit 12233 10 10 mantle 12235 14 13 sweetheart 4060 6 6 fermentation 12236 33 33 voting 4061 23 22 transactions 12237 23 23 legged 4062 43 41 noblewoman 4063 8 8 draw 4064 124 120 intrusive 4065 12 12 classical_mythology 12238 15 15 innovative 12239 13 12 stronghold 4067 11 11 noticeably 4066 7 7 repetitive 4068 7 7 paragraph 4069 13 11 high_point 4070 7 7 commonwealth 4071 15 15 driving 12240 51 49 herbaceous 4072 36 36 leisure 12241 8 7 chase 4073 19 19 ego 4074 6 6 chariot 4075 8 8 momentary 4076 13 13 ripened 12242 5 5 bodily_structure 12243 5 5 reign 12244 39 38 conductor 12245 55 55 lagoon 4078 8 8 adjourn 4077 5 5 tomato 12246 68 66 piece_of_writing 12247 11 11 crime 12249 119 117 astonishment 12248 10 10 tongued 12250 10 7 discontinue 12251 6 6 bourbon 4079 6 6 southern_cross 12252 5 5 mountain_peak 12254 29 29 right_to_vote 12253 8 8 lovely 4080 5 5 phrase 12255 115 112 odysseus 4081 10 8 gladly 12256 5 5 quiet 4082 81 75 oedipus 12257 5 4 room_temperature 12258 6 6 boil 4083 27 25 expound 4084 7 7 task 12260 106 102 intrusion 12259 8 8 insufficiency 4085 5 5 confusion 4086 41 41 dilute 12261 10 9 senses 4087 58 58 ember 4088 5 5 ramp 12262 6 5 open_door 4089 5 5 thyroxine 4090 8 7 antler 12263 18 18 anthropologist 4091 21 21 gently 12264 35 35 jelly 4092 39 39 prepared 4093 138 135 earless 4094 5 5 unemployed 12265 15 15 processor 4095 5 5 modified 12266 53 50 basketball_shot 12267 10 10 seem 12268 146 143 queue 12269 9 9 stage 12270 264 245 periodic_table 4096 5 5 insanity 12271 11 11 irritated 12272 11 11 earnestly 4097 10 10 desk 12274 33 33 gilded 12273 6 6 editor 4098 29 29 extremely 4099 158 156 sarcastic 12275 5 5 sunset 4100 16 16 courteous 4101 12 12 topped 4102 34 34 hold_back 12276 9 9 pastry 4103 35 33 blood_serum 12277 12 12 semitone 12278 11 11 categories 4104 12 12 spending 12279 27 27 billed 12280 27 26 eats 12281 17 16 neural 12282 22 22 taxonomic 4105 22 21 medieval 12283 86 85 worthlessness 4106 5 5 town 12284 416 405 raucous 12285 7 7 solving 12286 16 16 terminated 4107 7 7 cobra 12287 5 5 surreptitiously 4108 7 7 social 4110 266 258 liar 4109 5 5 provincial_capital 4111 12 12 prolific 12288 20 19 cargo_ship 4112 6 6 tread 4113 23 23 attained 4114 17 17 engraved 12289 15 15 wallow 12290 5 4 domination 4115 5 5 normandy 12291 13 13 oleoresin 4116 9 9 eye_socket 12292 7 7 conqueror 12293 7 7 primates 4117 20 20 paid 4118 157 148 victor 4119 6 6 imprisoned 4120 9 9 ruminant 12295 21 20 chess_master 12294 5 5 announcement 12296 22 22 convertible 4121 18 17 insert 12297 102 100 ingestion 12298 14 14 encode 12299 6 6 knead 4122 6 6 millions 4123 12 12 reddened 4124 7 6 moderately 4125 26 26 asset 12300 10 10 shoulder_joint 4126 5 5 kiln 4128 13 13 milky_way 4127 9 9 door 4129 201 189 sleeping 12301 41 40 buried 12302 32 31 courtesy 4130 17 17 involvement 4131 19 17 detergent 4132 30 30 unsaturated 12303 23 23 hew 12304 7 6 mortally 4133 6 6 lacerate 4134 5 5 footstep 12305 8 8 brownish_yellow 12306 6 6 profess 12307 5 5 humans 12308 102 102 revolutionary 12309 48 46 stately 12310 12 12 oppressed 4135 7 7 correction 4136 24 23 telephone_line 4137 10 10 skip 12311 7 7 manifest 12312 46 45 loyal 4138 18 17 metaphoric 4139 7 7 flamboyant 12313 6 6 requirement 4141 57 54 protrusion 12314 12 12 nephew 4140 15 15 ride 12315 115 108 publisher 12317 17 16 overboard 12316 5 5 valve 4142 51 50 spots 12318 67 66 head 4143 844 818 fission 4144 14 14 invading 12319 11 11 emitting 4145 25 24 niger 12320 11 11 toxic_condition 4146 10 10 piston 4147 20 19 cutting_edge 12321 15 15 gazelle 12322 7 6 greed 4148 8 8 latest 4149 29 28 bowsprit 4150 5 5 overwork 4151 5 5 defiance 4152 10 10 residence 4153 49 48 shop 12323 69 67 threaded 4154 15 14 syndrome 12324 28 28 binding 12325 42 42 years 4155 351 344 loss 4156 233 225 brown 12326 328 323 strategically 12327 14 14 smut_fungus 12328 7 7 touching 4157 23 23 concert_hall 12329 6 6 remain 4158 97 92 quarter 4159 40 37 drop 4160 102 98 hypnosis 4161 10 10 oversee 12330 10 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constrained 12358 8 8 respiratory_tract 4184 6 6 gray 4185 256 242 rocky_mountains 12359 28 28 inventor 12360 45 45 declared 12361 41 40 wrongdoing 4186 15 15 get_out 12362 16 16 bellows 4187 6 6 motionless 12363 6 6 synthetic 12364 56 56 unite 4188 37 37 radio_station 12365 5 5 participle 12366 5 5 photograph 4189 105 101 wait 12368 44 43 sheathed 12367 7 4 haste 4190 16 15 longest 12369 24 24 servile 4191 15 13 lucid 12370 9 9 auxiliary 12371 12 12 address 12372 92 90 id 4193 12 11 on_the_spot 4192 9 6 unplanned 4194 7 4 heart_failure 12373 5 5 reminder 4195 5 5 mild 12374 77 72 instructional 12375 5 5 chip 4196 21 19 attentiveness 4197 6 6 fair 4198 53 48 travel_by 4199 13 13 tragic 12376 23 20 project 4200 112 111 ruler 12377 62 60 deprived 12378 5 4 natural_resources 4201 15 15 advise 12379 21 21 opera 4202 75 74 cool 12380 76 61 share 4203 133 124 option 12381 17 16 hopeful 12382 8 7 death 12383 266 259 dandelion 12384 14 14 painstaking 4204 7 7 arthropod_family 12385 5 5 haircut 12386 10 9 headache 12388 31 31 inadequacy 12387 8 8 staircase 4205 14 14 sedate 12389 5 5 birth_control 12390 7 6 dependence 4206 11 10 appropriate 12391 113 111 rate 12393 130 126 putrefaction 4207 5 5 gone 12392 36 36 lament 4208 6 6 disciplined 4209 6 5 granitic 12394 7 7 crown 12395 123 119 organic_process 4210 14 14 density 12396 36 33 guest 4211 87 85 penalty 4212 32 31 splitting 4213 16 15 lavishly 4214 10 10 sink 4215 33 31 outcome 12397 70 70 striking 4216 58 57 reproach 12398 13 12 sweating 4217 12 12 education 12400 82 81 vowel 12399 63 54 puckered 4218 6 5 politic 4219 7 3 powder 4220 78 77 thrush 12401 18 18 tableware 12402 11 11 helios 12403 5 5 enveloped 4221 5 5 stimulation 12404 35 35 procure 12405 6 6 physical_chemistry 12406 6 6 obedience 4222 13 13 way_out 12407 5 5 bike 4223 7 7 dependent_on 4224 22 22 offender 4226 22 22 repayment 4225 9 9 abiding 12408 5 5 wash 12409 51 49 even 12410 178 161 wring 12411 6 5 writing_system 12412 12 12 crease 4227 12 12 breath 4228 37 34 be_active 4229 5 5 belong_to 12413 10 10 reformer 4230 18 18 nutrient 12414 32 32 nepal 12415 21 21 figured 4231 6 6 involve 12416 404 404 advisable 12417 9 7 brown_algae 4232 10 10 mason 12418 18 18 call_attention 12419 6 6 crumb 4233 18 16 housewife 4234 5 5 6th 4235 29 29 elector 4236 8 8 repeating 4237 17 17 rose 4238 132 127 cancerous 12420 9 8 editing 12421 10 10 storeroom 12422 5 5 artless 12423 6 5 archaic 12424 97 97 sahara 4239 6 6 intercourse 4240 6 6 hedge 12425 25 25 margarine 12426 6 6 sting 4241 15 14 leaders 12427 24 23 affectionate 12428 9 9 queens 4242 9 9 panel 12430 33 32 irresponsibly 12429 6 6 already 4243 54 53 differently 4244 15 15 trojan 4245 5 5 fumes 4246 15 13 microwave 12431 16 15 danger 4247 133 130 mescal 12433 6 6 reserves 4248 14 14 memorization 12432 5 4 album 4249 10 10 resonant 12434 10 10 exchange 4250 114 112 facetious 12435 6 6 pitching 12436 8 8 taxonomic_group 12438 18 18 veteran 12437 16 13 gleam 4251 9 9 arms 12439 171 167 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12552 6 6 nourished 4334 8 8 foot 4335 549 527 stored 12554 77 75 freezing_point 12553 9 9 christian 12555 113 109 plan 12556 205 195 ticking 4336 5 5 braid 12557 16 13 hard_palate 4337 7 6 worker 4338 213 210 alcoholic_beverage 12559 10 10 electropositive 12558 6 6 funnel 4339 36 35 parliament 4340 25 25 sailboat 12560 12 12 equation 12561 40 38 ecuador 4341 13 13 scarce 12562 5 5 aerodynamic 4342 5 5 secrete 4343 108 108 husband 12563 148 145 cystic 4344 5 5 executive_agency 12564 5 5 stand_in 12565 6 6 sheet 12567 104 100 immature 12566 31 30 sweetness 4345 5 5 criminal 12568 93 91 right 12569 576 524 construction 12570 97 96 intonation 12571 6 6 unconcerned 12572 10 9 wading 12573 14 14 protecting 4346 25 24 unclean 4347 8 7 administrator 4348 18 18 wrasse 4349 6 6 pot 4350 44 44 tail 4352 229 227 reorganize 4351 5 5 blatant 12574 6 6 burdensome 12575 8 8 fix 4353 43 41 chilly 12576 5 5 gamete 4354 36 32 thursday 4355 8 8 abdomen 12577 45 45 clan 4356 10 10 four 12578 348 327 taxes 12579 67 64 alchemy 4357 8 8 cotton 4358 118 114 verse 4359 64 61 be_given 4360 12 12 pharynx 12580 21 20 hypothetical 4361 33 32 attend 12581 40 39 sports_equipment 12583 9 9 allay 12582 5 5 detectable 4362 7 7 terminology 4363 16 16 weathered 12584 7 7 temperature_scale 4364 6 6 meat 4365 244 239 starfish 4366 8 8 affected 12585 111 111 linearly 4367 5 5 tract 4368 46 46 individual 12586 254 247 apology 4369 10 10 seller 4370 23 22 state_of_matter 12587 5 5 stabbing 12588 6 6 blast 4372 11 11 decayed 4371 7 7 spare_time 12589 5 5 wine 12590 222 214 imperious 4373 5 5 heavyweight 4374 10 9 handkerchief 4375 7 7 imperial 4376 33 33 lemming 4377 6 6 risen 4378 6 6 mastering 12591 5 5 affirm 12592 17 17 military_post 12593 5 5 aircraft 12596 174 172 mark_twain 12594 7 7 emerging 12595 11 11 tie 12597 73 71 database 12598 14 13 mercury 12599 30 26 cranium 12600 17 17 mint 12601 36 35 anus 4379 28 28 devoid_of 12602 25 25 meteorite 12603 12 12 rudder 12604 7 6 partnership 4380 7 7 humid 12605 13 12 blocking 12606 21 21 taken_with 12607 5 5 shoot 12608 61 61 leading 12609 121 120 abused 12610 11 10 ruthless 4381 5 5 virtuous 12611 6 6 indication 12612 33 32 quadrilateral 4382 11 11 swordlike 4383 5 5 temple 4384 33 33 boron 12613 9 9 bloodshed 4385 11 11 blemish 4386 13 13 elliptical 12614 13 13 cliff 4387 45 44 contradict 12615 12 12 boxer 12616 40 36 harsh 12617 67 62 profusion 4388 12 12 interlace 12618 5 5 frustrated 12619 7 7 trapped 4389 12 12 action 12620 609 582 arthritis 4390 19 19 hallucination 4392 11 11 starred 4391 7 7 shine 12621 37 35 brightly 4393 85 85 charles 4394 11 11 pueblo 4395 5 5 causal 4396 6 6 coffee 12622 94 92 atmosphere 12623 108 107 slave 12624 46 42 cgs 4397 5 5 sighted 12625 9 9 bare 12626 35 32 mallow 12627 8 8 lord 4398 40 39 curdled 12628 8 8 uninteresting 12629 9 9 immune_response 4399 17 16 abortion 12630 18 18 considerably 12631 8 7 radish 12632 5 5 surveyor 4400 8 8 widespread 12633 68 67 queen_of_england 12634 9 9 inventory 12635 13 13 unconscious 12636 21 20 throw 4401 155 151 tug 12637 10 8 exact 12638 43 41 cheek 4402 56 56 watching 12639 18 18 paleontologist 12640 7 7 a_thousand 4403 12 11 veneration 4404 7 7 tediously 12641 5 5 divorced 12642 5 5 lacking 12643 541 538 shelter 12644 59 58 wheels 12645 79 78 east_germany 4405 5 5 pilgrim 4406 15 15 parenthesis 12646 8 8 artificial_language 4408 25 25 morning_glory 4407 6 6 philippine 4409 13 13 marketplace 12647 10 10 cold_weather 4410 6 6 stack 4411 11 11 quail 12648 10 10 gorge 12649 7 7 thrown 4412 73 69 betting 12650 7 7 eucharist 4413 13 13 universities 4414 16 16 equilateral 12651 5 5 surpassing 4415 10 10 aluminum 4417 58 53 stuffy 4416 5 5 sharpen 4418 11 11 joyful 4419 16 14 chromatography 12652 6 6 cardinal 12653 19 19 bridle 4420 6 5 investment 4422 46 41 organelle 4421 6 6 line_of_scrimmage 12654 17 17 residue 12656 17 17 lymphatic 12655 10 10 emphasizing 4423 21 21 focused 4424 10 10 league 4425 31 31 torture 12657 14 14 succeed 4426 61 60 gate 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asserting 4475 9 9 rhode_island 4477 10 9 variously 12711 33 33 periodically 4479 16 16 adverb 4478 15 15 fawn 12712 7 7 founder 12713 45 45 improper 4480 24 23 flute 12714 23 22 sardine 4481 6 6 convicted 12715 26 26 synthesize 12716 35 33 harder 4482 12 12 ceremonies 4483 16 16 cereal 12717 50 47 spartan 4484 7 6 move 12718 483 469 cervical 12719 10 10 yugoslavia 4485 19 18 prophetic 4486 11 9 distinct 4487 114 104 woodwind 4488 10 10 allergy 4489 5 5 depressed 4490 33 33 determined 12721 112 110 preserved 12720 50 50 axiom 12722 11 11 jaws 12723 45 45 overly 12724 26 26 affection 12725 74 73 brother 12726 110 109 cotyledon 4491 9 9 course_of_study 4492 15 15 pronounced 4493 35 34 akkadian 4494 15 15 superior 12727 104 99 accepted 4495 140 135 fellowship 12728 6 6 preach 12729 8 8 softness 4496 7 7 compute 12730 18 18 bed 12731 192 180 urine 12732 99 95 dermatitis 4497 10 10 situated 12733 104 103 scrutiny 12734 6 6 medical 12735 88 85 nevada 4498 14 12 as_usual 12736 5 5 movie 4499 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paralysis 13251 33 33 jamaica 13252 15 15 educated 13253 16 16 spectacles 4985 10 10 mushroom 4986 77 72 interior_angle 13254 6 6 distribute 13255 49 49 packed 4987 36 34 finishing 13256 16 15 genus_bos 4988 7 7 conjunctive 13257 5 5 adhere 13259 55 55 police_car 13258 5 5 french 13260 384 377 traditionally 13261 21 21 depend 4990 47 47 adolescence 4989 9 9 agility 13262 7 7 device 13263 229 224 camera 4991 44 42 consonant 4993 45 41 nut 4994 146 143 illuminate 4992 8 8 vessel 13264 246 243 exciting 13265 36 35 spatial 4995 37 32 subtraction 4996 7 7 glowing 13266 12 12 th 13267 15 13 air_force 13269 18 18 poliomyelitis 13268 8 8 completion 13271 34 34 hypocritical 13270 6 6 socket 13272 29 28 uses 13273 188 188 faction 13274 20 19 assigning 4997 7 7 evade 13275 20 18 played 4998 254 245 unaffected 4999 14 13 dangerous 13276 82 77 apply 13277 161 158 badly 5000 69 58 peach 13279 36 35 manually 13278 10 10 evil 13280 111 106 amber 13281 13 13 sealing 5001 10 9 script 13282 22 22 example 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14 declaration_of_independence 5223 18 18 threadbare 5224 5 5 chlorophyll 5225 19 19 equality 13510 21 20 stare 5226 14 14 sinner 13511 11 11 assumption 5227 35 32 norm 13512 36 35 city 5228 898 849 sketch 13513 21 21 congestion 13514 18 18 punish 5230 12 12 trunks 5231 25 25 reformed 5229 9 9 arthropod 13515 50 50 flaring 5232 6 6 positively 13517 8 8 splice 13516 7 7 wrinkled 13518 25 24 spontaneously 13519 18 18 straightforward 13520 13 13 ambiguous 5233 20 18 henry_vii 5234 5 5 slide 13521 41 41 inferiority 13523 6 6 purport 13522 5 5 solar_radiation 13524 5 5 perception 13525 42 42 magnetic_pole 5235 5 5 lien 5236 12 11 plutonium 5237 6 6 garment 13526 145 140 exertion 5238 14 12 result 5240 433 424 crater 5239 10 9 sizzling 13527 6 5 impressed 13529 12 12 scapose 13528 5 4 constitutional 5241 34 32 ceremonial 13530 29 28 biology 13531 191 191 airborne 5242 5 5 helpless 5243 16 15 alpha 13532 13 12 text 5244 134 129 automatically 5245 41 40 gemstone 13533 41 41 disc 13535 23 23 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13560 5 5 bodily_process 5269 5 5 newspaper_publisher 13561 7 7 ecclesiastical 5270 33 33 overflow 13562 6 5 british 5271 686 678 out_of_sight 13563 8 8 in_case 5272 17 17 plowed 13564 8 8 hateful 13565 5 5 transverse 13566 13 13 adaptation 5273 15 15 bend 13567 44 41 youngster 5274 5 5 unawares 13568 7 6 caste 5275 25 19 speaking 13569 100 98 argentina 5276 49 47 vitamin_c 13570 6 6 ox 13571 15 14 notable 13572 12 12 linguistic 13574 33 33 iceland 13573 19 18 total 13575 92 90 interchange 5277 23 23 development 13576 292 280 surgery 5278 81 80 factor 5279 76 76 forgive 13577 11 11 hinge 13578 66 66 treble 13579 6 6 outgrow 5280 5 5 involuntarily 13580 6 6 uprise 5281 6 6 surgical 5282 85 83 complete 13581 192 184 multiplied 5283 17 17 interwoven 5284 8 8 southeast_asia 5285 20 20 pants 5286 10 10 amazon 5287 10 10 stain 5288 58 55 order_agaricales 5289 8 8 parting 5290 8 7 quotation 5291 19 18 black 13583 522 500 pectoral 13582 18 16 barber 5292 8 7 sewn 13584 16 16 permeable 5293 7 6 young_person 13585 8 8 institutional 13586 7 7 boston 13587 24 22 wood 5295 521 508 disrupted 5294 9 9 hate 13588 25 25 pleated 13589 6 6 structured 13590 7 7 continuity 5296 8 8 altered 5297 36 34 bread 5298 137 136 wattle 13591 9 9 vagina 5299 41 40 necked 13592 13 13 critically 5300 7 7 adventure 5302 31 31 communion 13593 10 10 tallow 5301 7 7 pealike 5304 7 7 in_trouble 5303 6 6 mystery 5305 13 13 bhutan 5306 6 6 inhaled 13595 15 15 rental 13594 9 8 drink 5307 205 200 professed 13596 7 6 notional 5308 7 6 demonstrative 13597 5 5 lover 5309 53 53 resolution 13598 31 30 fixing 13599 7 7 infusion 5310 5 5 urination 13601 17 17 king_arthur 13600 9 9 alternation 13602 6 6 not_able 13603 16 16 writing 5311 177 173 disloyal 5312 6 6 marry 13604 26 26 decreasing 5313 22 21 strenuous 13605 14 14 written_agreement 5314 7 7 done_with 5315 17 16 call 13606 178 170 scholastic 13607 6 6 chipped 5316 5 5 environment 13608 123 123 comment 5317 58 56 larva 13609 190 186 distinction 13610 54 54 mortise 13611 8 8 knitting 5318 11 11 details 5319 78 76 titmouse 13612 6 6 doubly 13613 7 7 contour 5320 8 8 significant 5321 47 42 illegally 13614 40 40 admiral 5322 20 19 saucer 13616 10 10 magnitude 5323 51 50 victorian 13615 9 9 improvised 13617 12 12 bring_back 13618 7 7 marinate 13619 8 8 participating 13620 15 15 untruth 13621 5 5 mobile 5324 15 14 shelley 5325 5 5 clip 13622 20 17 interlaced 5326 5 5 insulting 5327 6 6 contained 5328 76 76 hercules 13623 11 11 wheelchair 13624 5 5 cultivation 5329 34 34 manchester 13625 5 5 post 5331 105 101 desperately 5330 6 6 easily 13626 206 201 adulthood 13627 6 6 bathroom 5332 19 19 subtle 5333 19 18 simultaneous 13628 20 20 ford 13629 6 6 crossbar 13630 9 9 bathing 5334 6 6 organizer 13631 5 5 harrier 13632 6 6 completeness 5335 5 5 connection 5336 76 75 come_back 5337 5 5 traveler 13634 36 36 mayor 13633 24 23 adopt 13635 27 25 fanned 13636 5 5 contempt 13637 39 39 seaweed 5338 22 22 inhibitor 13638 7 7 adverse 5340 19 19 whitewash 5339 5 5 faucet 5341 6 6 petrel 5342 11 10 closed_in 13639 8 8 micro 5343 7 7 remarkable 5344 26 26 canon 5345 13 12 rambling 13640 12 11 beast 13641 8 8 weak 5346 103 99 region 5347 751 741 exalted 13642 9 9 codified 13643 8 8 wartime 13644 12 12 flavoring 13645 42 42 prairie 13646 35 33 cumin 5348 5 5 oh 5349 11 11 digestion 5350 25 25 abundantly 13647 16 16 serbian 5351 5 5 padding 5352 11 11 language 13648 851 789 lengthy 5353 12 12 homogeneous 5354 17 17 fresh_water 5355 31 31 cooking 13649 117 117 in_length 5356 12 12 thousandth 13650 26 26 unhurried 13651 8 6 scaly 13652 21 21 seriousness 13653 11 11 syntactic 5357 12 10 conic 5358 14 14 pains 13654 23 23 monotonous 13655 17 17 incumbent 5359 5 5 upward 13656 92 91 plastered 5360 5 5 classification 5361 367 363 scrap 13657 5 4 form_genus 13658 9 9 drive 5362 135 130 feat 13659 14 14 alternative 13660 53 51 tear 5363 17 17 broadly 5364 19 19 magnetism 13661 19 19 testimony 5365 30 30 substantially 13662 5 4 temperament 13663 16 16 effects 5366 153 149 plumelike 13664 5 5 acetylcholine 5367 6 6 protein 13665 136 128 newly 13666 33 30 lactation 5368 6 6 stamen 13667 15 15 devious 5369 8 8 dark_blue 5371 12 12 placement 5370 7 7 abuse 5372 26 26 concerned 13668 212 206 rubber 13669 90 86 assam 13670 5 5 implication 13671 13 13 raid 5373 26 26 modern_greek 5374 5 5 mexican 13672 72 69 reasonably 5375 6 6 wield 13673 6 6 analytic 5376 7 5 life 13674 706 677 at_least 13676 50 49 irritation 13675 31 31 punishing 13677 9 9 install 13678 8 8 atherosclerosis 5377 10 10 palace 13679 30 30 monotreme 5378 7 7 the_english 5380 87 87 permanent 5379 61 59 shades 5382 10 10 intrinsic 13680 12 12 subdivision_basidiomycota 5381 5 5 penetration 5383 20 20 solitary 13681 102 98 crustacean 13682 46 46 float 5384 38 37 orbit 5385 83 67 economically 5386 17 17 southward 5387 18 18 doomed 5388 9 9 desirable 13683 37 35 matter_of_fact 5389 7 6 every 13684 225 217 intermediate 5390 61 57 invulnerable 5391 5 5 intentionally 13685 23 23 scintillating 5392 5 4 in_some_respects 13686 6 6 invite 13687 9 8 apartheid 5393 5 5 recklessly 13688 5 5 ovate 5394 16 16 terrible 13689 33 33 avenue 13690 7 6 inaudible 5395 6 6 subtropical 5396 49 49 fluorine 5397 7 7 fractious 13691 5 4 epithelial_tissue 5398 6 6 menstrual_flow 13692 6 6 tarsier 13693 5 5 serene 13694 7 6 jerboa 5399 6 6 unspecified 13695 22 21 betray 5400 22 22 blackberry 5401 20 18 prodigal 13696 5 5 assurance 5402 15 15 appellate_court 13697 6 6 care 13698 143 139 displeasing 13699 5 5 accommodate 13700 19 19 explain 5403 75 75 microphone 13701 15 15 pressing 13702 31 30 ductile 13703 13 12 eskimo 5404 9 9 homo_sapiens 5405 5 5 disentangle 13704 6 6 prune 13705 7 7 numeral 5406 10 9 highly 13706 227 223 alkaloid 5407 25 25 flimsy 5408 7 6 ignore 13707 12 12 seeded 13708 38 36 disappearance 5409 7 7 perishable 5410 5 4 genial 13709 6 6 endorse 5411 9 9 study 5412 458 456 dichromacy 13710 6 6 fundamental 5413 39 35 futile 5414 8 8 injury 13711 121 120 acclaim 5415 8 8 family 13712 602 593 inhuman 5416 6 5 flip 5417 15 15 horned 5418 32 31 electrical_energy 13713 8 8 boulder 5419 13 12 unisexual 13714 7 7 clapping 13715 5 5 dissent 5420 10 10 predator 13716 8 8 seam 13717 28 25 sheepdog 5421 7 7 mouth 13718 252 241 weed 5422 104 103 untreated 13719 9 8 out_of_wedlock 5423 7 7 rice 13720 52 51 bucket 5424 18 18 midwest 13721 13 13 artillery 5425 36 36 ago 5426 59 58 organized 13722 114 112 mistake 5427 62 60 invader 13723 13 13 craftsman 5428 20 20 encircling 5429 9 9 minnesota 13724 20 18 ladybug 13725 7 7 eurasia 13726 73 73 slip 5430 30 30 lean 13728 49 47 sense_of_taste 13727 6 6 immobilize 5431 9 9 conveying 5432 18 18 hormonal 5433 6 6 gastrointestinal 5434 9 9 family_hygrophoraceae 5435 5 5 entertain 13729 10 10 telegraphic 13730 10 8 complexity 13731 14 14 toxic 5436 74 73 altar 5437 11 11 propelling 13732 9 9 threaten 5438 10 9 sulphate 13733 8 8 dead 5439 159 141 smelling 13734 53 53 together 13736 565 556 disposed 13735 54 54 cart 13737 27 27 bald 5440 13 12 format 13738 11 10 nostril 13739 11 11 patron_saint 5441 11 11 formed 5442 399 397 mollusk 13740 44 44 normal 5443 265 256 closet 5444 14 12 congenital 13741 37 37 compressed 13742 40 40 rewrite 5445 7 7 discontent 13743 13 13 chill 13744 12 12 chafing 5446 5 5 goods 13745 216 212 body_temperature 5447 12 12 catcher 5448 17 17 daring 13746 10 10 spore 5449 132 128 regulator 5450 9 9 flatten 13747 15 14 extravagant 5451 22 22 perianth 5452 10 10 business_deal 13748 5 5 poseidon 5453 5 5 expansive 5454 5 4 purple 5455 383 372 advised 5456 11 9 toy 5457 45 45 naval 13749 49 48 redness 13750 12 12 station 5458 72 70 clash 5459 11 11 observable 13751 11 11 ardent 5460 15 13 wound 13752 103 101 algonquian 5461 30 30 carp 5462 8 8 capillaries 5463 9 9 grin 13753 10 10 ecclesiastic 5464 7 7 bait 5465 18 18 hypothesis 13754 26 26 songbird 13755 37 37 fin 5466 34 34 amphetamine 5467 8 8 scalloped 5468 6 6 chesapeake_bay 5469 9 9 repellent 5470 11 11 labrador 5471 5 5 7th 13756 18 18 icelandic 5472 5 3 starving 5473 9 9 shame 5474 29 29 hatching 13757 5 5 examine 5475 80 80 commonplace 13758 9 8 arabian 13759 10 10 edible_fruit 13760 51 51 condemning 5476 5 5 kinship 5477 18 17 smart 13761 17 16 wandering 13762 23 23 ophthalmic 5478 6 6 severity 5479 12 12 lawful 13763 14 14 urgency 5480 9 9 ionosphere 13764 8 8 answer 13765 142 134 getting 13767 70 70 dispel 13766 5 5 botany 13768 127 127 alternation_of_generations 13769 5 5 insulin 13770 20 18 cud 13771 9 9 contents 13772 37 37 disposable 13773 7 6 deceased 5481 7 7 slim 13774 16 16 endurance 13775 16 16 rowing 5482 9 9 hydrozoan 13776 5 5 subordinate 5483 37 36 civilization 13777 57 56 dominican_republic 13778 8 8 advertising_campaign 5485 7 7 ventilate 5484 5 5 satisfactory 13780 24 22 independently 13779 20 20 arduous 5486 7 7 farm 5487 77 74 saffron 5488 7 7 arithmetic_operation 13781 5 5 catkin 5490 16 16 painfully 5489 7 7 excommunicate 5491 7 7 break_down 13782 14 14 supersede 13783 11 11 speaker 5493 100 98 aside 5492 24 21 successive 5494 49 49 channel 13784 79 78 frame 13785 82 81 hog 5496 24 24 penetrable 5495 6 5 pliers 13787 8 8 smothered 13786 6 5 salute 5497 7 6 glazed 13789 21 19 perceptual 13788 11 11 decent 13790 12 12 revealed 5498 29 29 movement 5500 353 345 ease 5499 38 37 clothed 13791 19 18 break_into 13792 20 18 refractive_index 5501 5 5 dwarf 5502 38 37 platinum 13793 18 13 literally 13794 17 16 annually 5503 9 9 crabmeat 5504 5 5 attempt 13795 118 118 reversed 13796 25 25 laughter 13797 46 46 proclaimed 13798 9 9 rock 5505 275 271 violent 13799 115 109 creature 13800 40 40 built 13801 196 193 legend 5506 33 33 luxury 13802 13 13 magnolia 13803 6 6 bulk 13804 28 27 cell_nucleus 13805 9 9 military_police 5507 6 6 flowery 13806 5 5 emblem 5508 29 29 greenish_yellow 13807 18 18 helm 5509 6 6 farming 13808 20 19 transitional 13809 5 4 merited 5510 5 5 condemnation 13810 6 6 carboxylic_acid 5511 8 8 archbishop 13811 7 7 credit 5512 69 63 trembling 13812 11 11 suborder 5513 16 16 rabbit 5514 39 38 atom_bomb 5515 7 7 initial 13813 37 37 phenomenon 13814 98 95 correctness 13815 6 6 fuss 13816 5 5 confusing 5516 15 14 congress 5517 24 23 shiva 13817 8 8 tilt 13818 13 13 acrobat 5518 16 16 archeological 13819 8 8 category 5519 60 59 central 13821 743 741 expect 13820 46 44 ideology 13822 15 15 observing 5520 10 10 perfume 13823 49 48 at_birth 13824 19 19 blubber 5521 6 5 kuwait 13825 7 7 social_organization 5522 7 7 pleased 5523 11 11 adam 13826 16 15 purposeful 5524 6 6 upholster 13827 11 11 dictatorship 5525 5 5 cursing 5526 5 5 sedative 5527 26 26 said 5528 180 179 clam 5529 35 33 beta_blocker 13828 8 8 transcription 5530 5 5 generation 13829 51 45 aware 5531 46 43 patron 13830 23 22 very_fast 13831 14 14 shrike 5532 9 9 family_umbelliferae 5533 6 6 yell 13832 6 6 discipline 5534 64 63 corporate 13833 20 18 quite_a 5535 5 3 comic 5536 32 31 syntax 5537 17 15 bushy 13834 62 62 ingenuity 13835 7 7 sag 5538 8 6 hollow 13836 106 102 depleted 5539 11 10 tend 13837 58 58 lyricist 5540 6 6 abbot 13838 5 5 prevailing 13839 21 21 casing 13840 16 16 glittering 5542 9 8 secret_agent 5541 5 5 plumlike 13841 6 6 mechanism 5544 61 61 favorable 5543 48 45 council 13842 21 20 winston_churchill 5545 8 8 propagate 5546 18 18 photographic 5547 40 39 scandal 13843 25 25 domineering 5548 10 10 juice 13845 98 98 channels 13844 27 25 depicting 5549 6 6 musical_note 13846 9 9 market_price 5550 5 5 guarantee 13847 29 29 openly 13848 29 29 essential_oil 5551 9 9 post_office 5552 18 18 recital 13850 9 9 ribosome 13849 6 4 ebb 5554 7 7 shear 5553 5 4 america 5555 235 235 orchid 5556 191 182 geometrical 5557 5 5 prosecution 13851 15 15 gallant 13852 7 6 toasted 13853 7 7 zaire 5558 9 9 bribe 5559 10 9 arabia 13854 20 20 placenta 13855 24 23 hillside 13857 12 12 hearts 13858 18 17 zeal 13856 7 7 october 5560 17 16 colorless 13859 125 122 reported 5561 29 27 bustard 13860 5 5 chew 5562 28 25 bowels 13861 9 9 cochlea 5563 10 10 cabin 5564 19 18 manual_labor 13862 6 6 stories 13863 100 99 circumference 5565 10 10 facial 5566 18 17 southernmost 5567 12 12 contention 13864 8 8 talented 5568 8 8 recur 13865 36 35 unto 5569 7 6 professor 5570 48 47 every_month 5571 6 6 clipping 5572 7 7 whether 5573 68 65 go_home 13866 5 5 whitish 13867 73 72 harshness 5574 5 5 reflective 5575 6 6 thistlelike 13868 6 6 sidelong 5576 8 8 stunting 13869 5 5 prediction 5578 21 20 brutal 13870 12 12 inscription 5577 15 15 opportune 13871 7 6 palestine 5580 14 14 straining 5579 6 6 descendant 13872 9 9 peeled 13873 6 6 silence 5581 42 40 prejudiced 5582 7 7 dividend 5583 20 17 circuit 5584 95 79 baltic_sea 13874 13 13 court 5586 256 240 digitalis 5585 5 5 shredded 5587 9 9 mantis 5588 6 6 steering_wheel 13875 5 5 pencil 13876 20 20 esau 13877 5 5 indistinct 5589 24 22 comprise 13878 170 169 sleepy 13879 7 6 dung 13880 12 11 execute 5591 19 19 visitation 5590 6 5 sleeve 13881 34 33 civilized 5592 5 4 greedy 13882 11 10 black_hills 5593 7 7 armed_forces 13883 27 27 noisily 5594 25 25 cream_cheese 5595 5 5 seize 5597 27 26 oral_contraceptive 5596 15 15 thermostat 13884 9 9 monarch 5598 42 42 heterogeneous 13885 5 5 caudal 13886 10 9 towel 13887 24 23 stock 13888 148 143 melancholy 13889 12 11 reality 5599 66 66 destitute_of 5600 5 5 melodic 13891 9 9 firsthand 13890 6 6 penetrating 13892 23 22 chalcedony 5601 9 9 bars 5602 49 49 unfasten 5603 7 7 fifth 5604 36 36 face_to_face 5605 9 7 close 13893 238 224 fermenting 5606 9 9 weave 5607 21 20 available 5608 160 155 cleaning_implement 13894 6 6 drawn 13895 137 135 assault 13896 15 15 block 13897 131 118 however 5609 12 9 thunderbolt 13898 5 5 vibrate 13899 12 12 grow 13900 201 197 sloping 5610 39 36 distinctly 5612 14 14 needy 5611 5 5 determinant 13901 8 8 confidential 5613 12 11 tolerant 5614 15 15 weakening 13902 8 8 progesterone 5615 8 7 output 13903 51 48 respiration 13904 29 29 muscle 5616 363 347 needlepoint 13905 5 5 outer 5618 137 134 respectful 5617 7 6 mission 5619 27 26 send_out 13906 5 5 nobel_prize 13907 8 8 crushed 13908 29 28 edit 5620 11 11 brief 5621 96 94 rooms 13909 49 47 nausea 13910 28 28 scaleless 13911 19 19 foresee 5622 5 5 restraint 13912 64 63 calico 5623 6 4 starlike 5624 11 11 sexual_relationship 5625 8 8 kansas_river 5627 5 5 molybdenum 5626 5 5 conforming 5628 59 59 eyeglasses 13913 5 5 gnat 5629 6 5 institution 13914 106 103 amends 5630 5 5 black_and_tan 13915 7 7 burman 13916 7 7 sh 5631 5 5 laboratory 13917 30 30 standardized 5632 12 11 threatening 13919 26 25 influenza 13918 8 8 irritate 13920 12 12 atomic_bomb 13921 7 7 amphibious 13922 15 12 approval 5633 55 54 expended 13923 6 6 photographic_film 5634 23 23 abbreviated 13924 7 7 social_group 5635 13 13 stalwart 13925 6 5 unmoved 5636 5 4 wild_ox 5637 5 5 closure 13926 12 12 inconspicuous 5638 17 17 bleached 5639 11 11 parrot 13927 17 16 recharge 5641 5 5 on_guard 5640 5 5 inattentive 13928 6 5 bearded 13929 6 6 invertebrate 5644 29 29 pearly 5643 8 8 cooking_utensil 5642 7 7 title 13930 144 138 recapture 5646 11 11 forcible 5645 7 7 electromagnetic 13931 43 43 still 5648 221 209 objective 5647 42 40 yarn 5649 42 40 preference 13932 21 21 agreeing 13933 10 10 mosaic 13934 10 10 cordage 5650 9 9 marine 13935 251 251 chilean 5651 10 10 forged 13936 8 8 tonic 13937 18 17 arrest 13938 24 22 wrist 13939 33 33 surplus 13940 8 8 inverted 5653 14 14 ugric 5652 7 7 teacher 5654 142 140 classify 13941 26 26 economic_policy 5655 5 5 third_party 5656 5 5 peaceable 13942 5 4 depend_on 13943 8 8 galaxy 5657 9 8 tar 5658 20 18 pathologically 5659 5 5 christmas_tree 13944 9 9 condyle 13945 6 6 ephemeral 5660 5 5 nice 5662 41 32 internally 5661 8 8 great_plains 5663 14 14 kindness 13946 21 21 lower_court 13947 5 5 welsh 13948 29 28 secrecy 5664 13 13 ionization 5665 5 5 signed 13949 39 36 genus_helipterum 13950 6 6 mica 13951 13 13 shrine 5666 6 6 stairs 5667 7 7 uncontrolled 5669 29 28 discourse 5668 18 18 sweat 13952 28 27 popular 5671 152 144 boldly 5670 5 5 percent 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615 cambodia 13981 13 13 authenticity 13980 12 12 sat 13982 55 55 betrayal 13983 7 7 formula 5695 27 27 stingy 13984 5 5 bask 5696 7 7 unflagging 5697 6 5 plead 5698 9 9 amusing 5699 19 18 umbrella 5700 27 25 orange_red 13985 37 37 pang 13986 6 6 cop 5701 5 5 increased 5702 74 73 bough 5703 11 9 ski 5704 14 10 six 5705 108 108 termite 13987 28 27 coldness 5706 9 8 dimensional 5707 48 45 regularity 13988 7 7 placid 5708 7 6 bouncing 13989 5 5 depository 5709 7 7 immaculate 5710 5 3 inside_information 13990 7 7 locus 13991 8 8 cerium 5711 6 6 archipelago 13993 15 15 naples 13992 9 9 important_person 5712 6 6 vacation 13994 30 30 zoologist 5713 12 12 replacement 5714 17 16 lemon_juice 13995 17 17 absolve 5715 5 5 thus 13996 26 26 daily 5717 47 44 conform 13997 10 10 restricted 5716 44 44 plum 13998 29 28 grate 13999 19 17 similar 5718 405 398 commemorate 14000 11 11 stupefying 5719 5 4 diacritical_mark 5721 8 8 pustule 5720 7 7 second_class 14001 5 3 summer 14002 102 100 innocent_of 14003 5 5 laden 14004 9 9 waste 5722 82 79 rotating 14005 66 63 bypass 14006 5 5 natural_process 5723 8 8 precise 14007 26 23 gray_matter 14008 7 7 same 14010 449 425 indicate 14009 325 320 rating 5724 15 14 dispensed 5725 10 10 byzantine_empire 14011 6 6 named 14012 54 52 nightclub 5726 7 7 clogging 14013 5 5 vertically 14014 24 24 motivation 5727 13 13 stuffing 5728 11 11 convert 5729 117 112 pathologist 5730 5 5 operate 14015 155 150 labor 14016 89 88 babylonia 5731 6 6 shinto 5732 8 8 athena 14017 8 8 based_on 14018 281 277 apprehended 14019 7 7 timing 14020 14 14 knot 14021 67 64 sen 14022 6 6 larger 5733 161 161 compliments 5734 5 5 toddler 14023 9 9 time_period 5735 27 27 attributable 5736 31 30 predicament 5737 7 7 propriety 14024 16 16 inflammatory 14025 49 45 occupied 14026 68 65 nude 5738 6 6 large_white 14027 28 28 turned_out 14028 8 8 here 5739 129 123 to_and_fro 5740 9 9 sickle 14029 7 6 mourning 5741 14 14 metallic_element 5742 71 71 guerrilla 5743 11 11 shortage 14030 16 14 coke 5744 11 9 raised 5745 152 149 with_reference_to 14031 10 10 fibrin 5746 6 6 ass 5747 5 5 lark 14032 7 6 ensure 5748 5 5 tv_program 14033 6 6 salivary_gland 5749 7 7 in_the_mind 14034 18 18 authorization 14035 16 16 mistress 5750 17 17 decomposed 14036 8 8 arched 5751 9 9 congo 14037 21 19 bosom 14038 8 7 soulful 5752 5 4 innermost 5753 17 16 carotenoid 14039 5 5 alpine 5754 29 29 surgical_instrument 14040 10 10 deterioration 5755 8 8 separate 5756 245 240 bowling 5758 21 20 solve 14041 27 26 symbolize 5757 9 8 corrupted 5759 8 8 symphonies 5760 6 6 chattering 5761 8 7 deafness 5762 7 7 exalt 5763 5 5 link 14042 36 36 lake_superior 14043 9 9 lustrous 5764 29 28 space 14044 290 282 herpes_virus 5765 7 7 hypotenuse 14045 6 6 sessions 14046 5 5 symptom 5766 141 139 parts 14047 477 473 nap 14048 21 21 peer 5767 23 23 determining 14049 43 43 negation 5768 9 9 parallel 5770 131 123 electric_circuit 5769 6 6 bohemia 14050 6 6 workmanship 14051 6 6 judgment 14052 135 129 corporation 14053 89 86 conical 5771 47 46 devout 14054 5 5 lawn 5772 45 45 province 14055 73 71 northwestern 14056 174 173 jellied 5773 8 8 radioactivity 5774 13 13 after_hours 5775 6 5 acceptable 14057 44 43 undress 14058 5 5 delta 14059 11 11 expert 14060 53 53 female 5776 243 229 schooling 5777 12 12 mime 14061 5 5 incident 14062 42 41 attack 5778 251 242 decorative 5779 69 69 buffer 14063 8 7 true_to 14064 5 5 el_salvador 5780 9 8 sized 14065 135 135 acute 14066 88 85 surf 5781 12 11 hardy 5783 52 51 plausible 5782 9 9 pulley 5784 9 9 pouched 14067 7 7 christian_church 14068 16 16 disordered 14070 9 9 sacked 14069 5 5 fuel 14071 92 89 pelican 14072 5 5 offer 5785 142 137 copied 5786 11 11 hearing_loss 14073 8 8 rearward 5787 7 6 assure 5788 6 6 thoughtfulness 5789 6 6 imitative 5790 17 16 morphine 14074 10 8 financially 5791 25 23 roe 5792 7 7 abbreviation 5794 17 17 publication 5795 70 68 squat 5793 7 7 overtake 14075 6 6 grayish 14076 64 63 as_well 14077 80 79 adequate 14078 48 46 detrimental 14079 5 5 civil_year 14080 12 12 restless 14081 18 17 gay 14082 19 16 receipts 5796 8 8 draped 14083 8 8 spirally 5797 15 15 shad 5798 12 12 miocene 5799 6 6 twine 5800 8 8 irreverent 5801 9 8 clubmoss 14084 6 6 digital_computer 14085 7 7 weatherproof 14086 5 4 theoretically 5802 7 7 racial 5803 21 21 hay 14087 49 48 determination 14088 44 44 cooperative 14089 11 11 juror 14090 8 8 william 5804 12 12 in_the_middle 5805 63 63 skier 5806 10 10 frontal 5807 16 16 commercially 14091 37 37 hysteria 5808 8 8 glandular 5809 16 16 needle 14092 98 94 subject_to 5810 162 161 washing 5811 50 48 turn_away 5812 12 12 liabilities 14093 5 5 justify 5813 18 18 conquest 14094 17 17 converter 14095 5 5 farthest 14096 28 25 comprehension 14097 10 9 gap 5814 33 32 dimension 5815 65 63 crippled 14098 5 5 ineligible 14099 6 5 faded 5816 10 10 sprain 14100 6 5 entice 5817 9 9 favoritism 5818 5 5 alveolus 5819 5 5 fitness 5821 10 10 louis 5820 8 8 bow 14101 68 65 inhalation 5822 7 7 cns 14102 6 6 blood_vessel 14103 29 29 peptide 5824 8 7 foothills 5823 7 7 resinous 5825 31 31 dire 5826 5 5 fourth 14104 59 59 groom 5827 7 7 inspired 14105 20 20 temporal 5828 48 44 backward 14106 61 60 crackling 5829 8 8 maiden 5830 12 11 clarinet 5831 14 12 deserts 14107 19 19 stringed_instrument 14108 22 22 encouraging 14109 13 13 epidermis 14110 14 14 stimulant 5832 20 20 paleozoic 14111 10 10 crunch 14112 11 8 pretending 14113 13 13 audio_system 5833 5 5 scheduled 14114 32 30 turn_over 5834 10 10 corpuscle 14115 5 5 ichthyosaur 5835 7 7 temper 5836 28 27 frog 5837 25 25 subnormal 14116 7 6 boards 14118 26 26 supposedly 14117 10 10 clotting 14120 9 9 nuclear_physics 14119 5 5 vengeance 5838 12 10 touch 14121 117 113 noon 5839 30 30 gilbert_and_sullivan 14122 6 5 donated 5840 7 7 rwanda 14123 9 8 vitreous 14124 9 8 written 5841 308 303 latvia 14125 7 7 limited 14126 104 102 achromatic_color 5842 6 6 unfriendly 14127 21 20 faculties 5843 6 6 warn 14128 31 27 spitting 5844 5 5 impregnate 14129 11 11 alive 5846 35 31 rainfall 5845 10 10 dentistry 14130 28 28 completed 5847 38 36 genus_cassia 14131 10 10 species 5849 360 353 despicable 5848 9 9 aperture 14132 16 16 contingent 14133 10 10 tudor 5850 8 6 a_couple_of 14134 7 7 scrubby 14135 8 8 organist 14136 8 8 range_in 5851 7 7 tariff 5852 16 16 abdominal 5853 42 42 animate 5854 5 5 cosmic 5855 12 10 grape 14137 87 83 flushed 14138 15 15 bleak 14139 5 4 genre 14140 23 21 elect 14141 15 15 malfunction 5856 9 9 taxi 14142 13 13 ostentatious 14143 12 12 growing_season 5857 6 6 alteration 5859 23 23 diver 5858 15 15 sentimentality 14144 9 9 dam 14145 19 19 priced 14146 12 12 track 5861 70 69 response 14147 132 127 mortgage 5860 30 26 laurel 5862 7 7 milkshake 14148 5 5 hexagonal 5863 7 7 loud_noise 5864 16 16 heap 5865 12 12 alleged 5866 17 17 oily 14149 43 43 communicative 14150 5 5 chemical_compound 14151 24 24 sprout 5867 6 6 configuration 14152 19 19 rare_earth 5868 23 22 prefer 14153 24 23 chaste 5869 7 7 intoxication 5870 6 6 walking 14154 72 70 blamed 5871 6 6 trusted 5872 16 16 bank 14155 110 101 bored 14156 14 13 vehicle 5873 209 203 coupled 14157 6 6 gopher 5874 11 11 axe 5875 7 7 slap 5876 8 8 youth 5877 42 41 trade_name 5878 269 267 installation 5879 9 9 subtropics 14158 9 9 garden 14159 115 113 shoulder 14160 131 123 mantra 14161 5 5 stupefied 14162 6 6 state_capital 5880 18 18 propel 14163 95 90 spite 14165 25 25 federal_agency 5881 7 6 illustrate 14164 8 8 lie_in 14166 6 5 nozzle 14167 7 7 turpentine 14168 7 7 dissertation 5882 10 10 antagonist 14169 6 6 luster 14170 13 13 done 5883 164 160 medical_school 14171 8 8 tendon 14172 24 23 spring 14173 146 141 lyric 14174 28 27 quakers 5884 5 5 reappear 5885 5 4 fringed 14175 23 22 insulator 5886 6 6 cheat 5887 13 13 falsify 14176 7 5 idea 5888 332 324 prophesy 14177 5 5 gourd 14179 11 11 unduly 14178 8 8 experimental 5889 23 22 chemical_change 14180 5 5 equal 14181 242 234 calcutta 5890 6 5 calcify 14182 8 7 hearth 14183 15 14 oppose 14185 24 24 inquisitive 14184 5 5 honor 14186 80 78 specious 14187 8 8 christianity 14188 36 36 parental 5891 11 11 publicize 5892 5 5 broad 14189 204 196 whole_blood 14190 6 6 alternating_current 5893 13 13 grandchild 5894 8 8 constipation 5895 6 6 vary 5896 37 35 shore 5897 75 73 genitalia 14191 6 5 bureaucracy 14192 10 10 internal_combustion 14193 33 33 gregarious 14194 22 21 spear 5898 22 19 leftover 14195 5 5 evergreen 14196 459 455 steps 14197 75 73 exercising 5899 18 18 swinging 5900 27 27 true 5901 208 197 irrigate 5902 7 7 winner 14198 40 40 loosely 5903 39 39 press 14199 69 68 etched 14200 6 6 responsible 5904 23 20 background 14201 50 50 infer 14202 16 16 deformity 5905 11 11 olfactory 14203 7 7 deduction 14204 14 13 consumptive 14205 6 3 woody 5906 160 153 overtone 14206 5 5 nautical 14208 39 38 beef 14209 55 54 tact 14207 10 10 visually 14210 11 11 malice 5907 8 8 translation 14211 36 34 epic_poem 5908 9 9 leaf 14212 149 144 pond 5910 46 46 positively_charged 5909 7 7 re 5911 35 33 finance 14213 26 26 disintegrate 14214 14 13 livelihood 5912 6 6 fashion 14215 51 51 believe 5913 218 214 burrow 14216 76 76 desire 5914 121 120 entail 14217 10 10 trailing 5915 27 27 centesis 14218 6 6 in_public 14220 26 26 festival 14219 21 21 noble 14221 27 25 mediterranean_sea 14223 14 14 family_convallariaceae 5916 5 5 phonetic 14222 5 5 confining 5917 6 6 conduct 5918 135 133 embellishment 14224 9 9 accept 14225 96 93 fraternal 5919 5 5 masters 5920 21 21 fluctuating 5921 5 5 solid 14226 221 210 debate 5922 47 45 mode 5923 18 18 united_states_government 5924 16 16 auricle 14227 8 8 constricting 5925 5 5 down_the_stairs 14228 5 5 sign 5926 132 127 colony 5927 62 60 apron 14229 6 6 flax 5928 13 13 revolution 5929 77 76 lilylike 14230 6 6 frenzied 14231 7 5 medusa 5930 5 5 outermost 5931 19 19 poem 14232 72 71 scroll 5933 8 8 coin 5934 111 107 grove 5932 5 5 tied_up 5935 5 4 randomly 5936 7 7 newspaper 14233 110 106 georgia 14234 39 39 sign_language 5937 6 6 new_guinea 14235 23 23 influence 14236 275 271 mali 5938 9 9 endure 14237 23 23 carnival 5939 5 4 ration 5940 6 6 enlarged 5942 41 41 courtroom 5941 7 7 bondage 5943 5 5 shelflike 5944 5 5 dime 14238 8 8 mary 5945 40 38 wallpaper 5946 10 10 beneficial 5947 21 19 hardened 14239 34 33 mental_attitude 14240 6 6 mesh 14241 26 24 curly 14242 19 19 communicating 5948 16 16 puffin 5949 6 6 projection 5950 62 62 stress 14243 104 99 neurotic 14245 9 8 mores 14244 6 6 obstruct 14246 20 20 imaginary 14247 51 51 credit_card 14248 5 4 revision 14249 12 12 comprehensive 5951 16 13 polyhedron 14250 14 14 high_frequency 5952 7 7 administration 5953 50 49 carrot 5954 22 21 names 14251 110 110 adjusted 5955 33 32 jesus_christ 14252 8 8 fungal_infection 14253 17 17 performance 5957 233 223 thor 5956 5 5 wail 5958 5 5 in_width 14254 5 5 flaky 14255 7 7 forehead 5959 39 39 strategic 14256 13 9 romanticism 14257 6 6 dysfunction 14258 9 9 liver 14259 103 101 verge 5960 12 12 indignation 14260 6 6 incarnation 5961 10 10 proclaim 14261 11 11 plundering 5962 8 7 easygoing 5963 7 7 specified 5964 218 214 pull 5965 88 82 cranberries 14262 7 7 spar 14263 20 20 gait 5966 36 36 briefly 5967 42 40 speculation 14264 15 15 shuffle 5968 6 6 law_enforcement 14266 13 13 sour_cream 14265 6 6 heart_surgery 5969 6 6 locked 14267 24 23 as_required 5970 7 7 miner 14268 13 13 fancy 5971 21 20 extremity 14269 13 13 sauce 5972 87 85 game 14270 421 404 reading 14271 90 88 ether 5973 6 6 creased 14272 5 5 rebel 14273 15 15 get_it 5974 6 6 unethical 5976 11 11 jerky 5975 7 7 repeated 14274 76 75 glow 14275 21 20 fantasy 14276 10 10 sect 5977 35 33 homer 14277 10 10 training 5978 107 101 erythrocyte 14278 10 10 nearly 14279 93 92 allele 14280 13 12 extension 14281 49 48 contest 14282 56 55 cornbread 5979 7 7 oatmeal 5980 7 7 alphabetical 5981 15 15 haiti 14283 13 13 worrying 14284 6 5 sensational 5982 10 10 eccentric 5983 14 14 await 14285 18 18 fluctuation 14286 17 16 mold 5984 47 46 concavity 5985 8 8 nucleus 14287 93 86 tapping 5986 13 13 sunflower 14288 15 15 schizophrenia 5987 21 19 seeking 14289 23 22 minute 5988 117 114 settler 5990 18 18 fluffy 5989 11 11 put_forward 14290 8 8 bullet 14291 51 51 invasive 5991 12 12 manner_of_speaking 14292 13 13 generalized 14293 13 12 frigid 5992 5 5 cross_section 14294 18 18 steroid 5993 17 17 tempo 5994 33 33 mailed 14295 10 10 new_zealand 14297 121 120 flattery 14296 12 12 lose 5995 89 87 spikelet 14298 7 7 sarcosomataceae 14299 6 6 batsman 5996 5 5 pause 14301 28 28 milky 5997 42 42 compositae 14300 5 5 viceroy 5998 6 6 meritorious 14302 8 7 goat 5999 44 40 slant 14303 9 9 nocturnally 14304 7 7 firecracker 6000 6 6 to_the_south 14305 6 6 persisting 6001 6 6 inflate 14306 9 9 dangerously 6002 11 11 roman_catholic 14307 42 42 persistent 6003 51 49 legal_proceeding 6004 6 6 champagne 14308 13 13 treason 14309 9 9 shipboard_system 14310 5 5 intersect 6005 18 18 upland 14311 23 23 baja_california 6007 17 17 courting 6006 5 5 knob 14312 10 10 decreased 14313 11 11 take_away 6008 19 19 toothless 6009 14 13 successful 6010 68 67 fog 14314 33 33 these 6011 301 296 technician 6013 6 6 rhythmically 14315 12 11 pharyngeal 6012 5 5 semiaquatic 6014 14 14 proverbial 6015 8 5 unceremonious 14316 5 5 preserve 6017 47 47 peace 14317 85 81 tenth 6016 23 23 co 14318 27 26 bluish 6018 68 68 night 14319 262 250 drove 6019 31 29 ray 6020 195 186 waiter 6021 22 22 globular 6022 24 24 ghostly 6023 5 5 chad 14320 19 19 retard 6024 16 16 criminal_law 14321 10 10 chisel 6025 14 13 intellectual 14322 84 79 weather 14323 140 127 seating 6026 13 13 unfaithful 6027 7 7 flare 14324 10 10 designated 14325 31 31 lime_juice 6028 6 6 cockroach 14326 17 17 simplicity 6029 14 14 somalia 14327 10 10 blistering 6030 6 6 discarded 6031 16 16 review 14328 30 29 landowner 14329 6 6 supposed 6032 61 59 somatic 6033 10 9 tag 14330 10 10 raccoon 6034 16 16 procumbent 14331 9 9 russian 6035 131 127 wood_pulp 6036 5 5 lao 14332 9 9 exemplify 14333 5 5 south_dakota 6037 19 17 finnish 14334 13 13 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digest 14843 14 14 prosecute 6521 8 8 plaid 6522 6 6 chapter 6523 21 20 ammunition 14844 24 24 surprisingly 14845 10 10 violence 6525 70 69 yoke 14846 11 8 monopoly 6524 14 13 butt 14847 10 10 exudate 14849 12 12 stall 14848 8 8 schizophrenic 6526 5 5 immovable 14850 6 6 supervision 14851 18 18 myelin 14852 5 5 squirrel 14853 33 31 supplies 6527 114 114 folktale 6528 5 5 digested 6529 6 6 sense_of_humor 14854 10 10 effort 6530 225 219 piece 14855 419 405 taxodiaceae 14856 5 5 figurative 6531 14 14 zero 14857 56 50 life_style 6532 7 7 cowardly 6534 10 10 malpractice 6533 5 5 demonstration 14858 15 14 chiefly 6535 321 320 manufacture 6536 78 78 article_of_clothing 6537 5 5 onion 14859 85 83 mop 14860 12 10 stud 6538 11 9 acre 14861 5 5 phagocyte 6539 7 7 welding 14862 7 7 flightless_bird 6540 8 8 dusky 6541 9 9 ecology 14863 16 15 data 14864 161 151 north_american_indian 14865 51 51 agaric 14866 49 49 roughness 14867 9 9 vibration 14868 21 19 singles 14869 10 10 lather 14870 9 7 overuse 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flicker 6615 5 5 refrigerant 6616 5 5 ram 6617 10 10 carve 6618 11 11 intent 14950 48 48 marks 6619 49 49 extensive 6620 34 32 aphasia 14951 8 8 saltwater_fish 6621 7 7 derision 6622 5 5 sought 6623 21 21 polymeric 14952 6 6 investing 14953 5 5 irrelevant 6624 9 8 external 14954 102 99 northwest_passage 6625 5 5 credited 14955 8 8 material 14956 512 496 rosette 6626 27 27 gown 14957 18 18 gallon 14958 20 19 ripe 14959 38 37 mutation 6627 25 25 eagle 6628 27 27 weeping 14960 12 11 ceiling 6629 42 40 song 6630 141 136 martial 6631 8 8 cleft 6632 18 17 toothlike 14961 9 9 sporophore 14962 11 11 utopian 14963 6 4 detailed 6633 38 38 overflowing 14964 8 8 polynesia 14966 25 25 greenhouse 14965 9 9 arterial 14967 7 6 misleading 6634 22 21 desert 6635 96 93 every_other 14968 5 5 yen 6636 5 5 information 14969 347 337 prostate 14970 7 7 carthage 6637 9 8 flatfish 6638 16 16 secretaryship 14971 13 13 attentively 6639 7 7 equipped 6643 81 78 polygon 6642 28 28 wallet 6641 7 7 postal_service 6640 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6667 22 20 soviet 14998 42 41 robot 14999 12 12 blind 15000 43 40 utilitarian 15001 5 5 unstressed 15002 16 12 crankshaft 15003 5 5 strabismus 15004 7 7 thoroughly 6668 44 44 accomplishment 6670 18 18 shoulder_blade 6669 6 6 socialism 6671 16 16 ascent 15005 7 7 tomb 6672 14 14 obligated 15006 5 4 spinal_column 15007 10 10 anatomist 6673 9 9 orienting 15008 9 8 whats 15009 13 13 disturb 15010 17 17 ergot 6674 9 7 brine 15011 15 15 scorn 6677 12 12 ambush 6675 8 7 currant 15012 8 8 empirical 6676 10 7 bible 6678 42 42 hipbone 6679 8 8 weekly 15013 12 10 flowering 6681 81 80 chastity 6680 7 7 typhoid_fever 15014 7 7 grudge 6682 6 5 intention 15015 70 70 overlap 6683 15 15 metamorphic_rock 6684 12 12 judge 6685 110 106 west 6686 194 186 coronary_artery 15016 8 7 sheet_metal 6687 9 9 fortification 15017 27 27 forest_fire 15018 6 6 scarf 6688 20 20 nematoda 15019 5 5 fishing 15020 34 34 junction 6689 32 31 bay_of_biscay 15021 7 7 helper 15022 8 8 probably 15023 33 33 wholesale 15024 8 6 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odorous 15705 9 7 meteorological 15706 9 7 smack 7315 5 4 tile 15707 18 17 shared 7316 41 40 biggest 7317 8 8 lever 7318 30 30 sapphire 15708 6 5 new_south_wales 15709 6 6 induced 15710 43 42 coincide 15711 6 6 polynomial 15712 13 11 secretion 7319 68 66 slope 7321 65 63 unlawfully 7320 15 15 midday 15713 6 6 efficacious 7322 5 4 turn_out 7323 10 10 listening 7324 14 13 magistrate 7325 12 12 accelerated 7326 11 11 oxide 15714 57 57 fastidious 7327 11 10 definition 15715 22 22 choreographer 15716 19 19 spanish 15717 108 107 fairly 7328 22 22 pig 7329 40 40 inviolable 15718 5 4 belittle 7331 7 6 globule 7330 6 6 diagnosis 15719 33 32 arrival 15720 20 20 fork 7332 20 19 furtive 15721 5 5 barred 7333 13 13 roentgenographic 15722 6 6 in_kind 7334 9 9 incurable 15723 10 9 honey 15724 39 39 meaningful 7336 12 8 breathed 7335 7 7 tumor 15725 72 72 optic_nerve 15726 7 7 solder 7337 16 14 producer 15727 18 17 vegetable_matter 7338 7 7 serial 15728 11 11 according 15729 185 185 contributing 15730 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cassava 15755 5 5 somewhere 15756 25 24 cage 7364 20 19 arch 7365 78 77 meal 7367 137 134 date 7366 126 114 opening 7368 207 199 autumn 15757 49 47 remora 7369 5 5 marble 7370 21 21 edited 7371 5 5 forceps 15758 5 4 extend 15759 237 231 tested 7372 31 29 rewarding 7373 6 6 subject 7374 214 210 renovation 15760 5 5 wit 7375 24 24 preaching 15761 11 10 moment 15762 62 62 plumage 15763 60 60 hermit 15764 5 5 month 7376 192 162 maturity 7377 46 46 uncertain 15765 41 31 battery 7378 22 20 in_effect 15766 15 15 monotypic 15767 7 7 oriole 7379 7 7 theropod 15768 10 10 overhanging 7380 6 6 classical_greek 7381 11 11 afoot 7382 6 4 batter 15769 67 64 bombing 7383 13 13 oak 7384 45 44 starchy 15770 14 14 mistletoe 15771 5 5 isolated 15772 47 47 sofa 15773 23 23 model 15774 78 76 gemini 7385 5 5 tightened 15775 6 5 semitransparent 7386 5 5 stand 7387 147 143 unfairly 15776 5 5 addition 7388 60 55 charitable 15777 11 11 the_french 7389 104 101 leased 15778 6 6 veterinary_medicine 15780 8 8 bering_sea 15779 6 6 risk 7390 87 84 creativity 7391 8 8 amino 15781 24 22 abolition 15782 10 9 greet 7392 20 20 eyelike 15783 7 6 drama 15784 54 52 heat 7393 308 291 airy 7394 6 6 sebaceous 15785 8 8 inhibited 7395 8 7 enveloping 7396 9 9 unidentified 7397 8 8 italian 7398 158 156 similarly 7399 9 9 truffle 15786 7 7 devote 7400 10 10 apricot 15787 16 16 underbrush 7401 5 5 tease 7402 5 5 strictly 7403 25 25 inner 15788 82 80 intensely 15789 43 43 benefit 15790 98 95 pledge 7404 13 12 accessible 7405 22 21 bile 15791 19 18 feather_palm 15792 10 10 sooner 15793 5 4 silkworm 7406 7 7 explorer 7408 46 46 underfoot 7407 6 5 independent_variable 15794 7 7 proposition 15795 59 51 tasmania 7409 26 26 dull 7410 126 115 characterize 7411 15 15 gentian 15796 7 7 visit 15797 94 88 erroneous 15798 6 6 willful 15799 14 14 climate 7412 61 58 case 7413 250 241 permissible 15800 6 5 regime 15801 10 10 bluff 7414 12 11 warmed 15802 14 13 andes 7415 44 44 sensory_nerve 15803 7 7 pupa 7416 5 5 pianist 15804 36 36 observation 15805 72 69 clockwise 7417 9 8 rhine_river 7418 5 5 shoe 7419 68 67 regard_as 15806 5 5 surface 7421 814 790 attract 7420 85 83 pamphlet 7422 7 7 thicken 15807 8 8 crack 7423 44 44 lively 15808 55 53 veil 7425 14 14 smuts 7424 6 6 lima 15810 7 7 prove 7426 45 42 calcium 7427 56 53 fearless 15809 5 5 monotheistic 15811 6 6 rhythm 15812 46 46 referent 7428 9 9 masquerade 15813 7 6 caliber 7429 13 13 vitality 7430 36 36 claret 7431 7 6 embarrassed 15814 11 11 maliciously 15815 6 6 plight 7432 5 4 battered 7433 7 6 outer_space 15816 21 21 hymn 7434 19 18 new_jersey 7435 36 33 continuous 7436 82 78 puddle 7437 16 14 hood 15817 31 30 esp 15818 138 138 stabilizing 15819 5 5 fertilize 15820 10 10 lesser 15821 22 22 trace 15822 44 43 mayan 15823 8 8 extra 7438 50 48 touched 15824 31 30 carrier_wave 7439 7 7 executor 15825 7 7 cone 15827 82 81 fiery 15826 8 8 edema 15828 15 15 continent 7440 55 51 liable 7441 47 47 lobe 7442 76 71 prudence 15829 16 16 wisconsin 7443 19 17 cheap 7444 55 53 reddish_purple 15830 14 14 negociate 7445 6 6 wages 15831 50 49 baffling 15832 6 6 stared 7446 19 19 beads 7447 21 21 operation 15833 135 130 fatty_tissue 7448 5 5 alliteration 15834 5 5 hypertrophy 15835 6 6 truly 15836 11 11 prism 7449 6 6 trigonometry 15837 6 6 take_place 7450 11 11 impermeable 15838 7 5 postulate 15839 13 13 set_up 7451 46 44 yield 15840 177 172 locality 15841 10 10 clump 7452 35 35 variability 15842 5 5 dividing 15843 23 23 redemption 15844 9 9 innocence 15845 18 18 democratic_party 7453 7 7 active 15846 192 179 ethnic 15847 24 22 glorify 15848 7 6 buddhism 7454 17 17 accounting 7455 26 25 urgently 15849 9 9 disfavor 15850 6 6 scolopacidae 15851 8 8 columbus 7456 6 6 sash 15852 11 11 diplomatic 15853 20 20 vitamin 15854 37 34 mexico 7457 268 264 reservoir 7458 14 14 precedent 7459 12 12 willingly 7460 8 8 contrived 7461 12 12 human_beings 7462 63 63 twisting 7463 28 28 unattractive 15855 10 10 justification 15856 19 19 please 7464 123 122 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almond 7514 31 30 commission 7516 37 37 aristocracy 7515 14 13 assistance 15906 47 47 phage 7517 5 3 levy 7519 27 27 believing 7518 21 21 buoyancy 7520 5 5 library_science 7521 5 5 stops 15907 37 35 democrat 15908 8 8 acoustic_device 15909 8 8 record 15910 211 196 might 7522 56 56 injection 7523 38 38 phlegm 7524 9 9 sing 15912 53 52 fertile 15911 47 46 forbidding 15913 10 10 caesar 15914 6 6 theatrical_production 15915 9 8 feel 15916 141 138 eyeball 15917 27 27 hiding_place 7525 5 5 treat 15918 374 364 cousin 7526 21 21 corresponding 7527 49 49 mile 7528 111 110 gaul 15919 13 12 answering 7529 11 10 border 15920 128 122 challenging 7530 7 7 generalization 15921 9 9 distinguishable 15922 8 6 blouse 15924 18 18 tranquilizer 15923 16 16 moderate 7532 66 58 concession 7531 18 17 offhand 7533 7 5 prominence 15925 18 18 detonating 7534 5 5 navy 15926 24 24 nerve_fiber 7535 11 11 superficially 15927 22 22 scheme 7536 45 45 bound 7537 97 93 skill 7539 165 159 dazzled 7538 5 4 juniper 7540 10 9 reckoning 15928 5 5 leg 7541 333 328 juxtaposition 15929 7 7 suitcase 15930 11 11 seasoning 15931 52 52 seashore 7542 16 15 terror 15932 26 26 furry 7543 11 11 refinement 7544 22 22 work 15933 798 764 generous 7545 29 28 psychiatric 7546 5 5 tipped 7547 30 30 hardware 7548 10 10 wool 7549 77 74 humanistic 7550 9 8 to_date 15934 6 6 conducted 15935 44 44 linkage 15936 6 6 labor_leader 7551 13 13 bayonet 15937 5 5 postage 15938 6 6 shelled 15939 23 23 cast_off 7552 10 9 ambassador 7553 17 17 pro 7554 9 9 three 15940 503 468 possess 7555 67 66 hopeless 15941 16 14 radiating 7557 27 26 temporal_lobe 7556 8 8 april 7558 19 19 electron 7559 110 100 coordinate 15942 38 33 cite 7560 10 10 on_fire 7561 5 5 discoverer 7562 5 5 army 7563 115 112 fasten 7565 70 70 orifice 7564 6 6 mix 15943 28 27 unwillingness 15944 14 14 refract 15945 5 5 sadness 15946 33 33 complaint 7566 38 36 succulent 7568 65 64 mathematical 7567 62 59 wisdom 7569 32 31 spermatic_cord 15947 5 5 saturn 7570 6 6 flake 15948 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1 7015 7 7367 1 5262 2 758 1 13672 2 3539 1 2698 2 7017 1 759 1 15997 2 9285 1 8512 1 9153 1 8012 2 9021 1 5016 1 7240 1 7377 2 5648 1 10 91 9291 1 15267 3 14065 1 14323 1 8017 1 4650 1 16273 1 13434 1 1818 5 6535 3 6283 1 6906 1 6536 1 11443 1 3415 1 7384 1 11022 1 10362 1 17 1 9830 1 14859 1 906 1 13034 2 6777 1 3793 2 12902 1 6411 1 13037 1 9032 1 16280 2 9301 1 13439 1 9721 1 2062 6 7894 1 1546 1 4540 6 11451 1 4412 1 2211 3 4196 1 13576 1 7040 1 12246 3 3565 2 918 1 7257 3 6421 1 1409 4 4909 2 5662 7 15548 1 9444 1 9971 1 2218 1 7904 2 15555 1 1055 1 7393 2 9843 1 3699 2 13581 1 13706 1 5183 2 12521 1 3436 1 13953 1 5555 1 13054 1 10247 1 12011 1 14087 1 4093 3 11326 1 12786 2 1836 1 14609 1 13853 2 3439 3 12395 1 1301 1 15171 2 7537 1 7913 1 293 4 7277 2 7667 1 13712 1 2873 4 11593 1 16045 2 12270 1 4432 1 16416 1 6557 1 14612 2 12274 1 14352 1 12147 2 12531 1 13720 1 15933 1 5298 2 9064 1 13204 1 2089 1 447 2 9860 1 10131 1 6178 10 13965 2 6801 1 11474 1 179 3 6940 1 13968 1 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11951 1 12866 1 10742 1 8869 2 9542 1 11133 4 8613 1 13404 1 10461 2 7224 1 3378 7 3764 1 16254 1 7006 1 7869 1 1932 1 7495 1 9142 2 4041 1 7230 4 16492 1 4169 1 6890 1 16258 1 10340 1 1789 1 7746 3 10079 1 6389 1 10602 1 15752 2 15753 2 6520 1 1019 1 1656 1 5140 3 urban_area 10403 1 10492 1 14855 1 6713 1 12284 1 16100 1 13569 1 8652 1 9764 1 15717 1 5228 2 5362 1 1770 1 11675 1 11477 1 14838 1 10325 1 3492 1 779 1 7012 1 15933 1 11605 1 3320 1 13434 1 15340 1 12491 1 descend 11431 1 1660 2 14707 1 4893 1 15636 1 14319 1 2701 1 2190 1 11147 3 6651 1 5776 1 4185 1 6400 1 5161 1 14461 2 11 42 2843 1 3788 1 768 1 3789 1 641 1 8021 3 1667 2 6146 1 5783 1 13171 1 4070 1 11444 1 905 1 400 1 12643 1 7888 1 16393 1 8899 1 6289 1 7389 1 277 1 4907 2 9723 1 5913 1 16170 1 10909 2 14993 1 7526 1 7257 1 2857 1 6423 4 4079 1 11174 1 14221 1 158 1 1291 1 13048 1 8046 1 1055 1 31 1 14873 1 1687 1 421 1 7398 1 8302 1 9184 1 12393 1 9186 2 15167 1 10503 1 5187 1 5412 1 9454 1 670 1 13583 1 13063 1 293 1 16300 2 13712 2 12270 1 15797 1 4308 1 13462 1 7413 1 4215 1 11327 1 14236 1 13465 2 12020 1 11871 1 13722 1 15301 1 13965 1 7169 1 1576 1 7421 1 15023 1 3958 2 3822 1 6568 1 9205 1 6319 1 1441 1 14626 1 14368 1 5688 1 1716 1 15028 1 15940 1 15811 1 12540 1 14376 1 11895 1 12415 1 5694 1 1194 1 5957 1 13872 1 15947 1 573 3 458 1 704 1 6824 1 12815 1 10829 1 10950 1 15695 1 3477 1 3320 1 15047 1 11215 1 4716 1 10421 1 3992 1 3483 1 11084 1 15959 1 5849 1 5850 2 7314 1 10701 1 6836 1 10965 1 15591 2 4004 1 16092 1 7585 1 10167 1 15486 1 1216 1 5598 1 11227 1 9518 1 7321 1 590 2 15966 1 5977 1 483 1 4861 1 14010 1 15717 1 13893 1 14279 1 6232 1 12068 3 11119 1 5234 1 5610 1 9922 1 1755 1 7970 1 6612 1 2655 1 2523 1 15089 1 7210 1 9393 2 10863 1 9797 1 9261 1 9657 1 5990 1 12726 1 4617 1 5128 1 4160 1 13784 1 8991 1 9532 1 6498 1 13260 3 13261 1 12596 2 13648 1 1242 1 12213 2 2815 1 8128 1 7486 1 12863 1 2036 1 6376 1 5883 1 12731 1 4030 1 2310 1 3524 1 1924 2 14691 1 6628 1 11133 1 2423 2 12092 1 1510 1 13406 1 8874 1 8742 1 1014 2 4887 1 10340 1 6890 1 12745 2 8145 1 7010 1 141 1 16494 1 7012 2 10756 1 4634 2 14047 1 9277 2 rout_out 2214 1 5058 1 13381 1 1501 1 appointee 7566 1 9528 1 13076 1 13843 1 5271 1 4450 1 15442 1 9203 2 1526 1 2755 1 8010 1 12215 1 399 1 civility 10415 1 8799 1 11910 1 12643 1 1738 1 13000 1 12623 1 3063 1 7198 1 uo2 10906 1 6563 1 16438 1 121 1 batidaceae 13216 2 6977 1 4711 1 8977 1 6681 1 6498 1 2131 1 6600 1 6516 1 11414 2 409 1 9880 1 cerebrum 3113 1 3367 1 12733 1 8172 1 15378 1 2896 3 10295 1 3065 2 6527 1 9285 1 2118 1 3987 1 13228 1 14421 1 2977 2 7473 1 3580 2 7442 1 15682 1 1459 1 3084 1 1953 1 7256 1 9639 1 11126 1 3460 2 16478 2 366 1 14008 1 16008 1 7556 1 7882 1 5727 1 7514 1 12 11 12308 1 11895 1 7747 1 11282 1 4707 1 14203 2 10656 1 14893 1 14047 1 1484 1 8762 1 11474 1 shorthand 5841 1 3404 1 5311 1 3483 1 16356 1 14222 1 9123 1 11433 1 9506 1 5210 1 1996 1 1054 1 13924 1 3025 1 12155 1 12480 1 7539 2 4328 1 gloves 1211 1 12718 1 5733 1 1370 1 10165 1 10965 1 12231 3 12602 1 6857 1 8582 1 2282 1 2724 1 5999 1 8869 1 1639 1 9886 1 7210 1 532 1 7505 1 2680 1 6248 1 10846 2 13526 1 8322 1 3875 1 1086 1 10779 1 2836 1 12033 1 2960 1 15940 1 8292 1 2915 1 11002 2 4132 1 7230 1 5707 1 1501 3 14352 2 15958 1 16008 1 9416 1 4361 1 14632 1 1180 1 3643 1 13 13 15933 1 12978 2 13381 1 2335 1 1108 1 9918 1 598 1 6375 2 make_full 12795 1 2953 1 10970 2 3970 1 15877 1 9326 1 9743 1 9618 1 genus_oenothera 4730 1 6681 1 2436 1 12011 1 petrifaction 1180 1 8292 1 4451 1 12720 1 1088 1 10247 1 5505 1 14557 1 4651 1 medical_scientist 5131 1 8309 1 2549 1 8603 1 3133 2 5412 1 1169 1 zoroastrian 15811 1 421 2 3992 1 11 1 9186 1 357 1 10514 1 2326 1 11147 2 7012 1 2423 1 7490 1 12213 2 15301 1 5977 2 a_lot 11966 1 6011 1 15126 1 6897 1 4774 1 9819 2 12231 1 4049 2 1024 2 3242 1 9423 1 11552 1 1939 1 12623 1 1145 1 2698 1 13820 1 13674 1 9821 1 13422 1 7880 1 11819 1 7374 1 3676 1 6134 1 7239 1 6274 3 5379 1 11437 2 11821 1 14458 1 6655 1 2195 1 2960 1 2705 1 9822 2 4061 1 9429 1 4529 2 1030 1 6530 2 7376 1 9021 1 1807 1 4530 1 9704 1 16008 1 2563 1 6279 1 8281 1 8282 2 12898 1 8757 1 15536 1 5528 1 4401 1 2443 1 9826 1 267 1 8523 1 14587 1 15 67 13171 1 13829 1 6283 1 9574 1 399 2 9830 3 4535 2 2447 1 10492 1 10363 1 5785 1 15774 1 5172 1 3556 1 4537 1 9298 1 10493 1 8029 1 11308 1 8531 1 6909 1 11990 1 4539 1 3116 1 779 1 5027 1 780 1 14864 2 8645 1 5913 1 12131 1 8769 2 7390 2 12776 1 8292 1 14077 1 10234 1 9305 2 11999 5 14600 1 2579 1 15419 1 5179 1 278 1 14337 1 7649 1 12002 1 2581 1 2214 5 4912 2 10238 1 13844 1 1413 1 10375 1 7260 5 7653 1 1054 1 7393 1 5180 1 6791 1 4549 1 6793 1 9731 1 11322 1 663 1 13315 1 424 1 8784 2 10247 1 9735 1 10503 3 3133 1 12011 2 5412 1 8787 1 12525 1 15170 1 6167 7 2865 1 11728 1 2598 1 8309 1 7539 2 9596 1 13461 1 14884 1 13194 1 8055 1 9854 1 3582 3 7665 1 2873 3 6305 1 7667 2 10253 1 9056 1 15797 1 4214 1 12669 1 1699 1 12397 1 8314 1 11327 1 5810 1 1309 2 16051 2 13718 1 4928 1 14236 2 1972 1 14237 1 15570 2 2231 1 14617 1 8435 1 15933 11 12795 1 10392 1 15445 1 2089 4 8436 1 15572 2 10807 1 9863 2 5945 1 2607 1 13209 1 7802 1 9067 2 11602 1 11735 1 10265 4 13968 2 944 1 2474 1 4110 1 13214 1 6947 1 12536 1 12942 1 7555 1 8325 1 5311 1 15459 1 3967 1 16312 1 10525 2 13606 2 13736 1 7687 1 3466 1 8813 2 3605 1 13342 1 5065 1 7563 1 9083 2 1450 2 4707 2 12952 1 4118 1 8946 1 8197 1 13981 1 6071 1 5317 2 7297 1 10532 1 11612 1 10415 3 7566 1 3309 1 9881 1 573 1 6203 1 9359 1 6958 1 4335 1 3608 1 9497 1 13743 1 9886 1 11906 1 12041 1 6080 7 13490 1 9227 1 5448 1 10153 1 5964 1 5965 1 8084 1 2490 1 10421 1 2263 1 6713 1 15831 1 14515 1 3992 1 2001 1 3995 1 12304 1 12563 1 4595 1 15590 1 1595 1 5458 1 12175 1 11090 1 8209 1 13100 1 4001 1 6596 1 14658 1 1596 2 12569 1 1209 1 10701 1 11769 1 14271 1 9768 1 7450 1 15839 2 8093 5 15479 1 4140 1 11636 1 8582 2 8834 1 4731 1 7585 2 11921 2 8361 1 6473 1 4143 1 9775 2 719 1 8467 1 7956 2 14525 1 13241 1 15965 1 14276 1 7324 1 6102 2 6230 3 484 10 15853 1 8472 1 8223 1 11110 1 8980 1 12448 2 6980 1 9912 2 1607 4 3862 2 15077 1 1749 1 10570 1 10977 1 13380 1 14147 1 9915 1 11383 1 9107 1 15225 2 4365 2 4017 1 5733 1 219 1 2019 1 6984 2 3202 1 9390 1 4156 1 2525 1 8853 1 2802 1 4262 1 16117 1 14677 1 14026 1 4490 1 116 1 14154 1 10863 1 1908 1 11123 1 12590 1 3875 1 5240 3 12725 1 10866 2 11400 1 8382 1 15864 1 1619 1 1501 2 12591 1 6497 2 6498 1 1621 1 12856 1 13912 1 16356 2 11662 1 11406 1 13648 1 15512 1 996 2 3881 1 231 1 6747 1 126 4 3646 2 10067 1 8864 1 12601 1 15371 1 14956 1 14687 1 5883 1 11671 1 15987 2 3218 2 12218 1 621 2 1924 1 235 1 13797 1 15110 1 4761 2 8137 2 1784 1 9937 2 14693 1 876 1 3082 2 623 2 15114 1 3375 3 3223 1 10078 1 5888 1 12346 1 7114 2 510 1 2175 1 7496 1 2945 1 11002 3 10465 2 13277 1 10211 2 514 1 10600 2 8499 1 10340 3 14838 1 7872 1 1517 1 7011 1 11419 1 14969 1 8393 1 11282 3 12102 1 12875 1 13411 1 6520 3 12352 1 12620 1 6639 1 6641 1 9277 1 7014 1 11287 1 6264 2 16144 1 batting 12229 1 2910 1 11100 1 10556 2 4537 1 2802 1 5173 1 9323 1 1836 1 11147 1 14626 1 6398 1 12301 1 11821 1 6123 2 2915 1 8108 1 12973 1 13785 1 4358 1 2214 1 6760 1 11662 1 4803 3 8393 2 12874 2 5745 1 16 8 11666 1 556 1 7014 1 butterfly_fish system_of_rules 1406 1 8093 1 993 1 1014 1 1024 1 5534 2 4537 1 12856 1 12556 1 5918 1 9787 1 8385 1 15933 1 13411 1 144 1 5913 1 12131 1 in_brief 571 1 9209 1 9899 1 15824 1 15591 1 pawn 5183 1 1224 1 15559 1 4710 1 9819 1 252 1 13054 1 15230 2 704 2 5187 2 11212 2 5485 1 12145 2 9697 1 3874 1 7697 1 15975 1 13063 1 2960 1 14855 2 3136 1 2440 1 10 1 9532 1 6871 1 11662 1 15015 1 6145 1 13432 1 2089 1 12998 4 12088 1 2394 1 10428 1 14270 1 17 9 4659 1 15772 1 15591 1 12181 1 13404 2 585 2 876 1 14905 1 12407 1 12609 3 16028 1 7006 1 6123 2 15940 1 3342 1 13634 1 12744 1 2246 3 6230 4 6421 1 11646 1 8942 1 2581 1 9971 3 12255 1 11278 1 14282 1 12543 2 14776 1 12352 1 2922 1 merchant 7015 1 7068 1 3388 1 4774 1 6266 1 9423 2 14975 1 4180 2 7940 1 13352 1 7302 1 6080 2 6527 1 13745 4 13553 1 8750 1 6830 5 2437 1 5380 1 2263 1 5648 1 3483 2 5214 2 13096 1 579 2 12569 1 1596 1 7197 1 15538 1 9768 1 6836 1 13625 1 905 1 8093 1 12643 1 10363 1 18 35 3556 1 13367 1 9716 2 8582 1 7141 2 717 1 8468 2 15339 1 4009 1 2728 1 14941 1 14525 1 11925 1 8587 1 8040 1 11718 1 11034 1 5978 1 8223 2 5228 1 11845 1 9914 2 14528 1 15968 1 5796 1 14225 1 15225 1 9448 1 12323 3 2073 1 8228 1 11789 2 14339 1 1563 1 10856 1 11939 1 13312 1 7398 1 5184 1 1224 1 219 1 12077 2 15926 1 14948 1 12848 1 4370 1 14155 1 10727 1 7099 2 7539 1 13063 1 2079 1 12464 1 13712 1 6925 1 9056 1 3283 1 1364 1 10255 1 8796 1 10732 1 7983 1 4437 1 794 1 15933 1 6749 2 11533 1 13264 2 1976 1 14956 1 1635 1 11735 7 14248 2 14626 1 2039 1 11339 1 9748 2 16310 1 3659 1 8558 1 1014 2 5311 1 1383 1 7619 1 4323 2 9874 1 10755 1 5512 1 14969 1 15251 1 12875 2 1656 2 1195 1 16318 1 8332 1 drying_oil 5928 1 15267 1 1357 1 4035 2 10131 1 7169 1 16092 1 5873 1 7765 1 6172 1 evocation 11749 1 585 1 8582 1 9176 1 2870 1 13169 1 2950 1 2488 1 satisfying 10415 1 15228 1 8726 1 4164 2 13209 2 3388 1 7367 1 14051 1 4141 1 14555 1 9717 1 12286 1 19 11 4616 1 6663 1 15615 1 12041 1 945 1 8137 1 6601 1 3652 1 4110 1 1997 1 8571 1 4009 1 10727 1 7426 2 2705 1 4307 2 763 1 14387 2 2320 1 14600 1 3679 1 5914 1 7649 1 15279 1 2083 1 2214 1 1841 1 5271 1 14585 1 14225 1 13648 1 8163 1 4990 1 1292 1 680 1 12570 1 8128 1 4495 1 6161 1 4656 1 9920 1 samoyedic 3815 1 11147 1 13569 1 8512 1 7634 2 349 1 14056 1 testicular_cancer 4634 1 12416 1 crest 14111 1 4332 1 1660 1 5897 1 12231 1 8153 1 4179 1 1523 1 13821 2 11212 1 7237 1 2436 1 2190 2 14712 1 10418 1 15763 2 4955 1 5455 1 5901 1 9704 1 13096 1 12503 1 13359 1 7380 1 8021 1 1596 1 12827 1 2277 1 2906 1 906 1 10363 1 3854 2 15480 1 9635 4 5718 1 3258 2 13834 1 6540 1 4143 5 5598 1 20 26 13836 1 8767 1 2063 1 2212 1 6785 1 589 1 9172 1 9103 2 8108 1 13118 1 10175 1 7590 1 12712 1 2215 1 6849 1 14021 1 12519 1 15353 1 11119 1 5285 1 10983 1 5610 1 3636 5 10310 1 10790 1 9115 1 1691 1 14533 1 15231 1 11726 2 4263 1 12786 1 9116 1 12852 1 16411 1 13583 4 2466 2 293 1 2026 1 15797 1 6437 1 9532 1 10511 1 6997 2 14235 1 227 3 10324 1 441 1 3760 1 16127 4 16484 1 12280 1 10259 1 13591 1 54 1 2232 1 6801 2 7350 2 4030 1 299 1 6502 1 1924 2 5049 1 13796 1 10333 1 3013 1 806 2 9349 1 12092 2 12939 1 4446 1 9206 1 953 1 4829 2 308 1 9210 1 812 1 1134 1 185 1 4169 1 7360 1 8942 1 3532 1 16207 1 14634 1 16314 1 5002 1 1138 1 15579 2 9216 1 11346 1 11612 1 sax 6786 1 5128 1 5648 1 4169 1 8965 2 11349 1 10380 1 11156 1 13579 1 7927 1 16315 1 5390 1 6596 2 insatiably 2945 1 12410 1 9965 1 13552 1 12672 1 12065 2 3690 1 8209 1 10097 1 8615 1 10507 1 3719 1 10617 1 9276 1 13482 1 11339 1 2296 1 siege 4455 1 5443 1 3388 1 8334 1 3350 2 9819 3 758 1 14507 1 12231 1 190 1 12524 1 2990 1 9390 1 3164 1 8156 1 6825 2 12011 1 2895 1 13422 1 1836 1 8881 1 11796 4 6171 2 5380 2 990 1 5646 1 4158 2 3678 2 4131 1 14650 1 7789 1 3483 1 9705 1 12562 1 13260 1 11557 2 8208 1 15138 1 12795 1 5298 1 5425 1 13031 1 15445 1 8551 1 7486 1 6801 2 4599 1 233 1 12829 1 3329 1 12283 1 13035 2 12284 2 1370 1 5597 1 8026 1 1924 1 8254 1 8529 1 16489 1 5908 1 13404 1 6035 1 10077 1 21 17 8072 2 9746 1 2504 1 11605 3 10335 1 3152 1 5334 1 7389 2 12443 1 1646 1 13008 1 12805 1 9075 1 4829 1 11925 1 15940 1 6230 1 14336 1 14277 3 7178 1 15459 1 11512 2 5228 1 3126 1 8942 2 2214 1 14106 2 2485 2 5065 1 8912 1 4633 1 12102 1 14504 1 2918 1 feline 421 1 11041 1 5555 1 10965 1 13300 1 1756 1 9635 1 8595 1 5718 1 14194 1 13821 1 12318 1 8119 3 2190 1 11147 1 3421 1 16299 3 7574 1 22 8 15651 1 13583 2 1510 1 2560 2 5361 1 3501 2 11622 1 13867 1 16217 1 9210 1 10956 2 4957 3 10871 2 14997 1 11498 1 14311 1 8089 1 10925 1 12068 1 11964 1 6801 1 snoopy 10492 1 8410 2 5417 1 8612 2 7191 1 1257 1 12011 1 10570 1 14184 1 7967 1 11572 1 arcadian 2280 1 10212 1 13674 2 10325 2 12838 1 16495 1 9169 1 15413 1 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8070 1 14610 1 11716 1 3406 1 2263 1 3601 1 8059 1 8875 1 87 1 1666 1 1290 1 14428 1 7796 1 4189 1 10425 1 8063 2 enameled 2935 1 9521 3 2840 1 4393 3 8108 1 12669 1 11746 3 3371 3 7360 3 6287 1 10559 1 449 11 1180 1 12785 1 15973 3 6035 1 16495 3 7697 3 3875 3 5642 1 9206 3 magnoliidae 15940 1 11614 1 14714 1 5705 1 4933 1 4491 1 15241 1 2718 1 9667 1 10800 1 15267 1 6498 1 4179 1 6890 1 6278 1 6600 1 12041 1 3135 1 13878 1 2797 1 13382 1 1510 1 powhatan 2817 1 2509 2 9291 2 12040 1 8517 2 10237 2 12713 1 13674 2 3128 1 7362 2 2608 2 5528 2 13048 1 14941 1 6652 2 1685 1 5380 2 intelligence 6011 3 3241 1 13668 2 1024 1 3242 1 6524 1 6525 1 2697 1 1145 1 13160 1 14318 1 14851 1 12884 1 13677 1 4523 1 11819 1 8881 1 11820 1 4527 1 15637 1 10617 1 2960 3 6530 1 1030 1 8012 1 10483 4 13931 1 6279 1 13169 2 14066 1 12898 1 11826 1 7135 1 9826 6 1667 1 11017 2 4534 1 11831 3 1669 1 9830 1 1406 1 16016 1 906 1 12643 1 9964 1 13299 1 1279 2 11709 1 8528 2 7522 2 6286 1 1282 1 11838 1 1949 1 16395 1 10778 1 779 1 9038 1 8903 5 16169 1 5541 1 13576 3 11312 1 6419 3 8769 4 16398 1 7774 1 2068 3 13698 1 278 1 2858 1 7649 4 659 1 1169 1 14999 1 5918 2 8180 1 9587 1 16177 2 10376 1 15658 1 12260 1 15784 1 924 1 8779 1 12519 2 9320 1 13848 1 4087 1 5181 1 2349 2 11182 1 13454 1 5283 1 7532 1 8426 1 5038 1 5924 1 8048 1 7267 1 10247 1 7049 4 7154 1 5926 1 1569 1 2598 1 15436 1 7536 1 10252 1 13583 5 7539 1 1968 1 2743 3 7410 1 11188 1 15565 1 14610 1 3951 1 10253 2 12929 1 4680 2 673 1 3138 1 14614 1 9601 1 2874 1 4562 1 4928 3 15015 2 16052 1 3816 1 12795 6 12020 1 7799 1 15572 1 10131 1 6444 1 5427 1 10395 1 13331 1 11875 1 9340 1 13209 7 300 3 3145 3 14097 1 10265 1 15935 1 14904 1 2240 2 450 75 11739 2 14768 1 14495 2 8323 2 14626 2 10005 1 12940 1 2881 1 12034 2 2759 1 5311 1 15459 1 11201 2 13341 1 5063 1 15312 1 12035 1 3158 1 6330 1 8449 1 4117 1 10144 1 12416 1 4707 1 13608 1 12953 1 8946 1 8197 1 16209 1 14638 1 5957 3 1330 1 3308 1 5443 15 15318 1 15692 1 6957 3 6958 3 326 1 2114 2 1868 1 12556 1 12041 1 9886 1 16450 1 14116 4 13490 2 13745 1 2118 2 2627 1 8202 1 3320 1 10418 1 3173 2 4338 2 5321 1 3480 2 11081 1 12698 1 1201 1 8458 1 2629 1 12171 1 2266 1 8575 1 11913 3 1875 1 3992 1 9764 1 5214 1 7191 1 15590 2 12052 1 8088 1 14652 1 963 1 1595 3 13751 1 8209 1 338 1 8970 1 2003 1 3036 1 13502 1 2635 1 12312 3 9767 1 8093 1 11367 1 3498 1 5331 1 8833 1 8582 1 8834 1 12439 1 3335 1 10294 1 2131 2 3189 1 16222 1 2132 1 15846 1 13239 1 15847 1 13892 2 4149 1 5226 1 6230 1 10564 3 14009 1 484 1 10439 1 13512 1 8223 1 13761 1 1893 1 16104 1 11784 2 9783 1 9914 1 14282 1 14147 1 8984 1 14018 1 7596 2 9920 1 10577 1 3350 1 1224 1 6854 1 4017 1 12586 3 4155 1 6859 1 8853 3 16116 1 7210 1 12848 1 16119 2 6735 1 3874 2 10867 1 11400 1 5619 1 12466 1 5128 1 1364 1 3359 2 16481 1 15098 1 11403 1 9928 2 1365 1 6241 1 14821 1 12856 1 616 1 9400 1 5498 1 2155 1 13648 2 15105 1 2416 1 7484 1 126 1 12998 1 12727 1 504 1 1779 1 11534 3 10738 1 12090 1 14956 1 1004 1 15987 1 14555 1 6505 1 7990 1 8134 1 15744 1 622 1 5632 2 14693 2 10077 3 751 3 7004 2 4881 1 3222 1 3527 1 2822 2 510 1 3896 1 1647 1 2175 2 2945 2 14041 1 4041 2 1381 1 13150 1 3087 1 4169 2 13280 1 8501 1 14838 5 139 1 6634 1 6760 1 1136 2 7122 1 14969 1 6263 2 11686 1 12353 1 1389 1 2826 1 16144 1 invalidate 7858 1 11761 1 13118 1 12620 1 8829 1 3766 1 shaky 1331 1 6896 1 7365 1 14532 1 16112 1 12139 1 4335 1 10116 1 9259 1 4588 1 3676 1 14883 1 7942 2 12268 1 6708 1 15901 1 6110 1 7541 1 6770 1 5648 1 676 1 6499 1 10672 1 12567 1 12795 1 8579 1 1818 1 10492 1 400 1 557 1 16457 1 10965 1 1370 1 11604 1 6351 1 14864 1 7956 1 451 7 4502 1 14626 1 16435 1 15916 2 15244 1 11108 1 15029 2 6230 1 3741 2 16258 1 10442 1 6330 1 7045 1 9490 1 13451 1 9148 1 2381 1 13812 1 3536 1 uterine 12297 1 2017 1 15725 1 7368 1 12231 1 8822 1 12075 1 2862 1 1836 1 7302 1 5615 1 4951 1 8456 2 7473 2 11080 3 15472 1 13855 3 16327 2 9599 1 2329 1 1178 2 14980 6 16328 2 15235 1 14352 1 8755 1 12856 1 7882 1 14464 1 6467 1 1067 1 5681 2 13099 1 12020 1 11872 1 6316 1 10131 1 5299 1 270 1 9369 1 127 1 1406 1 14096 1 532 1 9543 3 1602 1 11739 1 2641 1 13836 1 9546 1 16096 1 452 15 11712 2 13576 2 4883 1 7497 2 5475 1 8110 3 12539 1 6979 4 6890 3 7872 1 5033 1 1169 1 9353 2 4300 1 15719 1 12102 2 12416 1 5766 1 3435 1 dynapen 15918 1 5878 1 16026 1 15735 1 11160 1 16236 1 inhomogeneous 1595 1 female_horse 2190 1 7230 1 9268 1 8325 1 6888 1 2120 1 8953 1 12578 1 manual_dexterity 8962 1 8292 1 9116 1 7293 1 3461 2 1370 1 naval_gun 3815 1 213 1 4073 1 3118 1 6830 1 semiconductor_device 14658 1 4790 1 9872 1 5584 1 1562 1 10867 1 4534 1 5896 1 7233 1 15470 2 14956 1 medical_diagnosis 4529 1 453 6 3241 1 4041 2 12732 1 2675 1 6203 1 7649 1 13968 1 265 1 3133 1 16050 1 12898 1 4912 1 3035 2 6261 2 6020 1 5634 1 12020 1 10772 1 7623 1 semiautomatic 9953 1 mistily 10492 1 5455 1 15030 1 2546 1 5049 1 2613 1 9425 1 7932 1 5403 1 actuarial_table 10867 1 2514 1 15114 1 14009 1 10978 1 8325 1 12383 1 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3549 2 1595 1 4928 2 12597 1 16484 2 2634 2 13881 1 6375 2 11474 1 4464 1 15541 1 12472 1 11709 1 8582 1 7142 1 16092 1 3258 4 10137 1 4193 1 4143 1 62 2 10076 1 11742 1 10206 1 14666 1 10301 1 8036 1 5508 1 3766 1 536 1 11843 2 4200 1 15686 1 6230 1 14009 1 7360 2 454 15 8180 1 11648 1 8778 1 14186 1 5694 1 15225 1 16178 1 11241 1 9109 1 11584 2 7722 1 thrusting 4659 1 8592 1 3271 1 11307 1 11602 2 15742 1 11674 1 8853 1 1300 2 4588 1 10475 1 3398 1 4813 1 13656 1 3875 1 9437 1 15342 1 7541 1 13545 1 8292 1 436 1 4955 1 10751 1 6592 1 8205 1 15958 1 11513 1 10870 1 8346 1 14128 1 7762 1 6499 2 455 10 5500 3 4300 1 2306 1 4065 1 6933 1 999 1 10880 1 16318 1 14506 1 rhapsodize unsubdued 4158 1 9470 1 9561 1 16030 1 1633 1 8339 1 congratulate 3912 1 4680 1 2179 1 6257 1 3285 1 6511 1 6343 1 4887 1 5072 1 12468 1 12556 1 13012 1 15551 1 14105 1 1466 1 8970 1 11282 1 2171 1 379 1 13576 2 12228 1 5957 2 sputter 7421 1 10126 1 10453 1 15875 1 10816 2 4446 1 16263 1 3223 1 carriage 1198 3 15817 3 758 1 11209 4 2897 1 12167 1 15759 1 6080 1 3676 4 3320 2 8340 1 5965 1 4646 5 10281 1 5455 1 1807 1 8632 1 834 1 11630 1 3616 1 8971 2 13432 1 13171 1 268 1 4464 1 11701 3 2906 1 1211 1 12645 4 15591 22 2500 1 12315 2 530 2 4143 1 6035 1 4853 2 9518 4 11450 1 4540 1 3739 1 16096 1 971 2 14005 1 14525 2 7039 1 536 2 12578 8 12377 1 2857 1 11110 1 7900 1 9644 1 4863 1 15423 1 1957 1 1482 4 13895 15 16470 1 11517 1 8726 1 421 1 5038 1 13129 1 12460 1 7158 1 14154 1 13461 1 3875 2 5873 2 16237 4 6172 1 5415 1 11462 1 3136 1 13530 1 4376 1 4099 1 12672 1 13066 1 5500 2 12795 1 16127 2 173 2 9860 3 3520 1 10131 1 9470 1 1125 1 1435 1 15241 1 12473 1 12342 2 872 2 13405 1 1712 1 306 1 1716 4 877 1 8193 2 15940 1 10751 2 13076 2 2547 2 5362 1 6890 2 6697 1 7361 1 13737 1 16494 2 11279 1 4511 1 456 47 14047 1 11612 1 airlift 7256 1 14071 1 5336 1 9021 1 5785 1 1014 1 10824 1 11223 1 2581 1 7405 1 15423 1 3788 1 6035 1 11391 1 13430 1 13745 1 15601 1 8202 1 13434 1 1596 2 8408 2 9457 1 5056 1 7067 1 8715 2 back_burner 15374 1 14428 1 4930 1 7413 1 propitiate 13473 1 906 1 11512 1 6984 1 14838 1 13552 1 934 1 13674 1 1216 1 10846 1 14635 1 11612 1 536 1 1982 1 contagion 9805 1 4925 1 14943 1 4706 1 16026 1 984 1 2950 1 2488 1 creakily 15968 1 9971 1 7239 1 bilaterally 12522 1 10535 1 9524 1 10579 1 12326 1 4352 1 15330 1 227 1 15554 1 15933 1 14175 1 aerobic 457 16 6583 2 3436 1 11431 1 10149 1 7754 1 4260 1 8595 1 4263 1 10541 1 6828 1 14712 1 12959 2 8512 1 5449 1 8400 1 2705 1 14386 1 3280 1 12236 1 6494 1 3071 2 10421 1 2534 2 438 2 11327 1 3916 1 8435 2 5849 2 14 1 12020 2 5088 1 11474 1 9404 2 4933 5 4293 1 3258 1 11541 1 2755 1 14077 1 2980 1 8939 1 16438 1 3744 2 12614 1 12809 1 10341 1 12543 1 15191 2 1055 1 14018 2 5797 1 13223 1 11852 1 3571 7 14972 1 16372 1 6894 1 11854 1 samuel 5516 1 5517 1 15241 1 1755 1 13626 1 328 1 1490 1 10561 1 12220 1 5210 1 276 1 6099 1 9548 3 8401 1 8769 1 6925 1 11925 1 1826 1 3531 1 7230 1 10255 1 11137 2 6498 1 14374 1 16174 1 10875 1 4912 1 594 1 11686 1 13434 1 4425 1 reburied 14548 1 7372 1 14010 1 11949 1 15822 1 4764 1 8942 1 6655 1 stubborn 11289 1 14974 2 10535 1 12586 1 458 16 10503 1 39 1 15950 1 15089 1 16410 1 4893 1 12848 1 9150 1 5288 2 3135 1 15198 2 14783 3 6081 1 9285 1 7239 2 8512 1 14884 1 5190 1 5990 1 4211 1 14458 1 11081 1 4158 1 5739 1 13587 1 9021 1 7413 3 11 1 3409 1 14463 1 13066 1 5628 1 10329 2 4534 3 9094 1 869 1 9830 2 13592 1 12731 1 3145 2 8834 1 2337 2 6181 1 7835 2 7587 1 16026 1 9546 1 1130 1 13598 1 5888 1 5176 2 1443 1 15918 2 6230 1 14010 1 13512 1 8223 2 12540 2 11545 1 6890 1 14838 1 10177 1 6818 1 13765 1 4705 1 12068 1 4707 1 4546 1 16178 1 11788 1 6546 1 3238 2 9845 1 steal 420 1 13772 1 6525 2 459 20 8787 1 15089 2 1836 1 15897 1 13745 2 3320 1 10153 2 7759 1 15975 3 5873 1 6172 1 8312 1 8431 1 14950 3 892 2 14820 1 6090 1 3617 1 4064 1 15207 1 2846 1 12020 1 14828 1 3790 1 13994 1 15907 1 1669 2 12029 1 10705 1 6318 2 13799 1 5974 1 3189 1 8710 1 15486 1 62 1 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11739 1 948 1 16196 1 8739 1 15880 1 10206 1 12939 1 9546 2 14626 1 12943 1 9415 1 1013 1 4886 1 16438 1 13150 1 16492 1 6888 2 8687 1 15460 1 15311 1 11343 1 16494 1 11684 1 14251 1 5829 1 15251 1 3534 1 3386 2 5766 1 13665 2 9491 1 13223 3 13482 1 12228 1 6894 3 9493 1 vasoconstrictor 11183 1 1331 2 15704 1 3367 2 7049 1 6318 1 4303 1 531 1 14243 1 13675 1 15636 1 624 1 11105 1 11677 1 10300 1 5878 2 15918 4 5706 1 812 3 4228 1 9007 1 10389 1 13096 1 2582 1 895 1 615 7 13514 3 10033 1 14251 1 15251 1 1916 1 5694 1 5766 1 7393 1 2816 1 5180 1 14636 1 13264 1 sufficiency 4466 1 4164 1 906 1 8169 1 16182 1 13997 1 252 1 7488 1 7851 1 4616 1 13674 1 15089 1 15636 1 450 1 7757 1 9348 1 5963 1 14495 3 13239 1 6238 1 14941 1 8192 1 576 1 11188 1 7494 1 1499 1 4680 1 5778 1 14078 1 4041 1 7230 1 5708 1 3861 1 9311 3 8502 1 616 8 1595 1 10325 1 11090 1 5108 1 11896 2 11529 1 882 1 446 1 2918 1 8436 1 8946 2 13171 1 8408 1 6894 1 trinucleate 13216 2 6977 1 4711 1 8977 1 6681 1 6498 1 2131 1 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2613 1 6818 1 16314 2 4579 1 10018 2 4235 1 15688 1 9756 1 15318 1 3164 4 6462 1 16321 4 705 63 328 1 5837 1 10539 2 11619 1 7574 4 16080 1 15200 1 3321 1 7304 1 7443 1 2263 3 10956 1 3992 1 13749 1 3729 1 10286 1 2005 1 5218 1 13993 1 11363 1 16088 1 1598 2 10553 1 3738 1 8361 1 6601 1 12705 1 5221 2 9518 1 6841 1 11924 1 13756 1 3501 1 8108 1 2790 1 5228 8 15717 1 7457 1 14526 1 14811 2 14527 2 11381 1 7091 2 10716 4 596 2 11785 1 1107 1 9787 1 348 1 2917 1 10574 2 2920 1 9384 1 11119 2 10983 2 6731 1 9114 2 3869 1 3638 2 2144 1 12326 1 12848 1 13252 1 5347 2 14027 1 5126 1 11795 1 8856 1 5487 1 7726 1 8989 1 4986 1 3875 1 2806 1 5990 1 362 1 3757 1 1913 1 5128 1 1364 1 6620 1 2152 1 6498 3 1624 2 13648 2 8127 1 1773 1 13649 1 9664 3 7485 2 5134 1 12215 1 15611 1 7350 3 10070 1 505 3 3649 1 12733 1 13398 1 4995 1 14037 3 7112 1 10335 1 6754 1 9138 1 7868 2 3896 1 5508 1 9271 2 1134 1 3769 1 8502 1 7872 1 12873 1 247 1 14043 2 7012 4 1139 3 14444 1 6637 1 12489 1 16373 1 fiscal 892 1 2646 1 3061 1 1337 1 1370 1 3679 1 10429 1 424 1 3242 1 4887 1 10247 1 7923 1 265 1 16019 1 14691 1 11558 1 9203 1 8449 1 15729 1 12416 1 7066 1 14155 1 10879 1 1646 1 3611 1 yardline 756 1 14009 1 10189 1 easing 869 1 3531 1 13034 1 14456 1 2385 1 13263 1 8309 1 11105 1 4335 1 7419 1 5479 1 hard_candy 11593 1 14855 1 15500 1 1237 1 6334 2 12472 1 10369 1 16329 1 14997 1 877 2 706 6 10541 1 8787 1 16281 1 4263 1 unbelted 8517 1 2027 1 12643 1 3750 1 6369 1 1369 1 7667 1 ischia 505 1 3638 1 12705 1 5347 1 genus_nelumbo 8321 1 13878 1 9764 1 9860 1 1510 1 5361 1 13712 1 flightless 9648 3 1331 2 15352 1 3775 1 5733 1 1457 1 3636 11 146 1 884 1 12109 1 6461 1 4335 1 12882 1 3169 1 707 12 5347 1 12266 1 3069 1 669 2 1761 1 5616 1 293 1 8702 3 9631 4 6175 1 3992 2 1770 2 12084 1 3361 1 4062 1 3615 1 16126 1 14297 3 8435 2 397 1 1818 1 10771 1 3038 1 13236 1 9132 1 4030 3 505 1 12643 1 5718 4 11603 1 8529 2 15149 2 3295 1 11572 1 15028 1 15940 2 3264 2 3302 1 9973 1 10372 1 594 1 8475 1 284 2 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13900 1 loosestrife 8438 1 12822 1 6941 1 5991 1 3992 1 3636 1 4961 1 5213 1 12991 3 8994 1 8786 1 4263 1 6449 2 7379 1 6442 1 3760 1 4143 1 7471 1 711 5 2904 1 12092 1 3223 1 15901 1 3020 1 11612 1 obligatory 6011 1 8093 1 12325 2 13315 1 6794 1 6958 1 15502 1 4616 1 8097 2 13820 2 15195 1 10846 1 9377 1 6170 2 2703 1 15115 1 1250 1 8700 1 2177 2 12667 1 9021 1 15385 1 3679 2 11109 2 13277 1 6241 1 1063 1 712 9 12795 1 13182 1 12796 3 2637 1 genus_argyreia 9216 1 5455 1 11259 1 13970 1 chuffed 5523 1 9071 1 5271 1 9821 1 genus_pseudocolus 7365 1 10809 1 7753 1 6661 1 11645 1 1620 1 12439 1 2419 1 1624 1 10561 1 16494 1 8021 1 10018 1 1174 1 genus_ficus 11507 1 15423 1 1428 1 4933 1 14520 1 9635 1 9115 1 8581 1 11474 1 formalwear 5214 1 7587 1 apatosaurus 4143 1 6502 1 6997 1 16207 1 4550 1 10372 1 disrupt 5544 1 9429 1 4699 1 3404 1 5878 1 918 1 4041 1 7832 1 13931 1 10869 1 2151 1 6799 1 15671 1 8529 1 14464 1 15744 1 7745 1 14015 1 12145 1 13380 1 10077 1 7983 1 12707 1 6228 1 6392 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15864 1 4159 1 225 1 12330 1 2151 1 13909 4 13260 1 8861 1 7978 1 11406 2 14823 2 997 5 10879 1 8997 3 2815 1 7485 1 3648 1 10200 1 1635 2 3077 1 505 1 16248 1 3078 1 3218 1 7110 2 2166 1 13403 1 6249 1 3375 1 9413 1 13405 1 2424 1 3764 1 3893 1 6383 1 6755 1 7871 1 1014 1 4041 1 16491 5 8264 1 14838 1 7361 1 3382 1 9145 1 10755 1 2694 1 4281 1 16370 1 11962 4 junk_dna 7809 1 15559 1 1903 1 7214 1 15634 1 unix_operating_system 12943 1 16398 1 10253 1 12537 1 3060 1 8020 1 4041 1 1205 1 3320 1 14060 1 pistachio 14859 1 15786 1 3793 1 13967 1 2019 1 5172 1 5825 1 12785 1 4471 1 13845 1 7471 1 1818 1 7537 1 4986 1 1029 1 1806 1 12768 1 9768 1 13712 1 resonating_chamber 9654 1 11156 1 meter 6011 1 2696 1 5770 1 6266 1 1660 1 2553 2 10758 1 8008 1 1940 1 6016 1 15633 1 1524 1 15759 1 4643 2 3676 2 11972 1 8629 1 14197 1 14855 1 15402 1 9021 1 13931 1 3679 1 4531 1 16008 1 6279 2 16158 1 5271 3 4064 1 13432 1 10092 1 1667 1 11698 1 1818 3 13171 1 11831 1 4292 1 10363 1 11706 1 14861 1 2720 1 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15701 1 1735 1 679 1 2394 1 3216 1 12569 9 11633 1 8408 1 14270 1 6375 1 14271 1 2277 1 11701 1 941 34 4659 7 8438 1 2371 2 3218 1 11446 2 14935 1 13438 1 4537 1 12315 1 11477 2 14555 1 8135 1 2912 5 718 6 2061 1 14494 1 5910 1 3082 1 7956 1 8836 1 1163 1 1250 2 1510 2 3766 1 3084 2 12578 1 4998 1 2646 1 10564 1 309 2 12973 2 11927 1 15248 2 186 2 6667 1 784 1 16494 2 6105 1 10373 1 11749 1 1519 2 16036 1 11419 1 15225 1 32 1 16318 1 9920 1 7777 1 5957 1 9493 1 covenant 939 6 905 2 10363 1 8624 5 6732 2 13417 1 7909 1 6859 1 9298 1 58 1 2526 1 10594 1 8031 1 2326 4 7061 1 13942 1 15637 1 8322 1 1871 1 15339 1 9906 1 6137 1 1604 1 15667 1 8055 1 7785 1 3720 1 3531 1 13474 1 12726 2 13546 5 8991 3 2135 1 8575 1 10044 1 13826 1 5328 1 8648 1 16494 1 14585 1 10977 1 13877 1 15034 3 10147 1 6331 1 1656 1 10361 1 7093 1 moor 940 9 12139 1 2236 1 13054 2 12315 1 13834 1 7549 1 12848 1 15232 1 4263 1 13801 1 3477 1 12705 1 5153 1 13679 1 6830 2 14910 1 14371 1 14855 1 6977 1 9290 2 15717 1 16124 1 13169 1 5217 1 15968 1 1560 1 14311 1 2291 1 12747 1 8021 1 2005 1 6375 1 13264 1 2921 2 2035 1 5115 1 admission_charge 12156 1 1650 1 11339 1 11742 1 inconsiderable 14585 2 3617 2 1642 2 9982 2 520 2 11081 2 7722 2 14236 2 13674 2 spelt 10570 1 9842 1 albumin 8300 2 4944 1 12732 1 9744 1 4714 1 4260 1 15431 1 11636 1 6033 1 942 8 11308 1 3220 1 3983 1 13422 1 4031 1 15636 1 10416 1 14979 1 12772 1 2822 1 3874 1 12664 1 6367 1 9800 1 15403 1 3992 1 15714 1 151 1 10387 1 13150 1 15735 1 1463 1 10513 1 1893 1 11327 2 9445 1 14546 1 996 1 12277 2 2089 1 16276 1 5694 1 13665 2 5766 1 9467 1 6535 1 3435 1 2159 1 jefferson 5198 1 14659 1 4276 1 16290 1 2613 1 4133 1 12752 1 3134 1 13422 1 7604 1 8816 1 6841 1 9664 1 1646 1 10899 1 2806 1 9898 1 6037 1 5593 1 choice_of_words 15959 2 8113 1 15987 1 containment 9339 1 2619 1 14256 1 300 3 2298 1 10825 1 14273 1 14259 1 8785 1 13552 1 1282 1 13821 1 4443 1 13822 1 1964 1 4059 1 12156 1 1101 1 972 1 9675 1 11509 1 15415 1 4987 1 15310 1 5336 1 958 1 15918 1 4131 2 538 1 13277 1 16008 1 7148 1 13912 1 5728 1 1180 1 10325 1 8503 2 13752 1 1573 1 7563 1 12567 1 2444 1 15191 1 11686 1 5694 1 8408 1 3884 1 ordained 1197 1 1124 2 9528 1 5331 1 6200 1 3868 1 7850 1 8529 2 12555 1 8320 1 1523 1 14416 1 943 10 10907 1 8322 1 10496 1 5176 2 1176 1 7827 2 4256 1 10867 1 6992 1 2332 1 15886 1 11843 1 7376 2 11512 1 920 1 468 1 8942 1 1915 1 12948 2 8892 1 3728 1 14353 1 8180 1 8091 1 15058 2 15225 1 12727 1 1181 2 3976 1 nuclease 1291 1 4933 1 10339 1 8635 1 10197 1 9698 1 1242 1 9878 1 14352 1 16438 1 14665 1 9416 1 8459 1 unhappiness 12521 1 7566 1 9881 1 219 1 13775 1 10503 1 2114 1 2925 1 8853 1 12011 1 13743 1 14977 1 9691 1 8157 1 15260 1 1568 1 6167 1 12721 1 15601 2 1801 1 4459 1 5702 1 4529 1 10253 2 9926 1 15509 1 1363 1 14650 1 12669 1 9532 1 9263 1 9400 1 3813 1 8757 1 5811 1 1972 1 3954 1 5388 1 795 1 15933 1 6747 1 4688 1 7382 1 15 1 8801 1 7314 1 6801 1 1920 1 869 2 9830 2 4933 1 12643 1 16248 1 3145 1 3593 1 3734 1 3367 1 10265 1 3820 1 2337 1 944 14 2608 1 62 1 4539 1 6570 1 4853 1 9349 2 13040 1 1825 1 3893 1 6477 1 3084 1 11713 1 15071 1 3626 2 2731 1 12376 1 10139 1 11275 1 16492 1 7430 1 6814 1 14838 3 2214 1 12383 2 11749 2 16495 1 12102 1 4706 1 11547 1 2589 1 601 1 declining 5183 1 15692 1 11223 1 4155 1 11674 1 13552 2 10558 1 2863 1 9165 1 15232 1 12786 1 5346 1 946 8 11395 1 3223 1 8571 1 11778 1 3874 1 13063 2 2438 1 1716 1 7114 1 8769 1 2532 1 763 1 2263 1 6495 1 9705 1 7430 1 14651 1 14927 1 4100 1 1595 1 6144 1 12764 1 8180 1 12543 1 2652 1 16387 1 11686 1 12686 1 1792 2 15877 1 10605 1 5112 1 teasing 7690 1 9819 1 12142 1 8954 1 5986 1 13675 1 77 1 6019 1 8882 1 13490 1 3676 2 13190 2 11250 1 8793 1 9658 1 13588 1 12466 2 12272 1 3992 1 5084 1 11358 1 14613 1 13428 1 7762 1 6241 1 2083 1 13169 1 7167 1 5528 1 11090 1 5129 1 12596 1 7764 1 6747 1 12795 1 14296 1 7484 1 13263 1 8761 1 5629 1 2335 1 10879 1 7169 1 13002 1 4659 1 1635 1 9830 1 10492 1 3145 1 9902 1 3113 1 2337 1 13797 1 1282 1 7549 1 62 1 4410 1 947 15 780 1 3191 1 7956 1 6292 1 2173 1 3626 1 8680 1 15812 1 14835 1 3741 1 9441 1 14733 1 10643 1 6003 1 2581 1 6760 1 15620 2 2072 1 2766 1 11749 2 7260 1 6389 1 14284 1 8651 1 13704 1 7332 1 6520 1 11287 1 puget_sound 3310 1 9186 1 13821 2 3134 1 1300 1 4472 1 14305 1 11619 1 6841 6 2960 1 3084 1 1134 1 5228 2 2613 1 14584 2 6818 1 13359 1 10033 1 3998 1 142 1 6686 2 15518 1 6281 1 9664 1 13312 1 15688 1 hypersensitivity 1331 1 4933 1 15430 2 3258 1 531 1 778 1 948 6 4951 1 8309 1 16026 1 10120 1 5878 1 15918 1 14612 1 15441 1 614 1 897 1 7636 1 5679 1 3534 1 14018 1 16424 1 2816 1 12228 1 11854 1 12901 1 colonize 11966 1 4071 1 5555 1 12524 1 8026 1 12693 1 1567 1 11989 1 5412 1 7711 1 8899 1 4143 1 778 1 2436 1 16225 1 8678 1 9288 1 10233 1 2855 1 6977 1 8012 1 3444 1 8886 1 15219 1 211 1 6326 1 7789 1 2790 1 3483 1 14374 1 6241 1 13408 1 5271 1 3128 1 8349 1 3760 1 11530 1 154 1 2784 1 9092 1 145 1 15301 1 14915 1 2517 1 5140 1 12728 1 6835 1 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446 1 12863 1 3145 5 3218 5 13795 2 1980 1 13799 1 8137 1 2240 1 10076 2 10077 1 950 36 16365 2 5137 2 7868 2 8192 2 4829 1 13919 1 3531 1 2426 2 3659 2 6002 1 11002 3 15941 3 3602 2 14373 1 4887 2 16312 1 5826 2 13660 1 13408 4 14838 2 11686 2 12620 4 1389 4 pushing 4283 1 323 1 12229 1 16262 1 758 1 8821 2 6337 1 13551 1 9557 1 4523 1 12818 1 9224 1 13745 1 4129 1 2960 1 2195 1 9429 1 4590 1 14980 3 2266 1 8517 1 265 1 6279 1 4650 1 5528 1 13432 1 14 1 12569 1 10772 1 6223 1 13034 5 10492 3 8975 1 9096 1 8213 1 15774 1 4536 1 12439 1 1282 1 531 2 12059 1 8532 1 587 1 7587 1 12910 1 16096 4 9171 1 10233 1 536 1 4860 1 13177 1 6479 1 6230 1 11453 1 3744 1 11784 1 15077 1 1897 1 1053 2 13767 1 7393 1 12071 1 6792 1 12521 1 12718 1 16182 1 14532 1 2655 1 7780 1 1490 1 9116 1 6171 1 2873 2 3136 1 5877 1 6495 1 10800 1 9532 1 9397 1 6241 1 16356 1 3814 2 6441 1 5500 1 443 1 14892 1 173 1 12998 1 7799 2 15301 1 10068 1 6374 1 10395 1 4271 2 9541 1 299 1 12029 1 3145 1 11735 2 5568 1 15678 1 2240 1 3082 1 14905 1 7614 1 952 20 13008 1 14626 1 4446 1 10208 1 7114 1 4998 1 7810 1 11002 1 7230 1 2760 1 8325 1 10140 1 13276 1 13277 1 13605 1 13736 1 3971 1 11278 1 814 1 314 1 8815 1 11686 4 13608 1 4834 1 12352 1 6764 1 3238 1 6894 1 ancestry 16039 1 573 1 15318 2 11551 4 4238 1 7095 2 5803 4 5736 1 5412 1 3204 1 12848 4 3134 2 2865 1 6168 1 16268 1 5347 2 11757 1 11619 2 7824 1 13228 1 1871 1 1663 1 13583 2 6275 2 2560 2 293 2 12726 1 8458 1 2358 2 5455 1 8517 2 2563 1 15475 1 1595 1 7132 1 3487 1 14585 2 13295 2 14201 1 5849 1 8021 1 16127 3 799 1 7860 1 4495 1 6801 5 11771 8 16056 1 14555 1 9834 2 6287 4 5136 1 717 1 5598 1 2423 1 7587 1 951 22 14365 1 1549 1 15028 1 4998 1 12410 1 9210 2 13076 2 7717 2 8500 2 1560 1 8421 1 6483 3 1264 1 16315 2 5694 1 14841 2 11964 6 12688 4 11612 1 13482 1 11584 1 fit_out 6655 1 lengthways 5649 1 9394 1 13001 1 14410 1 saunter 12817 1 15576 1 11002 1 4921 1 5966 1 5053 1 13698 1 13163 1 karat 5934 1 13676 1 16344 2 16154 1 8666 3 12086 2 16492 1 10310 1 14374 13 9467 2 15996 2 nitpicking 9830 1 7996 1 5778 1 3860 1 10600 1 4098 1 7488 1 6988 1 3778 1 5319 1 7751 1 8769 1 anal_intercourse 2385 1 13171 3 8517 1 2247 1 703 1 10380 2 6255 1 10318 1 arithmetically 2918 1 12953 1 7649 1 7961 1 devastate 10077 1 14925 1 8290 1 rampantly 5739 1 14683 1 1659 1 13900 1 5669 1 reigning 5598 1 9937 2 9572 2 12102 1 5776 1 8942 1 12563 2 7132 1 bipara 2566 1 2898 1 8517 2 319 1 4925 1 1510 1 8128 1 8749 1 12102 1 12795 2 7729 1 feint 15375 1 4266 1 6324 1 542 1 2619 1 4638 2 4048 1 1370 1 7900 1 9477 1 13096 1 10874 1 62 1 8911 1 16405 1 10077 1 14858 1 2476 1 10656 1 12268 1 362 1 renting 2263 1 5038 1 3306 1 10934 1 5271 1 8750 1 machinate 6530 1 1137 1 2639 1 387 1 14529 1 9247 1 15225 1 14614 1 4689 1 2757 1 11769 1 12556 1 nimrod 8421 1 16494 1 1216 1 4506 1 1444 1 1248 1 7729 1 4073 1 7266 1 16156 1 2887 1 6104 1 skip_town 8618 1 9359 1 1976 1 2997 1 6053 1 triskaidekaphobia 4925 1 11108 1 11739 1 8739 1 penicillin_v 5878 1 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7729 1 5621 1 13260 1 6498 1 8862 1 16356 1 9400 1 8246 1 12595 1 125 2 16483 3 1239 1 5628 2 126 2 10879 2 2815 1 4495 2 15611 1 4164 2 5883 1 3218 3 1370 1 3367 1 12735 3 15878 1 7488 2 8869 2 11133 2 13917 1 7111 2 9937 2 1642 1 3082 1 15114 1 3375 1 6884 2 3376 2 3377 2 9546 2 13273 1 1510 1 510 2 10208 1 3766 2 6123 2 135 1 7496 1 11002 1 1014 1 4887 1 4630 1 14838 2 7009 1 13408 1 2691 5 3382 4 14181 1 16494 3 11546 1 12616 4 7012 1 7876 1 11282 3 4281 2 11420 1 6010 1 9552 1 10081 4 8270 1 unceremonial scientific_theory 3634 1 11172 1 10363 1 9242 1 529 1 11002 1 13531 1 5901 1 13754 1 10430 1 4537 1 16472 1 5889 1 5165 1 5403 1 4823 1 2620 1 2423 1 5849 1 68 1 15880 1 4502 2 9457 1 4495 1 11612 1 13814 1 in_short_order 2608 1 1201 1 2241 1 8338 1 1596 1 3133 1 2214 1 3859 1 perceptable 14883 1 14658 1 12145 1 3913 1 4087 1 4707 1 10584 2 8769 1 4950 1 13626 1 endearment 11033 1 8083 1 15643 1 5804 1 12020 1 3410 1 822 1 give_way 4158 1 12718 1 3592 1 6713 2 13939 1 12139 3 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6799 1 1651 1 3983 1 11726 2 1146 1 14584 1 7747 1 3676 1 6865 1 12569 1 14658 1 10131 2 12353 2 11346 1 409 1 11985 1 11474 1 birthing 4283 2 4590 2 3806 2 4886 2 1134 2 politics 16374 1 5005 1 15254 2 7367 1 14572 1 4049 1 3242 1 8153 1 14708 1 631 1 13551 2 1145 2 14317 1 13821 1 5148 1 13822 1 5266 4 13674 1 4056 1 7238 1 11147 1 15400 1 15899 2 5379 1 10899 6 6655 1 763 2 12757 1 14460 1 1030 3 5778 1 10619 1 16004 1 3679 1 10485 1 12497 1 4532 1 265 3 14719 2 14463 1 14066 1 7516 1 152 1 3107 1 4064 1 638 1 13684 1 8761 1 10903 1 8167 1 772 1 10363 1 1040 2 7888 1 11986 1 273 1 8764 1 1282 3 6287 2 10495 1 14595 1 16168 1 13040 1 12774 1 11031 1 8769 1 9439 1 7256 2 12776 1 3427 1 11033 1 8040 2 5279 1 5178 1 278 1 7649 1 7042 1 1169 1 8180 2 6158 2 1054 1 1055 1 16178 1 6161 1 16180 1 10245 1 5184 1 5671 1 13053 1 5038 1 16182 1 2989 1 12524 1 12922 1 5187 1 9052 1 4093 1 2991 1 1836 1 7660 1 2355 1 1965 1 14884 1 16189 1 13711 1 1176 1 931 1 2873 1 7789 1 8314 1 16190 2 8432 1 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9109 1 2921 1 14946 1 9920 1 11119 1 12718 1 219 1 2143 1 7095 1 4155 1 9793 1 8853 1 7851 1 16117 1 606 1 12848 2 11121 1 2657 1 8379 1 1617 54 15090 1 14536 1 6238 1 611 1 5493 1 1364 1 13260 3 6498 1 12995 1 9400 1 9535 1 2031 1 10325 2 5129 1 5500 1 7983 2 1242 1 5745 1 16484 8 3646 1 2814 1 15875 1 1635 1 12471 1 5883 1 3218 1 11538 1 13795 1 12736 1 13799 1 12220 1 8137 2 752 1 6509 1 4502 1 1646 2 15115 1 6254 1 3528 1 3084 1 3531 1 2426 1 4998 1 3898 2 6257 1 13150 1 14309 1 4630 1 15249 1 13408 1 14838 1 7009 1 6760 1 12101 1 4889 1 12225 1 14570 1 11282 2 2824 1 10216 1 11686 1 14186 1 12352 2 12353 1 7751 1 1656 1 15521 1 12620 2 15124 1 13813 1 14189 1 3238 1 16373 1 dissuasion 4041 1 15436 1 hereditary_pattern 3797 1 15515 2 9268 1 994 1 12312 1 5240 1 12848 2 divided_up 11678 1 15772 1 8718 1 1707 2 12326 1 8002 1 2608 1 142 1 4104 1 12418 1 8997 1 15115 1 1196 1 13354 1 compromising 9830 1 12410 1 7531 1 3992 1 14492 1 1053 1 2736 1 8239 1 4247 1 1548 1 5820 1 7759 1 4599 1 mount 11614 1 5184 1 10983 1 13521 1 1084 1 9735 3 13419 1 4155 1 14193 1 7912 1 12041 1 3396 1 1618 21 8202 3 2627 1 8512 1 6655 2 7665 3 14855 1 5778 3 10321 1 3992 1 14650 1 15441 3 6345 1 9662 3 9462 1 7636 1 5426 1 9860 1 4534 1 4994 1 201 3 4464 1 9830 1 5428 1 13034 1 5785 1 15591 2 2720 1 343 1 7032 1 6118 1 4353 1 11882 1 1673 1 9169 1 13008 1 2879 1 1604 1 4295 1 4605 1 14627 1 2855 1 7256 1 7293 3 3156 1 13698 1 14374 1 12540 1 14012 1 9309 1 4172 1 8813 2 11282 3 15392 1 1193 1 1685 1 11612 2 3832 1 5957 3 zingiberaceae 3760 1 9680 1 1014 1 13878 1 5849 1 13004 1 13382 1 11414 1 12578 1 6600 1 profanity 4164 1 12643 1 13130 1 9693 1 13273 1 15115 1 2173 1 12941 1 12302 1 11483 1 12410 1 6371 1 15459 1 7360 1 1309 1 5526 1 13718 1 12255 1 15495 1 7718 1 8180 1 13648 2 13395 1 14569 1 4843 1 4707 1 12569 1 4495 1 4190 1 6852 1 5181 1 plastic_explosive 16065 1 caryophyllaceae 13216 2 6977 1 4711 1 8977 1 6681 1 6498 1 2131 1 6600 1 6516 1 11414 2 409 1 9880 1 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15279 1 845 1 13377 1 1607 2 12541 3 13513 1 13736 4 14702 1 4172 4 595 1 14147 1 10017 1 6070 1 5317 1 6580 1 unfurnished 2303 1 7110 1 6643 1 14913 1 tapering 13740 1 387 1 7365 1 3310 1 7753 1 15255 1 5262 2 14507 1 15692 1 1332 1 5771 3 7879 1 11010 1 3904 1 8154 1 4335 3 6585 1 12815 2 10541 1 16078 2 4588 1 10830 1 5012 1 7239 1 3172 1 2775 1 14196 1 3320 1 2260 2 7129 1 2836 1 9822 1 8702 1 5016 1 1149 2 264 4 14651 1 6967 2 2197 1 4958 1 13935 1 6345 1 8828 1 16087 2 8021 1 13171 2 2394 2 14270 1 11983 1 10772 1 9899 1 10628 2 6661 1 1344 2 9635 1 8582 1 12439 1 3258 2 1041 1 16092 1 4352 5 5466 1 7891 1 10561 1 7711 1 4143 5 8032 4 13114 1 14216 1 15342 1 14994 1 11233 3 11645 1 13893 1 3195 1 2214 1 14526 1 1104 2 3697 1 12070 1 850 1 13768 1 16109 1 9648 1 15500 1 14532 1 8477 1 13314 1 4260 1 12981 1 9650 1 7268 1 2802 1 4263 1 12847 1 1836 3 12849 1 4809 1 1906 1 16411 1 12266 1 13323 1 13461 2 3875 1 1174 1 10991 1 15293 1 7541 2 2027 2 8733 1 10321 1 13785 1 9192 1 2999 1 6496 1 14352 1 1698 1 1620 37 6871 1 1915 1 1624 1 10513 1 14163 1 440 1 10128 2 7542 1 14294 1 2306 2 9932 1 16244 1 12795 1 2157 1 2089 4 15572 1 3366 1 6375 1 12148 1 4029 1 6801 1 7486 1 11474 2 179 2 802 1 10809 1 14432 1 5135 1 13967 1 12342 1 2419 1 5950 1 1508 1 4031 1 6251 1 4882 1 4698 1 4998 1 15887 4 185 1 3532 1 7010 2 2951 2 16494 1 9876 1 7120 1 3972 1 2548 1 10018 8 12546 1 9216 1 11346 1 14047 1 14189 1 14972 2 6644 1 retractile 2050 1 11463 2 3692 1 15541 2 10784 1 8595 1 16377 1 185 1 10871 1 11496 1 3532 1 1622 6 1624 2 11811 1 9760 1 16229 1 8748 1 13895 1 14658 2 8512 1 1736 1 10656 1 3875 1 11474 2 12122 1 339 1 intuitively 8438 1 5883 1 13486 1 9113 1 8821 1 3367 1 1185 1 5333 1 9670 1 15788 1 13552 1 12623 1 8531 1 4950 1 6553 3 779 1 4664 1 3135 1 11776 1 10418 1 3261 1 12268 1 12360 1 16237 1 15155 1 10820 1 2011 1 15812 1 6809 1 1014 1 3741 1 5648 1 8314 1 12002 1 1623 7 11282 1 2088 2 1055 1 7800 1 5531 1 7751 1 1541 1 7169 1 5427 1 15877 1 alarm 5143 4 7935 1 758 1 5897 1 4332 1 1724 3 10220 1 4948 1 4950 5 7302 1 11819 1 258 1 12961 1 13354 1 7945 1 3404 1 11761 1 82 2 8757 1 14465 1 3490 1 3729 1 2902 1 6833 1 1209 1 16012 1 1541 1 15537 1 9830 1 4728 3 13626 1 12315 1 9719 1 5654 1 4076 1 4907 1 14525 1 9779 1 3859 1 15965 1 12912 1 11108 1 11781 2 212 1 11646 2 10970 1 213 1 2581 1 14279 2 4082 1 1750 1 7260 1 13949 1 2797 1 6853 2 2349 1 12718 1 13772 1 5926 1 11461 1 8121 1 15667 1 4921 1 5190 2 2997 1 4814 1 741 1 11657 1 4308 1 14612 2 14820 1 16482 1 6241 1 1626 20 8246 1 442 1 5500 1 13263 1 16484 1 13467 1 126 1 7731 1 8437 1 15301 1 10131 1 10068 1 8128 1 9470 1 5883 1 6059 1 1004 3 1246 1 3145 1 10996 1 10333 1 9202 2 15677 1 62 1 12740 1 15026 2 10522 1 7227 1 4040 1 7293 1 16491 1 3303 1 16258 1 10688 1 7872 1 13736 1 14442 1 13082 1 11749 1 11895 1 6389 1 14504 1 144 1 12620 1 rumor 6011 1 8151 1 13315 1 2862 1 10896 1 4369 1 7724 1 10351 1 16117 1 15503 1 7301 1 6167 1 16473 1 3676 1 1693 1 9453 4 15602 1 14883 1 8055 1 432 1 16326 1 12207 1 2490 1 762 1 10385 1 13092 1 8458 3 9955 1 9021 1 10028 1 5901 2 1204 1 15136 1 11945 1 8517 1 2122 1 8967 1 1625 18 8088 1 6685 1 14585 1 13360 1 3816 1 1734 1 14828 1 10067 1 7731 4 13722 1 15301 1 9574 1 15986 1 10362 1 1211 1 299 1 401 2 10363 1 4728 1 56 1 1637 3 2640 1 14555 1 10333 1 14692 1 3189 3 4824 1 16195 1 11227 1 16395 1 8289 1 12939 1 14626 1 1645 1 15517 1 10780 1 510 1 7496 1 8292 1 12410 1 2945 1 245 1 1254 1 211 1 3303 1 7148 1 10340 1 7932 1 10893 1 2736 1 13380 1 10376 1 14969 1 6389 1 15225 1 13608 1 12352 1 6853 2 15084 1 8373 1 inundated 4659 1 5332 1 9219 1 10758 1 4936 1 10351 1 9936 1 759 1 15563 1 14979 1 3399 1 739 1 2703 1 3875 1 12131 1 8769 1 11108 1 1014 1 3741 1 14964 1 12274 1 2415 1 10970 1 13449 3 4388 1 6261 1 15933 2 12161 1 8523 1 13171 1 15572 1 last_resort 7172 1 3145 1 9667 1 9771 1 13795 1 10503 1 13674 1 10076 1 2641 1 950 2 3223 1 10917 1 7868 1 8203 1 9596 1 481 1 5240 1 7390 1 16151 1 5586 1 2426 1 3659 1 9641 1 3032 1 5878 1 15918 1 11512 1 14718 1 13749 1 7792 1 9400 1 15906 1 3729 1 13765 1 8449 1 15058 1 12260 1 11686 1 10377 1 1389 1 12620 1 fragile 7752 1 10824 2 1660 1 6203 1 12692 2 328 2 13821 1 8823 1 16078 1 4951 1 7239 2 15762 1 3173 1 11493 1 7441 1 2836 1 2560 1 11081 2 6023 1 8012 1 14980 1 4591 1 4531 1 11358 1 7189 1 14323 1 1534 1 834 1 1037 1 8021 1 4000 1 8523 1 12570 1 8408 1 8167 1 13236 1 8093 1 12643 1 6907 1 6661 1 16459 1 3556 2 13626 3 12439 1 4352 1 15911 1 1161 1 7711 1 3558 2 3420 1 8901 1 14470 1 8837 1 11171 2 7039 1 11509 1 7774 1 15219 1 9041 1 12380 1 13118 4 8418 2 9176 1 1049 1 12448 1 12003 1 13578 1 1104 1 11178 1 2796 1 2921 1 4366 1 3269 1 9590 1 7598 1 1170 2 15353 1 9527 1 5671 1 5408 2 14532 1 10310 1 10054 1 7049 1 15788 1 5736 1 4487 1 4156 1 4263 1 15973 1 12849 2 12145 1 12720 1 5288 1 10795 1 3136 1 5877 1 15799 1 2152 2 8314 1 1624 2 7167 1 12212 1 1627 24 935 1 15872 1 172 1 8550 1 15446 1 12862 3 7169 1 7486 1 13535 1 11601 2 7350 1 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1630 11 6747 1 3676 3 924 3 1845 3 2394 1 7837 1 10727 3 6444 3 1888 1 5965 3 9094 1 12044 3 5989 3 2662 3 9920 3 graduated 7068 1 5443 1 15692 1 9219 2 16448 1 10023 1 10541 2 15470 2 3245 1 13021 1 4643 1 3839 1 8083 1 5210 1 15637 1 5447 1 8571 1 5964 1 10356 1 1999 5 14578 4 2262 1 10421 1 9021 3 7376 1 7189 1 1877 1 16008 1 4243 1 5328 1 1534 1 8576 1 5271 1 1595 1 5847 1 11156 1 267 1 13099 1 11560 2 4464 1 580 1 1211 1 3556 1 94 1 15910 1 14469 1 837 1 12181 1 12439 1 10231 1 13110 1 10296 1 15339 1 7837 1 14007 1 5602 1 14009 1 2857 1 13578 1 6848 1 11380 1 15000 2 4975 1 6105 1 9381 1 12008 1 9918 1 11119 2 351 1 15430 1 112 1 7912 1 5412 1 15089 1 6106 1 11461 1 12145 1 7158 1 3874 1 2026 1 3138 4 16478 1 5128 1 6619 1 13785 1 12669 2 14352 1 9532 1 16355 1 6241 3 12856 1 16356 1 8246 3 11406 1 1631 32 14892 2 373 1 10392 2 12599 3 5132 1 10131 1 2232 1 9606 2 7419 1 13002 4 9340 1 3145 1 7990 1 11060 1 62 1 11413 1 14693 1 6508 1 13271 1 15880 1 8322 1 10685 1 12094 2 12480 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15649 1 13837 1 12374 1 13694 1 4907 1 10637 1 13839 1 13301 1 8769 2 2577 1 13177 1 1828 1 15419 2 4202 1 2581 1 2214 1 1681 52 14738 1 4082 1 8180 1 4546 1 1055 1 924 1 9046 1 6546 1 5183 1 2989 2 6794 1 10660 1 168 1 12145 6 1964 1 7661 2 15564 1 13583 1 11187 1 4921 1 2466 1 7410 6 8313 1 4925 1 14612 1 7669 1 7412 1 12019 3 8314 5 5810 2 2083 1 16302 1 9330 1 15015 1 14236 2 6686 1 12795 1 9195 1 15445 1 9604 1 7169 1 5945 2 179 1 300 1 4933 2 3145 1 10265 3 7174 1 945 1 9868 1 12534 1 4110 1 10684 1 11335 1 2882 1 11339 2 11886 1 1443 1 12410 2 3461 1 14500 1 10525 1 10406 1 11749 1 815 1 697 1 4707 3 10411 1 6070 1 8330 2 9753 1 14636 1 16318 1 15946 1 10533 1 9217 1 11753 1 15468 1 3475 1 5960 1 704 2 3608 1 12556 1 5836 1 11906 1 6081 2 6829 1 16081 1 8571 1 15953 1 5965 1 10544 3 5078 1 2262 2 11622 1 10698 1 6713 1 2266 1 3483 1 14929 2 5846 1 12962 1 8578 1 15058 1 6969 1 835 2 6596 1 14133 1 10428 1 3852 1 14934 1 8093 1 11920 1 3044 1 8834 1 12181 1 9903 1 4966 1 9517 1 10298 1 14940 1 2134 1 1219 1 5859 1 594 1 13765 1 13380 1 15497 1 7595 2 7966 1 6853 1 2142 1 2016 1 9920 1 12585 1 218 1 1756 1 2924 1 4155 1 13525 2 1490 1 12848 2 15729 1 11796 1 6237 4 6367 1 16236 1 6369 1 745 1 1235 1 7607 1 12333 1 9400 1 10325 2 13648 1 10196 1 503 1 16484 1 997 1 12727 1 7733 1 7734 1 1924 1 13795 1 15110 1 4761 1 13799 1 9542 1 3223 1 6001 1 10207 1 7616 2 5888 1 10462 1 7115 1 3531 3 1014 1 11002 2 13150 1 1256 2 14838 1 10756 1 11960 1 4510 1 11419 1 12102 1 11006 1 6520 1 6894 1 7014 2 16372 1 account_for 8726 1 1635 2 6011 1 8374 1 1183 2 11903 1 6318 1 4868 1 10221 1 1145 1 8237 1 1171 1 62 1 7237 1 15436 1 2642 1 11742 1 8202 1 2627 1 1086 1 3949 1 2881 1 5913 1 9639 1 7164 1 1013 1 13075 1 15119 1 14600 1 484 1 2483 1 6714 1 16329 1 12449 1 1682 8 7636 1 13283 1 5512 1 2846 1 1055 1 4013 1 9860 1 5813 1 5088 1 1611 2 13814 1 farsi 14243 1 421 1 10977 1 12674 1 15058 1 10836 2 6043 1 9819 1 12990 1 1099 1 yelling 4542 1 593 1 13082 1 4669 1 5034 1 13186 1 15602 1 9704 1 15100 1 9477 1 15124 1 loft_bombing 702 1 10258 1 8582 1 9723 1 2284 1 16065 1 7413 1 solitarily 15764 1 8128 1 10845 1 6732 1 canine 9648 1 13034 1 2971 1 9405 1 13726 1 3636 1 9635 1 8595 3 10790 1 12342 1 8853 1 10167 1 13821 1 14664 1 4521 1 11147 1 3754 1 7239 1 15651 1 10583 1 11171 3 9103 1 11509 2 16170 2 363 1 11956 1 12537 1 11416 1 11002 1 4383 1 7729 1 7230 1 10871 1 14065 1 6574 1 1770 1 7329 1 897 1 4803 1 7501 2 7746 1 1683 8 6146 1 5694 1 7314 2 7350 1 knife_pleat 8980 1 4933 1 996 1 2163 1 807 1 tiered_seats 5295 1 13214 1 2100 1 10951 1 7191 1 14270 1 12353 1 collider 10126 1 14015 1 2544 1 13235 1 7559 1 1893 1 6876 1 15470 1 10280 2 plaza 3182 1 1565 1 2989 1 8652 1 3094 1 9735 1 8917 1 3476 1 13469 1 6474 1 2314 1 12647 1 13386 1 623 1 11451 1 11103 1 12609 2 334 1 3875 1 10617 1 8769 1 14562 1 1982 1 4626 1 4680 1 3661 1 918 1 1014 1 10255 1 16491 1 13076 1 14159 1 14927 1 8020 1 12323 1 1684 5 601 1 3059 1 ignite 5733 1 9386 3 10247 1 13187 1 14193 2 289 1 10454 1 12041 1 3701 1 15636 1 7505 1 6080 1 332 1 2598 1 10506 1 1458 1 3676 4 7239 1 1528 1 1944 2 9853 1 10316 1 4813 1 7973 1 12206 1 3173 1 8512 1 2997 1 7665 1 2490 2 4433 1 14855 2 10421 2 5295 1 11657 1 9704 1 13428 1 15203 1 676 1 5810 1 4216 1 3816 1 13263 1 14828 1 7799 1 14658 2 10880 1 5089 5 11633 1 8408 3 14430 1 9509 2 14071 3 7028 1 10492 1 4728 1 2499 1 557 1 13966 1 7547 1 9031 3 7250 1 8360 1 7202 1 14555 1 12371 1 9408 1 15963 1 10495 1 15880 1 4856 1 12094 1 13008 3 12131 2 15071 1 5544 1 2319 1 11002 1 14143 1 11608 1 3694 1 2581 1 213 3 6890 1 14838 2 16104 1 6260 1 1290 1 14015 1 7120 1 15687 1 7393 2 12620 1 1686 21 3748 2 3699 1 4088 1 6894 1 600 5 1296 1 pursuer 2745 1 1807 1 252 1 8254 1 11674 1 12995 1 12556 1 12041 1 10905 1 4631 1 3815 1 11268 1 1053 1 12740 1 11309 1 680 1 8034 1 747 1 14075 1 13068 1 16144 1 for_example 5007 1 9819 1 13552 1 12622 1 2433 1 10350 1 12882 1 15260 1 13422 1 7757 1 7238 1 3676 1 15637 3 3909 1 10478 1 8277 1 6655 1 2960 1 8012 1 5778 1 11 1 9705 1 1150 1 264 1 8014 1 16008 3 14585 1 13434 1 1818 1 7025 1 8408 5 7765 1 10492 1 13034 2 10363 1 529 1 6538 1 5172 1 273 1 16019 1 15910 2 7522 2 4537 1 12372 1 531 1 14212 1 8032 1 4907 1 12245 1 11171 1 536 1 7256 2 16399 1 9305 1 13177 1 9306 1 10368 1 2857 1 5546 1 14221 1 2581 1 2214 1 1287 1 1293 1 1687 66 13952 1 15664 1 14086 1 7534 1 15009 1 5926 1 15170 1 12145 2 16413 1 14481 1 13063 1 15667 2 2466 1 13712 1 7667 1 14610 1 10253 1 1425 1 7413 1 13064 1 16418 1 1180 1 6685 1 13330 1 10392 1 15572 1 6178 2 7169 1 9994 1 7421 1 4933 1 7802 1 448 1 13968 1 7549 1 2240 1 9478 1 1853 2 14626 1 2099 1 8076 1 16065 1 13076 1 13736 1 11895 1 3831 1 5694 1 1194 1 16318 2 1861 1 11612 1 5957 1 4944 1 15581 1 573 2 11902 1 15692 1 3164 1 6958 1 8954 1 6204 1 11209 1 4950 1 7302 1 11755 1 6828 1 14118 2 15042 1 15043 1 6830 1 3320 1 9889 1 4129 1 1873 1 3481 2 5586 3 10156 1 16328 1 4957 1 7447 1 15330 1 15056 1 3490 1 9506 1 12569 1 1880 1 1596 1 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12283 1 6502 2 11734 1 11476 1 6628 1 15112 1 13405 1 9138 1 11414 1 5202 1 2425 1 5689 1 13339 1 12744 1 3964 1 4169 1 10752 1 7010 2 8147 1 16069 1 3158 1 12225 1 7120 2 2548 1 15579 1 3974 1 6580 1 12353 1 14189 1 9277 1 11287 1 6644 1 indelicate 12410 1 3072 1 3736 1 5620 1 1160 1 1689 6 16329 1 5960 2 11485 1 4701 2 566 1 15312 1 13130 2 4912 1 15025 1 1784 1 9563 1 1510 1 14047 1 15795 1 theatrical_poster 5684 1 13461 1 11227 1 8083 1 reshoot 12608 1 12995 1 16495 1 6655 1 2036 1 horny 7690 1 1331 1 1937 1 12231 1 884 1 4335 3 5837 2 2958 1 8454 1 10475 1 15636 2 7237 1 4951 2 3676 1 7574 2 1397 1 10418 1 6965 1 7129 1 5644 1 2560 2 6656 2 10545 1 14514 1 14980 1 13987 1 3992 1 15905 2 5903 1 16008 2 11496 1 1463 1 8890 1 10487 1 11217 1 4062 1 7636 2 11827 3 15057 1 5849 1 13434 1 6535 1 14394 1 771 1 9899 1 3038 2 6718 1 4346 1 2971 1 3555 1 6907 2 1738 1 11636 1 15591 1 3556 5 1472 1 4352 3 7386 1 6540 1 1822 1 4143 2 20 1 1042 3 779 1 11777 1 11994 1 15277 1 11171 1 14216 2 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1 3582 1 5240 1 8312 1 9598 1 12726 4 5877 1 1766 35 5419 2 14160 1 14820 1 10514 1 1180 1 6685 1 2306 2 15873 2 4495 1 15611 1 8491 2 3010 3 14687 1 10881 1 4692 1 5750 1 1924 2 13968 1 7218 2 14173 1 1313 1 5505 1 4761 1 6628 1 1507 1 5307 1 6568 1 9937 6 2753 3 5506 1 11605 1 9268 1 3764 1 10745 1 9615 1 2426 1 6385 1 14698 1 15118 2 1444 1 14177 1 3089 5 8942 4 13280 2 13408 1 10212 5 13736 2 2950 1 2765 1 3305 1 16494 1 7563 1 7012 9 15579 1 4511 1 12102 2 1262 2 5830 2 14186 1 11285 3 backwoodsman 9830 1 2621 1 16224 1 5710 1 4004 1 12392 1 13171 1 182 1 debar 12569 1 4537 1 11819 1 8031 1 6376 1 deflated 4659 1 16436 1 4933 1 14328 1 4847 1 14352 1 1768 9 8314 1 10340 1 1893 1 12100 1 3958 2 11906 2 7133 1 6168 1 7702 1 5188 1 6081 1 15933 1 8659 1 1389 1 5078 1 12045 1 6894 2 diagnose 12585 1 4283 1 14449 1 758 1 12231 1 6318 1 11838 1 11308 1 12059 2 10906 1 532 1 4527 1 12091 1 10401 1 8309 1 14120 1 15411 1 4907 1 118 1 7615 1 14941 1 11080 1 10544 2 7474 2 15917 1 14680 1 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6494 1 scorpaenoid 16377 1 13911 1 14189 1 13718 1 16411 1 2089 1 4698 1 gulf_of_mexico 5442 1 11966 1 5770 1 3310 2 4332 1 3164 6 13419 1 13672 1 4335 1 16321 6 705 1 13821 2 10539 1 11292 1 16078 1 15759 3 2701 3 7506 1 15400 4 6830 1 7239 1 6136 1 12359 1 2263 1 3992 6 14065 1 15833 1 5387 1 2052 2 14584 4 768 1 5017 1 13878 1 8021 2 1596 1 1403 1 12123 1 13035 1 3738 1 3258 1 12968 1 4143 1 14990 2 4540 1 2646 1 8040 1 13700 1 5179 1 11646 1 7457 3 5228 5 15547 1 14811 2 9838 1 11381 1 7091 9 3130 2 6232 1 159 1 11383 1 8044 2 414 1 7149 4 2797 1 1357 1 10979 1 1685 3 12919 4 9384 7 13312 4 13706 1 11119 2 5671 1 11652 1 7657 1 353 1 5733 1 663 1 856 1 9186 4 11859 2 3134 2 1300 3 11728 1 4678 1 5347 1 7853 1 6238 1 6555 2 13583 1 6368 1 293 5 1177 1 5739 2 223 1 13784 1 6175 4 7103 1 12084 1 13718 2 15982 1 3954 1 4437 1 6686 2 1773 46 11871 1 2089 3 11598 1 7485 7 9664 1 11055 1 15020 1 6801 1 9131 1 802 1 4030 1 13724 1 10398 1 13144 1 12284 4 13728 1 11675 1 13538 1 685 1 6691 1 10335 2 6754 2 6188 1 7007 1 3084 3 4829 4 6125 1 1983 1 1134 1 185 1 6890 1 311 1 6128 1 16314 2 8503 1 11683 1 1583 2 3307 1 1328 1 12163 1 2043 1 9278 1 scalp 4283 1 4944 1 5959 1 14191 1 10757 2 4332 1 12231 1 16377 1 8822 2 14641 1 3167 1 1725 1 7574 1 12557 1 8398 1 10418 1 9228 1 11213 1 2836 1 15401 2 8012 1 5778 1 151 1 16008 1 12761 1 4720 1 11827 1 4188 2 7636 3 13029 1 12899 1 8021 1 8829 1 13034 3 12643 1 6907 1 5718 1 14469 1 5720 1 3258 2 8709 1 10561 1 4143 3 10633 1 7587 1 16026 1 4606 2 3051 2 3741 1 10305 3 5104 2 8180 2 12386 1 13451 1 3346 1 9918 1 3699 1 2460 1 9920 1 12718 1 10245 1 3750 1 15430 1 15925 1 9449 3 2658 1 7783 1 13461 1 3875 2 7161 1 5240 1 4024 1 2928 1 3136 2 1364 1 13531 1 7789 1 10189 1 14160 1 11327 2 227 9 996 1 1772 27 2749 2 867 1 6178 1 14893 1 11262 1 2161 1 8319 1 16429 1 4497 1 14555 1 12091 1 2755 1 2881 4 7870 1 12943 1 10463 1 9079 1 7557 1 5439 1 5440 3 16494 1 8265 1 7120 1 8620 1 11419 1 5694 3 14253 2 11964 1 14047 1 wasted 15581 1 1331 1 6897 1 7820 2 252 1 9496 1 10380 1 328 1 7210 1 13674 1 12662 1 2528 1 8309 1 6238 1 15586 1 15761 1 7019 1 9926 1 2389 1 4433 1 6530 2 3071 1 6677 1 9021 1 3914 1 4159 1 13785 1 10956 1 15831 1 14515 1 7342 1 9532 1 14160 1 4957 1 5214 1 8124 1 6967 1 16482 1 6241 1 12764 1 4401 1 637 1 13648 1 15933 1 1774 12 6283 1 7170 1 10678 1 16015 1 800 1 9830 1 7802 1 6907 1 8975 1 8463 1 1370 1 9067 1 10265 1 16019 1 5307 1 5570 1 205 1 971 1 11572 1 4041 1 484 1 15459 1 2288 1 5178 1 7148 1 14838 1 3382 1 7362 1 16107 1 11749 1 5108 1 16036 1 7120 1 8984 1 11282 1 11547 1 4118 1 9109 1 3238 1 8332 1 harmonic 14687 1 11430 2 12297 1 15274 1 3145 1 1660 1 6336 1 1782 1 14469 1 4487 1 14948 1 13901 1 5412 1 4950 2 3753 1 12145 3 9867 1 3839 1 2504 1 5888 1 2466 1 15812 1 1698 1 12669 1 8559 1 10306 1 12099 1 9379 1 1426 1 16258 1 10513 1 7103 1 2494 1 15462 1 12159 2 8799 1 14147 1 4300 1 11383 1 1667 1 1054 3 1775 9 12353 1 9337 1 5169 1 treatise 6633 1 6257 1 7178 1 15630 1 5621 1 10465 1 3483 1 10800 1 4141 1 4767 1 6781 1 813 1 7189 1 1615 1 12848 1 3701 1 6892 3 7374 2 4706 1 13490 1 795 1 14657 1 333 1 1777 6 2038 1 15020 1 2922 1 beatles 1406 1 6712 1 14916 2 7729 3 3483 3 85 1 4761 2 9486 1 13110 2 4779 3 7927 1 4705 1 9168 1 8072 1 1776 6 3582 1 4749 4 6058 1 detective 8507 1 5671 1 4712 1 4774 1 3202 4 9359 1 1298 2 6825 3 4180 1 9886 1 4054 1 10313 1 433 1 2873 1 4529 1 4785 1 7729 2 3483 4 13260 1 8432 1 8892 1 11192 1 1180 3 153 2 14297 1 9064 1 13171 1 11666 1 1778 28 13863 4 6801 2 10262 1 13105 1 3077 1 5197 1 8462 1 11709 4 3925 1 15110 1 8530 2 2911 1 9346 1 3189 1 16136 1 13110 2 12534 1 11777 2 12288 1 2882 1 1826 1 14041 1 14307 1 14077 1 8108 1 13698 1 813 12 4700 1 6890 3 1607 1 4450 1 5403 1 4737 1 12808 1 4912 10 4279 1 8180 1 10755 1 6328 1 14969 1 12260 1 5205 1 9918 1 11939 1 psychologist 12718 1 3308 2 219 1 7049 1 5187 2 5412 4 10278 1 607 1 7573 1 13058 1 1907 1 120 1 958 1 4267 1 7729 1 5621 1 3483 2 13260 1 265 1 14322 1 12083 1 16356 1 469 1 14931 2 14236 1 15871 1 5528 1 7135 1 14356 1 13648 1 12795 4 9159 1 8550 1 10427 1 11917 2 1779 22 6801 2 1976 1 906 1 4660 1 8026 1 3370 1 1408 1 16019 1 11921 1 531 1 1078 1 532 1 450 1 14864 1 689 1 12609 1 3527 1 1510 1 13576 4 7773 1 6257 1 8108 1 13075 1 11034 1 13011 1 12713 1 8501 1 8983 1 4117 1 10572 1 281 1 4013 1 7966 1 12228 1 284 1 10309 1 toiling 16078 1 13440 1 14268 1 5486 1 15933 1 1807 1 9869 1 4358 1 13583 1 13447 1 10223 1 12624 1 remake 3701 1 1180 1 12995 1 6655 1 10361 1 7239 1 byron 5841 1 10213 1 2797 1 2843 1 2343 1 7722 1 carangid 12783 1 7657 1 2089 1 7332 1 5054 1 13281 1 uncorrupted 16344 1 4158 2 9340 1 13648 1 8293 3 9006 2 10097 2 3483 2 9992 2 16330 1 12135 1 8084 1 unreliability 5736 1 99 1 harass 1781 9 7402 1 14691 1 12315 1 5654 2 13130 1 12041 1 7387 1 15195 1 8881 1 7836 1 3375 1 13371 1 2451 1 13191 1 15308 1 4338 1 2873 1 8391 1 12529 1 9926 1 9658 1 2027 1 5778 1 2320 1 6003 3 14613 1 6217 1 486 1 8992 1 6241 1 2029 1 14199 1 1607 3 2633 1 6716 1 12597 1 12260 1 15815 1 6747 1 12795 1 13381 1 8946 1 8651 1 9918 1 13896 1 13914 1 takeover_target 6854 1 3082 1 11282 1 15519 1 971 1 702 1 107 1 10068 1 1389 1 1976 1 10627 2 1787 1 1169 1 unripe 11549 1 14959 1 13708 1 13967 2 8338 1 1780 8 10310 2 9635 1 5718 1 9115 1 9186 1 15453 1 3258 1 12059 1 14137 1 10461 1 15434 1 1907 1 14196 1 360 1 1932 1 15940 1 5295 1 12246 2 10321 1 9210 1 12192 2 151 1 13859 1 3992 2 15267 1 5179 1 10141 1 16329 1 12809 1 1428 1 10326 1 9081 1 1155 1 7904 4 3684 2 13649 1 14520 1 7776 1 90 1 15875 1 4365 1 11474 1 8581 1 order_gaviiformes 3302 1 frequency 5442 1 11491 1 9494 1 5443 2 5896 3 7879 1 11691 1 6900 1 13552 1 4950 5 15695 1 4182 1 7439 2 3839 1 4057 1 9365 1 6962 1 7507 2 16080 1 13228 2 5584 1 10618 3 12889 1 9153 1 2329 1 5323 2 9429 3 6710 1 3404 2 10698 1 9021 1 10485 1 13931 3 10030 6 4531 1 12172 2 9705 1 9704 1 4648 2 6279 1 5328 1 14464 1 5271 1 12500 1 7133 1 11156 1 4789 1 11362 1 4790 1 14391 1 6596 2 2634 1 2496 2 2202 1 1208 1 7314 1 12434 1 1882 1 906 1 15772 1 10628 1 10430 2 5022 1 473 2 11222 1 2129 4 2131 1 1949 1 1602 4 10495 1 10097 1 11373 2 3120 1 721 1 4907 4 8104 1 11229 1 589 1 10302 1 16399 1 11716 3 9305 1 14868 7 5103 1 6664 1 14010 3 8110 1 5178 1 1166 1 11035 1 1893 1 10442 1 9912 5 5729 1 9585 1 9728 3 13764 1 14147 1 5549 1 924 1 14018 1 8590 1 1562 1 4014 1 9048 1 8726 1 2074 1 12718 1 4740 1 9527 1 14744 1 12202 1 12393 2 5735 1 2300 1 12077 1 14750 1 1490 2 12848 2 15089 1 6107 3 12145 2 11728 4 12721 1 10664 3 13903 1 6736 1 5988 1 5616 1 2466 9 15731 1 11188 1 611 1 10253 2 4925 1 10867 2 673 1 11657 1 5128 2 13784 1 2151 1 13064 1 994 3 8123 1 11327 4 10192 1 16049 4 9400 2 9063 1 16052 1 11596 1 5131 1 15106 1 7983 1 9195 1 15933 1 6747 2 12020 1 14828 2 9860 2 9539 2 1775 1 8997 2 7800 9 6374 1 938 2 7486 2 9337 1 14095 1 12026 1 1635 2 4164 2 4933 5 557 1 12677 1 1782 65 4760 3 7738 2 10816 1 11878 3 11197 1 15744 2 2678 1 8871 1 2422 1 9868 1 14693 1 3082 1 5248 1 13273 1 4446 1 7006 1 5888 1 5056 1 7995 1 2478 1 1012 3 9079 1 13218 1 8076 1 4169 1 3667 1 6812 1 3381 2 13011 1 1136 1 4830 1 14702 1 7501 1 6455 1 10214 1 10344 1 16259 1 6763 4 7815 2 1082 3 15035 2 4177 1 7623 3 experiental 5599 1 7189 1 12448 1 219 1 intersection 3749 1 5442 4 13184 2 11429 2 9527 2 5770 1 4368 1 9735 1 14975 2 4155 1 16321 1 8853 1 1490 1 5926 2 7210 4 1568 4 11461 1 12145 1 1395 1 11728 1 12721 1 5075 1 13460 1 15827 3 8120 1 15898 1 5041 1 3874 1 12015 1 14885 1 7856 2 13354 1 2026 3 3404 2 8886 1 2263 1 6592 1 1148 1 16481 1 9705 1 1034 1 7189 1 11085 1 745 1 5271 2 8246 5 14583 1 3954 1 5458 2 14585 2 10128 1 373 2 6747 1 8021 1 14428 1 7800 1 12569 2 8408 1 9664 1 1975 1 10131 1 14360 1 7733 1 12639 1 12827 1 7169 1 11410 1 4659 1 7421 2 1577 1 6059 1 9510 1 15213 2 10812 1 4312 1 10556 1 7218 2 9772 1 1783 24 3258 2 10231 1 9033 1 9034 1 13690 1 4031 1 718 4 4734 1 9722 1 11882 1 8139 1 3896 1 8874 1 16436 1 10403 1 8367 1 5689 1 8292 1 15940 3 11063 1 2945 1 6125 1 1014 1 6230 1 15120 1 14010 1 11110 1 13893 1 8875 1 15942 3 10442 1 14838 1 9912 1 1894 1 13514 1 7010 1 14181 2 975 1 1169 2 15423 1 9354 1 3158 8 9587 1 7260 2 14044 1 13949 1 10018 3 2826 4 dexamethasone_intensol 643 1 14251 1 15918 1 15251 1 15736 1 531 1 12197 1 enkaid 15918 1 5878 1 8449 1 10503 1 7792 1 13674 1 13919 1 7390 1 idyll 7597 1 5555 1 2115 1 13674 1 8555 1 1413 1 7374 1 5506 1 16108 1 13694 1 16346 1 5068 1 6367 1 1188 2 mumps 2542 1 6498 2 1 1 6389 1 9009 2 13918 1 spiers 12731 2 7068 1 12567 1 4220 1 4532 1 4099 1 14242 1 3544 1 12326 1 3501 1 227 1 parturient 13986 2 9022 1 8110 2 6427 3 addendum 487 1 5621 1 hamlet 10415 1 3239 1 2989 1 2205 1 8591 1 11876 1 4661 1 4499 1 4180 1 13674 1 15896 1 11693 1 2804 1 16195 1 4264 1 12534 3 5506 1 10418 4 1786 9 3401 1 6655 1 11081 1 1459 2 12188 1 11483 1 12270 4 4998 2 3406 1 9021 1 4999 1 7230 1 5967 1 2151 1 15441 1 9176 1 12563 1 11067 1 14473 1 8632 1 594 1 7903 1 10804 1 11282 2 14828 1 3903 1 12023 1 556 1 1861 1 doom 905 1 1248 1 6298 1 4876 1 7173 1 4486 1 15729 1 1785 7 12206 1 14482 1 8140 1 1952 1 16398 1 14980 1 1807 1 8040 1 1514 1 5243 1 14128 1 14527 1 9944 1 5001 1 2031 1 13380 1 16036 1 3140 1 11830 1 13431 1 15034 2 5531 1 14339 1 9109 1 4235 1 748 1 peridotite 7634 1 15219 1 9097 1 11312 1 3992 1 2855 1 urinary_tract_infection 7661 1 12732 1 230 1 16417 1 8939 1 5766 1 5694 1 8192 1 9457 1 5033 1 11308 1 candlewick 16122 1 2062 1 12821 1 2136 1 3269 1 7777 1 management 7015 1 387 1 13668 1 14052 1 3242 1 14192 1 13551 1 3 1 1939 1 1145 7 14055 1 12356 1 13674 1 13676 1 10353 1 1395 1 11147 1 15897 2 14979 1 11820 2 8750 1 12234 1 10899 2 14060 2 7509 1 2961 1 4529 1 9429 2 6530 1 3404 3 5899 1 9021 1 5383 1 10619 2 8514 1 14716 1 9705 1 265 1 14719 3 897 2 8021 2 267 1 13684 1 11697 1 1667 4 14 1 5390 1 13434 1 13171 1 6535 1 527 1 10361 1 10627 2 6776 1 905 1 8285 1 10363 1 3113 1 1543 1 3924 1 6032 1 9716 1 12905 1 13833 1 7141 1 4537 3 16280 1 7031 1 9719 2 6909 1 4539 1 3928 1 2062 2 3559 1 12909 1 7252 1 4907 1 13576 1 3262 1 1950 1 11312 1 5176 1 536 1 1553 1 652 1 11172 1 7774 1 15157 4 11033 1 4201 1 12248 1 8414 1 13698 3 13842 1 10643 1 278 1 7649 2 10106 1 15656 1 2214 1 12003 2 6426 1 1169 1 15423 1 11039 1 8180 1 1413 1 16177 1 13844 1 4422 1 1055 1 6161 1 4087 1 11584 3 9731 1 3436 2 2989 3 663 1 5924 1 10789 1 15790 1 5412 2 9594 1 14088 1 12786 1 6168 1 7539 2 15667 1 6925 1 3705 1 4925 1 8314 2 15441 5 6309 1 4562 1 16051 3 2747 1 790 1 4928 1 1180 1 8799 1 15570 1 8435 1 9860 1 8436 2 15448 2 10395 1 15674 1 1976 1 16425 1 4933 1 9342 1 11476 1 15305 2 15677 1 9203 2 5053 1 4110 1 14905 1 8323 1 12156 1 3596 1 14771 1 306 1 8192 1 10006 1 12940 1 3022 1 8558 1 14500 1 15458 1 15941 2 10140 1 14374 1 5203 1 10143 1 1450 1 10942 1 8449 1 4706 1 15191 1 8818 1 13608 1 1990 1 700 2 9493 1 8198 1 5957 1 2488 1 7569 1 573 1 2385 1 9359 1 702 1 2114 1 4950 2 330 1 7302 1 13490 2 13745 1 2624 1 6962 1 13228 1 1728 1 575 5 5964 1 4338 6 15046 1 15829 2 5702 1 9629 1 5841 1 2266 1 10956 1 4840 1 3992 2 2268 1 15831 2 8965 1 15833 1 15330 1 1595 1 3617 1 9234 1 6969 1 12568 1 3327 1 12569 1 8972 1 4598 1 9897 1 3852 1 6835 1 4250 1 4466 1 11773 1 4847 2 15840 1 7316 1 5093 1 10556 1 10705 1 5718 1 10559 1 12059 1 2641 1 720 1 13371 1 14003 1 13888 1 6099 1 971 1 1099 1 7838 1 13374 1 8221 1 7839 1 7455 1 11108 1 2405 1 6230 1 1478 1 484 3 14010 2 5978 1 15221 1 8223 2 8718 2 5104 1 846 1 3506 2 15077 2 15968 1 6359 2 14016 6 12068 2 9787 2 2652 1 8721 1 1611 1 14815 1 7333 1 15348 1 9920 2 3061 2 8593 1 11519 1 3063 1 4487 5 15089 1 2658 1 15729 1 3874 5 1762 1 362 1 8604 1 5240 1 9926 1 3071 1 13390 1 10867 2 8732 1 16478 1 993 1 1362 1 8382 1 12330 1 11403 1 13260 1 6498 1 7607 5 6745 1 12994 1 9400 1 8246 1 11406 1 16483 1 9663 2 16484 2 10068 1 2540 1 14956 3 1635 1 2161 1 16489 1 11133 2 12220 1 13801 1 3082 1 2170 1 3223 1 11954 2 3764 1 9546 2 508 1 4502 2 5756 1 3766 1 1787 71 9006 1 1014 1 10598 3 4169 3 14838 1 3382 2 14181 1 5512 1 629 1 10756 1 6261 1 11419 2 6389 1 8876 2 15251 1 10343 1 6129 1 15519 1 12489 1 145 1 14047 1 1389 1 12620 2 dories incorrigibly 8680 1 13691 1 12272 1 13626 1 8696 1 9397 1 hobson 1617 1 13893 1 9793 1 334 1 3971 1 8769 1 2824 1 1054 1 6238 1 uncoupled 3395 1 1192 1 14675 1 916 1 cherry_tomato 15576 1 877 1 9950 2 saiid 6629 1 8662 1 10465 1 7799 1 3699 1 8769 1 frederick_the_great 8845 1 15294 1 13035 1 6372 1 10868 1 8663 1 11686 1 11512 1 4155 1 6834 1 decrease 14053 1 15996 4 5264 1 13552 2 13422 1 15636 2 3676 2 4399 1 1804 2 12889 1 9565 1 2329 1 4529 1 892 1 9429 1 14062 1 2440 1 9021 1 263 1 14579 1 265 1 4648 1 6279 1 11824 1 14464 1 266 1 7133 1 4900 1 12764 1 14585 1 768 1 641 1 8408 2 11307 1 3258 1 531 4 16023 1 779 1 7146 2 8032 2 13837 1 7037 1 12910 1 4907 1 3425 1 16028 2 12245 1 11571 1 12512 1 26 1 11312 1 14597 1 13445 1 13177 1 15918 3 7527 1 1680 1 4203 1 15422 1 5033 2 12383 1 8180 1 9587 1 9728 2 4422 1 12260 1 10786 1 9978 1 2594 1 5671 1 14341 1 3573 1 12393 3 9051 1 8183 1 15790 1 8051 1 4809 1 6168 1 1419 1 12145 1 7159 1 1964 1 11729 1 8309 1 12396 1 9055 2 10252 2 2466 1 7665 1 14348 1 9599 1 4925 5 13064 1 14352 4 6799 1 9601 1 11327 1 10388 1 7792 1 14236 1 3288 1 1067 1 11870 1 5942 1 9195 4 5564 2 8668 1 15447 4 15572 4 5299 1 6941 1 13865 1 9477 1 11878 1 15676 1 6690 1 12032 1 11742 1 11884 1 8323 1 11339 6 6323 1 12943 2 5689 2 13076 8 8559 1 9874 1 3158 1 6576 1 6328 4 14251 1 5955 1 5065 1 8449 19 11896 6 5694 7 6070 1 1082 1 10018 1 9493 1 5832 1 9494 1 4944 4 14254 1 13741 1 12554 1 12300 1 15470 1 2257 1 15695 1 7302 1 11076 1 13490 1 9365 2 8202 1 5965 1 2389 1 15049 1 5323 1 10030 1 15831 1 12050 1 7189 1 2271 1 4342 1 9368 1 15056 1 10036 1 13099 2 12825 1 6596 2 1093 2 5089 1 9767 2 11773 1 11222 1 11775 3 7585 1 11369 1 1475 1 3189 1 8361 3 6473 1 14403 1 7711 1 344 1 585 7 13888 1 1348 1 15342 1 12579 1 4476 1 8841 1 10174 1 14010 2 484 4 4479 1 15077 1 13121 1 6232 1 14147 2 3346 1 12716 1 13897 1 3058 1 14286 1 9920 2 2519 1 15725 1 1225 1 984 1 8853 1 4156 1 115 1 13903 1 11395 1 6736 2 3874 1 10988 1 9394 1 5616 1 16237 1 5240 1 5128 2 4160 1 16479 1 6373 4 11128 1 13532 1 7103 1 10325 1 2155 1 5993 1 15513 2 2306 1 13263 1 8997 2 10879 1 2159 1 13264 1 3884 1 1635 1 1004 1 15108 1 1924 2 11672 1 10741 4 4761 1 12606 1 10076 1 9546 1 12609 1 4502 1 3766 1 6384 1 3531 1 10465 1 1788 73 13150 1 10211 1 7009 2 14181 1 5366 2 11684 3 6636 1 14313 1 7122 1 6389 1 15251 1 8394 1 15519 1 11686 1 1521 1 16259 1 1389 2 16372 1 13814 1 5893 1 in_the_long_run 16332 1 4466 1 4426 1 5279 1 6525 1 12592 1 4877 1 14592 1 15305 1 14494 1 5353 1 9116 1 779 1 3534 1 11849 2 14495 1 15933 1 11686 1 5957 1 gulf_of_bothnia 13993 1 14334 1 505 1 1169 1 outdoorsy 2384 1 8869 1 3267 1 13674 2 12848 1 cake 6011 1 7752 1 7367 2 9819 1 4775 1 1794 1 10473 1 8626 1 2698 1 5264 1 12622 1 13422 5 3676 2 16148 2 3403 2 16150 1 14855 2 14980 1 5016 1 7631 1 10 1 15267 2 9024 1 16008 2 8631 1 7514 2 5271 4 5528 1 6535 1 15769 2 5169 1 11022 3 9830 1 4659 1 10492 1 9964 1 3793 1 10363 1 14209 1 13036 1 777 1 4193 1 13689 1 10906 1 8901 1 646 1 9167 4 276 1 12131 1 3798 1 653 3 13698 1 9582 1 4203 1 9444 1 1049 1 8296 1 10375 1 9728 1 5035 1 7904 2 11178 1 14338 1 7393 1 12656 1 13705 1 1685 1 1834 1 3699 1 11584 1 2986 3 3269 1 5551 1 2861 1 1960 2 12524 2 7658 1 4092 2 12785 1 5188 2 4554 1 930 1 15171 1 4430 2 13461 1 13583 1 3280 1 293 1 7667 1 11593 1 12270 1 15012 1 9057 5 16047 1 2229 1 16050 1 12147 1 9062 1 4102 1 4103 3 12795 1 5298 9 9064 1 10392 1 9467 1 10131 1 11474 1 16303 2 179 1 8438 1 7421 2 557 1 11735 1 9745 1 3956 1 62 1 12154 1 14494 1 11882 2 10683 1 14771 1 8678 1 5201 1 4574 1 11339 1 9077 1 6452 1 15028 5 15940 1 12944 2 2245 1 13076 1 13736 2 67 1 12809 4 11204 2 10942 1 16073 1 15943 1 1328 2 4579 2 8450 1 4233 1 6071 1 8948 1 14255 1 6334 1 14507 1 73 1 15194 1 8338 4 15948 1 6204 1 13742 1 3983 1 16078 1 3171 1 10416 1 13745 1 12959 1 10954 1 1087 2 5080 1 5587 2 16328 3 6592 2 11495 1 4957 3 8345 1 4958 2 6220 1 12567 1 12569 1 1596 1 8461 1 8581 1 4845 1 11771 1 15062 1 6350 1 7316 1 13368 1 16335 1 12183 1 8361 1 12059 1 11102 2 719 1 11923 2 15217 1 13372 1 15339 1 12708 1 9639 1 6230 1 14009 1 11512 1 8980 1 10176 2 16104 9 4480 1 15223 1 14146 1 8845 1 5980 1 14018 1 1357 1 13897 1 15724 2 5984 1 5733 1 14675 1 4867 1 2019 4 2020 1 6732 1 12586 1 7601 1 2802 1 8237 1 2658 1 9116 1 12720 1 7473 3 15976 1 3875 4 2806 1 10866 2 743 2 12332 1 13786 1 11662 1 9662 1 8996 1 13649 1 12861 1 16358 6 7731 2 4994 4 1635 1 6378 1 1924 1 3371 1 13796 1 10742 4 10333 1 5505 1 9411 1 7224 1 132 1 15617 2 1009 1 16139 1 5756 1 2945 1 9680 3 12097 1 13010 2 16258 1 1516 1 14838 1 1789 83 14181 1 11278 1 16494 1 7746 1 7120 5 9946 3 2953 1 6762 1 1656 1 14047 3 plate_tectonic_theory 421 1 11118 1 10983 1 7218 1 4361 3 10561 1 1641 2 15729 2 7574 1 7440 3 2921 1 2560 1 10356 1 9103 1 7350 1 atomizer 9219 1 2646 1 2581 1 4906 1 9246 1 4714 1 nipple 7421 1 4535 1 906 1 13668 1 12866 1 13669 1 1738 1 4260 2 8595 1 716 1 12342 1 6080 1 3171 1 4664 1 12969 1 3931 1 12091 1 14679 1 11493 1 14121 1 12186 1 15401 1 8962 1 2026 1 7774 1 10282 2 2263 2 8086 1 3072 1 14439 1 14888 1 16008 1 15422 1 9661 1 13718 1 3487 1 229 1 1790 9 8180 1 14945 1 4707 1 11441 1 10411 1 6535 1 6375 1 11474 2 3142 1 yucatec 12545 1 10070 1 terminally 7810 2 472 4 7048 1 4840 2 1279 1 3367 1 3242 1 8940 1 12735 1 1145 1 1282 4 3997 1 15077 1 13422 1 3157 2 1791 7 10214 1 4437 1 10998 2 4281 1 12416 1 11017 1 3153 1 15271 1 8645 7 11571 1 13482 1 10544 1 13249 2 appellant 214 1 16164 1 5586 2 4466 1 3032 1 14328 1 11757 1 4041 1 8044 1 14052 1 9663 1 12543 1 6004 1 13063 1 13947 1 13338 1 12059 1 nuptial 9279 1 16035 1 297 1 6351 2 10264 1 4729 1 13604 1 12011 1 2453 1 15935 1 tippecanoe 8166 1 9279 1 9926 2 15466 1 7970 1 9793 1 1310 1 3396 1 6630 1 11830 1 6579 1 284 1 4138 1 chemiluminescent 8509 1 13814 1 candida_albicans 3200 1 16026 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5499 1 14428 1 10068 1 10070 2 1635 1 15987 1 12733 1 14035 1 235 1 12735 1 8870 1 1129 1 12606 1 14693 1 12091 1 13405 2 3222 1 13804 1 3223 1 9546 1 1644 1 508 1 7226 1 1649 1 1381 1 16491 1 1382 1 13407 1 7361 9 12485 1 16494 3 12102 1 2824 1 6763 1 6893 1 7014 1 3238 1 chorister 4955 1 13583 1 6375 1 2444 1 8892 1 13526 1 reed_pipe 7799 1 11430 1 4588 1 4998 1 relapse 2196 1 12249 1 16387 1 7254 1 nationalities 3531 1 3685 1 11142 1 14053 1 12416 1 15225 1 766 1 3996 1 5858 1 4907 1 16158 1 16300 1 evening_primrose 8702 1 5638 1 englishman 13505 1 10363 1 7367 1 12524 1 1794 5 13551 1 12077 2 8530 1 10475 1 4110 1 3676 1 535 1 14247 1 16187 1 13167 1 7408 1 1087 1 5322 1 1362 1 15782 1 11512 1 3483 1 9582 1 1789 1 13408 1 5271 1 5528 1 16494 1 5002 1 11803 1 12861 1 13434 1 14740 1 11180 1 for_sure 9830 1 10809 1 573 1 9833 1 4759 1 6145 1 14494 1 406 1 443 3 7593 1 10411 1 13517 1 2920 1 5190 1 8829 1 14109 1 15594 2 5240 1 political_system 5442 1 9830 1 13034 1 5372 1 8170 1 2989 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3270 1 5923 1 7657 1 5555 1 8047 1 9186 4 4917 1 5556 2 12010 1 12011 1 1568 1 12786 4 6302 1 5188 1 15792 1 16411 1 12266 1 6169 1 5189 1 13583 3 12017 1 2465 1 293 5 14347 1 7410 1 6925 1 12270 1 16301 1 3444 1 12791 1 11189 1 11594 8 4559 1 10511 1 9328 1 11327 1 14616 1 2084 1 15297 2 5422 2 8434 2 15299 1 16055 1 447 4 15445 1 1845 1 15573 1 15181 1 6801 2 11474 4 7421 1 4933 1 8319 1 13726 1 14623 2 9067 1 13968 1 13970 2 1709 1 7683 1 15026 2 14365 1 9206 1 953 1 2244 4 14370 2 15028 1 4829 2 4698 3 8679 1 3962 1 9210 1 13076 1 15311 1 13734 1 10142 1 311 1 6814 1 2613 1 11203 1 9875 1 16314 2 16439 1 1583 1 16440 1 6700 1 4579 1 12546 1 7435 1 9216 1 2111 1 1457 1 4238 1 10538 1 9760 1 329 1 13086 2 4588 1 11619 3 14921 1 7574 3 5449 1 6965 2 15588 1 2261 1 3613 1 8458 2 8702 2 2263 2 13356 1 5455 1 8343 1 5083 4 3992 2 5213 1 3033 1 11217 3 3487 1 3998 1 7581 1 13878 1 6969 1 3618 2 5849 2 11767 1 9238 1 15210 1 1467 3 8581 1 1343 1 4468 1 8094 2 16459 2 15213 1 10704 4 15840 1 9635 7 8359 2 5718 1 2128 4 7711 5 12968 1 11227 2 6840 1 14002 1 7587 1 15487 1 9103 1 3501 2 8366 1 6977 3 211 2 7457 1 16465 1 1893 5 3629 1 1104 1 6727 1 2921 8 16231 1 8851 1 5984 1 1487 4 13900 1 9922 2 1755 2 12981 1 10310 4 1756 1 9115 1 12075 1 1613 2 4263 4 12847 2 9259 1 15973 2 5346 1 14027 1 5347 5 2529 1 3354 9 5126 1 988 1 1231 1 10727 1 8241 1 2024 1 16236 2 9799 1 7976 1 10321 1 13391 1 15235 1 16481 1 16124 1 1624 1 11256 2 11129 1 6746 2 3760 18 15872 1 8734 1 13395 1 9932 1 2812 1 7731 3 12862 3 13143 2 14830 1 2035 1 7984 1 5502 2 7350 10 6377 1 1370 1 13004 1 13652 1 12342 1 14555 1 5505 1 5136 1 2312 1 8738 2 11411 2 14175 1 9673 1 5754 1 12608 1 8259 1 1645 1 5361 2 7007 3 14961 1 4167 1 7499 1 4169 2 7618 1 7360 2 15249 1 7744 1 6890 1 1515 2 14838 1 6515 1 4387 1 9945 2 15753 2 15253 1 14047 1 14972 2 isomorphic 11327 1 12192 1 13967 1 5407 1 vote_in 12216 2 8302 1 2324 2 1798 7 7239 1 16189 1 9891 1 7774 2 5042 1 8325 1 13045 1 12397 1 12253 1 14719 1 12003 2 2214 1 8844 1 15077 1 14738 1 12962 4 11381 1 7132 1 14236 1 1517 1 2888 1 14570 1 9826 1 8021 1 3534 1 15519 1 12569 1 6801 1 15538 1 748 1 cobia 10766 1 1261 1 3282 1 13878 1 2089 1 13712 2 jurisprudence 4466 3 16455 1 14053 1 10629 1 4537 1 7851 1 4616 1 5412 4 7302 1 719 1 15897 1 1800 10 6865 3 14626 2 15093 2 10921 1 9520 1 3404 1 3679 1 7789 1 8718 1 14889 1 12237 1 7459 1 7858 1 794 1 15191 1 10343 5 16484 1 1667 4 15445 1 11017 1 14018 1 1328 1 12569 1 11321 1 elecampane 3760 1 11956 1 254 1 round_of_golf 7799 1 11461 1 1519 1 4762 1 3113 1 inhaling 350 5 16263 3 10757 2 6731 1 9388 1 2299 1 8156 1 10223 1 8565 1 8628 3 13823 1 8309 3 8571 1 1803 20 120 1 16082 1 5240 1 1589 1 12853 1 10421 1 3072 1 1088 1 262 1 10387 1 2746 1 8516 1 7189 2 15736 2 8890 1 11327 1 14066 1 15056 1 442 1 3288 1 4246 1 3006 1 8021 1 2335 1 12599 1 9897 1 13298 1 1406 1 9343 1 11987 1 13401 1 8531 1 14274 1 1602 1 2641 1 779 3 12739 1 10999 1 15880 1 5819 1 9076 1 4146 5 4039 1 2646 1 8939 1 2070 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7657 1 7565 2 assassination 3725 1 2526 1 14879 1 7300 2 14319 1 3839 2 12721 1 11391 1 3780 1 1801 6 10899 1 4529 1 15532 1 10385 1 14718 1 10323 1 8631 1 1401 1 5388 1 15445 1 2749 1 6030 1 1737 1 12735 1 16094 1 944 1 1784 1 205 1 10001 1 3826 1 1741 1 13806 1 410 1 15071 1 3531 1 13444 1 12376 1 5177 1 10139 1 2479 1 16031 1 16438 1 15459 1 13150 1 5228 1 8942 1 4701 1 1607 1 8180 1 8845 1 12617 1 16495 2 14338 1 9976 1 10574 1 5317 1 540 1 3059 1 13312 1 15688 1 3671 1 dot_matrix 16178 1 14460 1 12534 1 2989 1 16486 1 soapwort 12508 1 8886 1 10554 1 15404 1 211 1 9922 1 4238 1 6287 1 5422 1 1560 1 6516 1 2436 1 9206 1 2970 1 7350 2 genus_scabiosa demode 11279 1 2513 1 16492 1 14626 1 5888 1 6981 2 11886 1 adrenergic 12393 1 531 2 61 1 12606 2 9055 3 12609 1 5616 1 1804 5 2329 1 6384 1 15918 3 5878 1 1788 2 6799 1 13532 2 14582 1 10388 2 7792 1 13121 1 14487 1 5366 1 9587 1 14251 1 15513 3 14970 1 15251 1 11686 1 10786 1 13897 2 6147 1 supplying 5442 1 10085 1 7755 1 13552 1 2896 1 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4110 1 9869 1 11742 1 8192 1 12940 1 2882 1 1982 1 12034 1 12410 1 13075 1 5436 1 2760 1 5311 1 2613 1 13081 1 11749 1 6328 1 11896 5 8449 1 14636 1 11490 2 15688 1 2111 2 7567 1 5443 1 1333 1 15318 7 12693 1 2114 1 10695 1 330 2 7301 1 11908 1 13490 1 7574 1 12043 2 5076 1 334 1 5839 1 11081 1 10421 1 8458 1 2263 3 6711 1 3032 1 4957 1 7189 1 9368 1 6715 1 10840 1 8349 1 6089 2 10033 1 3617 1 8091 1 8209 3 7312 1 2394 1 1596 1 9094 1 8829 1 1210 1 4464 1 10292 1 16332 1 8093 3 16455 1 8213 1 9097 1 12437 1 4847 4 5718 1 7585 1 10559 2 8361 10 13110 1 6473 1 585 3 719 1 15846 1 14275 2 11105 1 15340 1 972 1 13116 1 15849 1 3741 1 14010 2 484 2 11512 1 10970 1 13761 1 5108 1 14147 1 7593 1 9787 1 14018 1 8849 1 4363 1 3633 1 11789 1 2921 1 2016 1 9384 1 11939 1 3059 5 9527 2 12324 1 2298 1 353 1 219 3 6984 1 7095 1 9793 4 15230 1 2525 1 4156 1 1490 1 4263 1 860 1 5486 1 5347 1 6865 2 4745 1 2662 1 10058 1 2149 1 5240 1 9926 1 5128 1 3359 1 1621 1 6241 1 16356 1 13912 1 9662 1 10325 1 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13574 2 9374 1 10775 2 1949 1 11572 1 15918 2 13698 1 5978 2 10106 1 8418 1 15656 1 8180 1 9587 2 13247 1 4424 1 9920 1 10181 1 12324 1 8047 1 6611 1 6794 1 10247 2 10503 3 15286 1 7851 1 5412 3 11523 1 8855 1 1171 1 8309 1 12590 1 2927 1 6172 1 8732 1 10867 2 1969 57 13531 1 866 1 6498 1 15441 1 9400 1 3813 1 12400 1 13648 1 2366 1 1239 1 9663 1 12795 1 2086 1 746 1 10879 1 2815 1 4689 1 6178 1 16192 1 1246 1 9609 1 620 1 12735 3 7218 1 10884 1 7488 1 12534 1 4110 1 3223 1 508 1 9546 1 9074 1 8192 1 4502 1 1510 1 8140 1 5689 1 9679 1 8558 1 1649 1 10598 1 4321 1 1015 1 13150 3 14440 1 11201 1 14969 1 629 1 15251 1 4281 2 12416 1 8946 1 3236 1 12353 2 6580 1 11612 1 9493 1 2111 2 equisetales 14596 1 8879 1 10221 1 16365 1 13712 1 inoculation 15310 1 4933 1 12795 1 8115 1 12924 1 16356 1 bunting 11956 2 2263 1 3176 1 3661 1 6379 1 6409 1 1970 9 1621 1 7360 1 6742 1 8328 1 6287 2 1558 1 4263 1 7711 1 16314 2 16078 1 2190 1 12653 1 1509 2 1909 1 2869 2 1889 1 oxlip 918 1 8021 1 2198 1 11017 1 1462 2 1510 1 free_state 12361 1 1635 1 2404 1 5184 1 7729 1 7789 1 11512 2 13408 1 8502 1 16117 1 2364 1 5271 1 12059 1 1180 1 16494 1 16177 1 11728 1 2399 1 14712 1 9891 1 1834 1 536 1 6835 1 14667 1 downlike 6449 1 3492 1 10450 1 corymb 5906 1 10704 1 14606 1 3258 2 3623 1 7711 1 15973 1 1709 1 6207 1 13985 1 14196 1 7007 1 4529 1 6977 1 3123 1 15685 1 13462 1 7900 1 1971 6 3760 2 7904 1 8021 1 6535 2 9216 1 1403 1 6801 1 11474 1 daedalus 905 3 4030 2 3329 2 8319 2 1766 1 10824 2 13003 2 1502 2 1924 2 12966 1 8942 1 12383 1 10905 2 3305 3 6685 1 7012 1 10901 1 9235 1 12020 2 8386 2 8033 2 14941 2 7697 2 11081 2 3010 1 negative_correlation 12537 1 10698 2 5896 2 2989 1 144 2 2572 2 9605 1 14318 1 dippers 9945 1 3703 1 11227 1 8663 1 equalize 1635 1 4319 1 2928 1 14714 1 7802 1 7527 1 16437 1 4333 1 7607 1 1282 1 14989 1 9409 1 15077 1 14181 1 2658 1 1642 1 8468 1 13411 1 15225 1 761 2 2634 1 6071 1 10131 1 6178 1 1999 1 complain 9979 1 1331 1 7566 1 7399 1 5344 1 9881 1 12457 1 12229 2 5072 1 9983 1 1939 2 2300 1 6958 1 15502 1 12011 1 13743 1 15790 1 9557 1 4808 1 15505 1 15436 1 1693 1 11553 1 10797 2 7340 1 3280 2 2562 1 9021 1 3679 1 2415 1 5561 1 2122 1 6241 1 1915 2 14928 1 16356 2 11406 1 1972 23 10161 1 12795 1 1734 1 12432 2 14587 1 15 1 7673 1 1596 1 2394 1 5089 2 3008 1 10928 2 11733 1 13034 1 8169 1 10363 1 8552 1 529 1 10264 1 13035 1 9297 1 10265 1 11674 1 4996 1 7488 1 8738 1 944 1 9350 1 9071 1 8192 1 12610 1 5888 1 14525 2 11230 1 2759 1 11002 1 14009 1 484 2 11680 1 2690 1 9583 1 12002 1 10340 1 2214 1 9915 1 12102 1 15497 1 7393 1 4013 1 6520 1 15856 1 2593 1 2142 1 11287 1 usnea 6133 1 8894 1 15404 1 11242 1 15297 1 pickwick 168 1 14975 1 13283 1 1099 1 4737 1 6053 1 12391 1 15459 1 yea 2690 2 4551 1 polymorphous 15228 1 2326 1 3684 1 4907 1 13171 1 11327 1 4487 1 echeneididae 5096 1 7369 1 1457 1 cleveland 16494 1 16419 1 1604 1 14971 1 934 1 dupe 4967 1 5872 1 2846 1 11235 4 1924 1 13171 1 5068 1 1209 1 14873 1 2836 1 5085 1 10874 1 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11414 1 6801 1 combust 14071 3 14314 2 14226 1 10824 1 14149 1 11602 2 1979 8 11349 1 13799 1 10223 2 5836 1 8628 1 9558 2 5570 1 6080 1 8556 1 4411 1 8101 3 7697 2 3703 1 12346 1 10798 1 2997 1 2490 1 4433 1 12157 1 10421 1 5778 1 15419 1 3663 1 5417 1 7307 1 3000 1 10323 1 10838 1 6241 1 6345 2 15189 1 13765 1 11894 1 8946 1 13519 1 15181 1 6894 2 5995 1 ross_sea 5235 1 14584 1 16126 1 11118 2 2263 1 16004 1 4140 1 13089 1 884 1 6239 1 7408 1 5271 1 1558 1 reckless 7969 1 8726 1 10578 1 5185 1 12693 1 8156 1 1027 1 860 2 14455 1 6019 1 4813 1 2225 1 5873 1 13614 1 14950 1 3136 1 9459 1 5778 1 5214 1 10511 2 16241 2 10062 1 6873 1 5271 1 6594 1 6145 1 5679 2 8435 1 4247 2 13684 1 2307 1 6146 1 8761 2 10068 2 12572 1 1633 2 11701 2 12240 1 10331 1 6059 1 2907 1 3145 2 5597 2 13795 2 683 1 1980 17 8529 1 13799 1 5053 1 950 1 11103 1 10851 1 6002 1 3022 2 1014 1 11002 2 2760 2 10175 2 1382 1 1134 2 11545 2 8392 1 12003 1 5480 1 8180 1 10215 1 7260 1 14503 2 15251 2 4707 2 8651 1 2797 1 7621 1 730 1 11286 1 gullible 6457 1 2918 1 7643 1 6890 1 11679 1 8156 1 3461 2 8325 1 10874 1 11081 1 16300 1 10821 1 13626 2 falling_out 1014 1 11824 1 13099 1 8110 1 227 1 milligram 15228 1 7015 2 6855 1 9859 1 14913 1 16438 1 10473 2 15311 1 3744 1 9451 1 5264 1 14181 2 4652 1 8449 1 11605 1 15572 1 12228 1 izanami 2009 2 505 2 2326 3 12102 1 14941 1 10701 2 2887 1 discomycetes 3404 1 14499 1 16478 1 3200 1 11730 1 1510 1 5361 1 tinware 14429 1 round_arch 15940 1 718 1 11982 1 3546 1 4734 1 71 1 13895 2 1134 2 9860 1 15788 1 lancet_arch 4040 1 10628 1 2796 1 wash_away 9714 1 15310 1 13034 1 4040 1 9021 1 1513 1 4561 1 3525 1 4957 1 9219 1 5032 1 8627 1 12318 1 5288 1 6261 1 10664 1 1357 1 6509 1 5694 1 2854 1 6520 1 6720 2 972 2 2159 1 3237 1 4446 1 9742 3 2490 1 6925 1 crack_shot 941 1 4998 1 11927 1 14935 1 12393 1 9359 1 14462 1 6667 1 8135 1 1595 1 6760 1 784 1 16494 1 2865 1 6105 1 14466 1 16484 1 12608 1 13461 2 3173 1 9920 1 cardiomegaly 14462 1 9587 1 11739 1 14759 1 8021 1 12374 1 takeover_attempt 2389 1 13445 1 11119 1 14053 3 2213 1 4203 1 10594 1 4437 1 8449 1 4231 1 13888 3 8729 1 3327 1 5451 1 11339 1 ridged 12718 1 15353 1 3436 1 15254 1 15692 1 5771 1 10947 1 8784 1 3164 1 884 1 15230 1 12326 3 12847 1 4263 2 10541 1 115 1 8823 1 15973 1 10251 1 2598 1 11619 1 9285 1 2045 1 7574 1 11397 1 8241 1 4185 1 2836 1 12045 1 1588 1 6656 2 5618 1 1697 2 363 1 13356 1 10321 1 15404 1 6437 1 10 1 9291 1 14092 2 8346 1 7103 1 1534 1 16055 3 1818 1 2394 1 10131 1 9768 1 1211 1 14959 1 4293 1 3524 2 9635 1 3258 1 15911 1 7354 1 8138 1 2062 1 8642 2 1981 14 2881 1 3501 1 6419 1 13472 1 2134 1 4698 1 9639 1 11233 1 2857 1 9210 2 9836 1 1134 1 6574 2 6814 1 311 1 1104 1 13845 1 1583 1 7904 2 5797 1 7776 1 13518 1 13768 1 3269 1 9731 1 3348 1 bing_crosby 10418 1 12995 1 7729 2 7789 1 1619 1 yuan_dynasty 8302 1 12871 1 14838 1 6715 1 5847 1 13801 1 16268 1 8175 1 9664 1 11229 1 14094 1 15574 2 15688 1 1982 1 processing_time 14994 1 7293 1 15105 1 9021 1 13263 1 13903 1 6958 1 5313 1 10356 1 5702 1 12898 1 11826 1 13581 1 south_carolina 6011 1 3310 1 12524 1 10310 1 3164 1 884 1 11859 2 16321 1 12624 2 705 1 13821 2 7469 1 12786 1 3134 1 3396 1 2436 1 2701 1 5347 2 14196 1 6555 2 1909 1 4185 1 10356 1 11799 1 13712 1 2263 1 10868 1 3992 2 14614 1 14234 4 11088 1 1339 1 14584 2 13648 1 5017 3 11259 1 6686 1 12309 1 1773 1 10490 1 7485 2 1403 1 11055 1 6801 1 11474 1 7350 1 10070 1 802 1 12284 2 14692 1 6601 1 5471 1 14990 1 16225 1 9746 1 10335 1 535 1 10635 1 2755 1 14865 1 6188 3 9748 1 15027 1 1983 22 11512 1 4358 1 1134 1 5663 1 5228 3 1893 1 10178 1 11578 1 3128 3 3130 1 9587 1 9728 1 7149 1 7435 1 1685 1 14047 2 9384 2 6547 1 15688 2 13312 2 lafayette 14012 1 10237 1 9021 1 1014 1 4737 2 3189 1 logic_gate 4933 1 13903 2 9365 1 angina_pectoris 4659 1 869 4 9199 1 10363 1 8173 1 2338 1 16321 1 531 1 1672 1 14693 1 8309 1 9798 1 11045 1 4907 1 12130 1 9546 1 5616 1 15588 1 12943 1 8921 1 4735 2 15918 4 5878 3 15404 1 1984 9 3741 1 1014 1 11110 1 14160 2 10388 1 1179 1 897 1 2014 3 16494 1 14251 1 9587 1 13828 1 15251 1 6850 1 5694 1 12569 1 sociality 2598 1 4110 1 6969 1 8595 1 9992 1 6498 1 510 1 dutiable 3077 1 15073 2 6180 1 2407 1 13745 3 16167 1 8779 2 126 1 7824 1 4718 1 3858 1 1939 1 5810 1 5873 1 3766 2 2837 1 amusement 6333 2 6895 1 8233 1 7048 1 2431 1 1113 1 15397 3 8652 1 15466 1 13674 1 9393 1 13490 1 3676 1 13089 1 258 1 2661 1 14091 1 1459 1 11400 1 9021 1 14351 1 14650 1 1501 1 15958 1 4788 1 14718 1 12895 1 13169 1 13718 1 14584 1 5939 1 996 1 12996 1 5781 1 3364 1 8408 1 6444 1 14270 3 9830 1 5883 1 4728 1 3113 1 3043 1 9668 2 5718 1 13438 1 12315 1 11477 1 6972 1 8215 1 6881 1 3824 1 62 3 778 1 6751 2 4110 1 13040 1 1099 1 6292 1 3896 1 4198 1 4256 1 5474 1 4998 2 1014 1 1985 14 1134 1 14081 1 15279 1 10340 1 14082 1 3862 1 6980 1 312 1 7872 1 12383 1 12159 1 594 1 8180 1 14015 1 7903 1 3430 1 6161 1 11612 1 9384 1 2986 1 9817 1 2111 1 woolly_aphid 10450 1 14774 1 4358 1 4957 1 14606 1 3875 1 227 1 15242 1 septillion 4529 1 2301 1 4925 1 2404 1 13606 1 14857 1 10119 1 7789 1 bloodthirsty 1125 1 3145 1 13832 1 3602 1 6525 1 14545 1 368 1 13428 1 3186 1 14755 1 4385 1 6924 1 5694 1 13434 1 1944 1 8621 1 physiological 9755 1 5443 2 14449 1 13668 1 13158 2 7754 2 3672 1 4714 1 11009 1 10473 1 10474 1 3096 2 7302 1 15636 2 6828 1 2190 1 4951 1 9285 2 10543 1 12234 1 8275 1 8512 1 9429 1 6769 3 9021 1 7131 1 4531 2 7308 1 10900 1 13428 1 5215 1 6279 1 7133 1 3729 1 3684 1 13099 1 10424 1 1667 3 3918 1 14658 4 525 1 1596 1 4660 1 13034 1 15839 1 15704 1 3113 3 8095 2 15065 1 5718 1 16019 1 776 1 4537 1 15910 1 531 3 16336 1 1949 1 533 1 12707 2 12772 1 11309 1 7319 2 3859 1 536 1 12578 1 5544 1 8841 1 2646 1 2734 1 15421 1 3343 1 14668 1 10236 1 5104 1 7259 3 7042 1 12916 1 1169 2 5608 1 8421 1 12837 1 7650 1 16177 1 16175 1 9587 2 9728 1 14147 3 4362 1 10786 2 11320 1 3867 1 1296 1 16110 1 3200 1 15665 1 9049 1 14746 2 7049 1 4303 1 11860 1 5412 1 12145 2 2598 3 5347 1 5413 1 12266 1 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1 1786 1 107 1 9907 1 9109 1 7839 1 9421 1 2944 1 atropine 15918 1 12192 1 15840 1 10704 1 13462 1 13967 1 85 1 7259 1 2900 1 14493 1 3760 1 2436 1 447 1 15026 1 15753 1 13583 1 1645 1 2043 1 11474 1 5407 1 resoluteness 1330 1 4256 1 15294 1 12643 1 2099 1 4041 2 11485 2 8607 3 14812 1 5528 1 2864 1 6080 1 12534 1 14457 1 15933 1 13171 1 13598 1 11807 1 textile_machine 3404 1 7368 1 16101 1 16451 1 2335 1 2206 5 5649 2 7837 1 6965 1 6113 1 3237 1 8035 1 2662 1 11611 1 5112 2 9768 1 3121 1 holland 421 1 12231 1 1755 1 8821 1 1756 1 16377 1 7850 2 12845 1 1115 1 13552 1 13674 1 11147 1 3986 1 10119 2 11619 1 1907 1 11655 1 5618 1 7789 2 15329 1 4957 2 14065 1 15510 1 7103 1 5500 1 9401 1 15139 1 6029 1 2635 1 8128 1 4566 1 7314 2 7984 1 1576 2 13505 1 10396 1 6908 1 13035 2 2127 1 2207 10 4352 1 1641 1 5820 1 15069 1 11312 1 9206 1 6419 1 2096 1 14076 1 4829 1 16398 1 13633 1 12871 1 2856 1 8108 1 4861 2 11512 2 16172 1 185 1 11784 1 15077 1 8419 1 12617 1 8845 1 1328 2 3057 1 9420 1 2921 1 lamb 6646 1 7689 1 11008 1 6984 1 14023 1 12264 1 14606 1 3836 2 11349 1 5040 1 4093 2 13821 1 12785 1 8119 5 14782 1 3676 1 13189 1 11796 2 13491 1 4869 1 7537 1 15564 2 13583 1 3875 1 11354 2 10954 1 10666 2 2560 1 763 1 8 2 7541 2 14855 1 15012 1 2663 1 6592 1 14351 2 5648 1 14516 1 4957 1 14159 1 2966 1 227 1 9765 1 7636 2 9739 1 13720 1 12795 2 2564 1 5783 1 1818 1 2394 1 2634 1 6535 2 10259 1 4994 1 11474 2 14859 1 3112 1 10965 1 14209 1 3333 1 1924 2 2208 39 4166 1 1739 1 14242 2 9998 2 8869 1 7549 2 3929 1 7587 1 4666 1 14366 1 1099 1 3828 1 953 1 15965 1 6452 1 781 1 2578 1 12246 1 10464 2 7230 4 12538 2 8980 1 15222 3 10569 1 1518 2 11895 1 12225 1 5034 1 2586 1 11282 1 9916 1 68 1 12952 1 2797 1 6363 1 8230 1 11612 1 818 1 roundish 12138 1 16264 1 4260 1 11184 1 10310 1 12075 1 9115 1 14606 1 666 1 12326 1 12815 1 13821 1 14056 1 4305 1 4263 1 16078 1 14027 1 13583 1 1119 1 2873 1 16003 1 4339 1 8458 1 10321 1 15404 1 15473 1 10387 1 8993 2 1624 1 11259 1 6686 1 11052 1 10392 1 16055 1 7922 1 1845 1 10962 1 12862 1 14830 1 6801 1 11474 4 7350 1 16459 1 10704 1 2722 1 10742 1 2209 9 3258 1 13970 1 6287 1 12968 1 10461 1 5910 1 8259 2 7175 1 13471 1 9103 1 9206 1 409 1 14370 1 4829 1 4296 2 6977 1 9680 1 1014 1 7457 1 7360 1 3127 1 9945 1 9587 2 9950 1 15082 1 2043 1 2517 1 9047 1 14972 1 augmentation circuit_board 15940 1 1955 1 16141 2 7505 1 14225 1 7290 1 3376 1 13272 1 3527 1 1101 1 2960 2 obsequiousness 5470 1 2291 1 11684 1 2374 1 1501 1 6238 1 10993 1 hutch 5514 1 ascending_aorta 13824 1 3830 1 14403 1 13893 1 2928 1 14006 1 15235 1 14103 1 13467 1 probabilities 4466 1 9021 1 13237 1 10321 1 12592 1 8223 1 13011 1 14693 1 14969 2 3534 1 1667 1 13214 1 14018 1 3161 1 8128 1 5957 1 meteor_swarm 7218 1 12623 1 7739 1 516 1 15187 1 8322 1 writiers 9482 1 13863 1 12139 1 pearly_nautilus 3693 1 4025 1 2264 1 koruna 14181 2 dishware 12402 1 12521 1 14855 1 14077 1 14532 1 2136 1 3830 1 1650 1 67 1 8246 2 13422 1 11413 1 2210 5 2953 1 6238 2 7393 1 9467 1 1596 2 15574 1 16236 1 13581 1 wheatear destructible 328 1 10974 1 13626 1 outfitter 673 1 4041 1 14913 1 12323 1 8408 2 1130 1 7665 1 mash 7015 2 12521 1 13953 1 10472 2 2019 3 8338 2 4260 1 15948 2 13873 1 11075 1 2802 1 8628 1 3983 1 2658 1 12785 1 10416 1 10862 1 1085 1 7239 2 2489 3 11397 1 14882 1 3875 1 13614 1 12236 2 3403 2 2873 1 14855 1 1361 1 16122 1 10 3 15473 1 7343 3 15267 1 16047 2 8345 1 789 1 10924 2 8017 1 5271 1 7133 1 4102 1 15931 2 9064 1 2089 1 175 1 7384 1 8438 1 9715 1 16017 2 3793 3 3112 3 11951 1 6350 1 13036 1 13999 1 9610 1 10461 2 12803 2 2211 27 2788 4 15068 2 4827 1 1102 1 6452 2 9639 2 3798 2 7716 3 917 1 7457 1 67 2 12809 1 9946 1 9916 1 14504 1 2768 1 1019 1 1685 1 5140 6 3699 1 4365 1 3347 2 match_play 15940 1 4998 1 1519 1 10506 1 5648 1 7799 2 12998 1 6424 1 10131 1 10672 1 9886 1 united_states_senate 16494 1 4680 1 1603 1 6841 1 10771 1 15168 1 9533 1 14988 1 15514 1 3231 1 external_body_part 6204 1 4200 1 7657 1 6078 1 9285 1 indicative_of 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2662 1 10358 1 insistence 10911 1 13053 1 8108 1 866 1 3483 1 1408 1 1811 1 1049 1 3003 1 13574 1 2051 1 9374 1 8146 1 16051 1 4950 1 16344 1 3617 1 5108 1 7312 1 15849 1 amendable 14658 1 4828 1 7388 1 8344 1 assemblyman 8517 1 volatile 15228 2 5551 1 4709 1 4944 2 15499 2 14226 3 5443 2 3350 1 5896 2 4017 1 12231 1 8153 1 6525 1 4238 1 9219 17 14193 1 9087 1 12326 1 15470 2 11861 3 4263 1 14879 1 9392 2 12145 3 9693 1 15636 5 7158 1 13823 3 3676 1 3066 2 7972 1 5041 1 10543 4 15263 1 10918 1 14679 1 7913 1 7441 1 9926 1 12725 2 13645 1 8458 2 11400 2 151 1 3992 1 13859 8 15329 1 7343 1 12669 1 10732 1 1064 5 3615 1 15738 1 2306 1 3684 1 11017 1 6221 4 8353 1 14168 1 6535 2 14685 1 11261 2 6536 1 14071 4 1406 1 1004 1 3793 2 3145 1 13967 3 8867 1 2723 1 3887 1 531 1 12059 3 9301 9 15113 1 1709 2 533 1 11884 1 8495 1 3222 1 4856 6 4035 1 1009 2 13008 1 14695 1 6419 1 2478 1 2011 2 15491 4 12778 2 1015 2 5724 1 16438 1 4116 1 13734 1 9310 2 16403 1 12542 2 9644 1 10404 1 593 1 15423 1 2216 35 16494 1 1053 1 11684 2 7119 1 68 1 2953 1 1357 3 16143 1 6792 1 14049 1 13706 2 valence 15228 10 2308 2 13034 1 9996 1 7316 2 9650 2 10473 2 14151 1 3794 1 12342 1 15996 1 10816 4 14287 1 14557 1 8613 3 4471 1 12145 2 13490 1 5041 1 14665 5 16254 2 3764 2 13063 1 2705 1 12578 1 9153 3 15940 1 7495 2 4925 1 3992 2 10 1 12303 1 4203 1 7559 4 7513 2 7514 1 5608 1 8180 1 16405 1 2218 22 12543 3 3684 3 5711 2 2089 1 6535 2 11532 1 8563 1 15035 1 2986 1 7934 2 global 906 1 1279 2 856 3 10555 2 7218 2 2300 2 1282 2 14977 1 3064 2 11121 1 6475 1 6865 1 8191 2 8456 1 520 1 2881 1 15186 2 14126 2 11002 2 7497 1 3679 1 14630 1 1337 2 11512 2 8314 1 5104 1 8180 1 9945 1 2217 8 1629 1 15251 1 2652 1 12416 2 7885 1 11018 2 6833 2 2488 1 exorbitant 37 1 7095 1 13552 1 15436 1 10119 1 16185 1 6170 1 11339 2 2945 1 3154 1 2179 1 484 2 12191 1 523 1 10304 1 2135 1 14515 1 4169 1 8058 1 8631 1 3003 1 16051 2 14738 1 1748 1 12486 1 2219 7 12279 1 15572 1 5089 1 15674 1 15688 1 nuprin 9273 1 869 1 14251 1 15700 1 15251 1 11851 1 10300 1 14458 1 14025 1 4390 1 rosin 1163 1 14101 1 561 1 13013 1 2915 1 2802 1 1240 1 9654 1 7687 1 2986 1 navigation 3310 2 12749 1 3473 1 13418 2 11903 1 3164 1 4335 1 10695 1 6204 1 13552 1 1145 1 4950 1 4643 1 8824 1 6830 5 8456 1 3320 1 10617 1 14578 1 10544 1 6713 2 9021 1 3679 1 14461 1 16330 1 14719 1 6280 1 13935 1 7516 1 5271 2 8088 1 6594 1 13684 1 7831 1 9235 1 15701 1 4792 1 2199 1 1818 1 2394 2 8408 1 201 1 1211 2 15407 1 10628 1 14208 1 8582 1 13507 1 4537 2 11224 1 12181 1 3189 1 532 2 11227 2 5221 1 972 2 5176 1 536 1 16398 1 6102 1 8108 1 6230 1 8718 1 3630 1 7845 2 13844 1 2797 1 2220 30 14340 1 9527 4 490 1 5120 1 12391 1 11587 1 859 1 12848 1 1568 1 11729 1 13460 1 4429 1 4744 1 670 1 5873 1 8547 1 931 1 2148 2 3516 1 8732 1 3282 1 10059 1 13784 1 7916 1 8123 1 1427 1 12082 2 12856 2 1700 1 2306 1 14892 3 373 1 12020 1 15805 1 6748 1 7800 1 8997 2 13264 1 4029 1 938 2 15450 1 7421 1 10809 1 3592 1 2877 1 9067 2 7218 1 2419 1 2373 1 7992 1 10998 1 10335 1 3718 1 6319 1 14101 1 4502 1 1011 1 11887 1 1320 1 8558 1 1252 1 879 2 12485 1 1325 1 13081 1 4279 1 9084 1 12416 1 14044 1 14504 1 14379 1 10018 2 15816 1 1389 3 6643 2 12877 1 14049 1 6894 3 wiped_out 16436 1 800 1 9755 1 2619 1 1565 1 5778 1 8592 1 4561 1 9833 1 5879 1 15822 1 2300 1 328 1 15259 1 10097 1 7239 1 8903 1 14658 1 6187 1 10218 1 commercial_paper 10615 1 8090 1 719 1 15481 1 1054 1 9423 1 9716 1 11241 1 12057 1 2508 1 fuller 7572 1 607 1 7373 1 7336 1 12020 1 13526 1 16492 1 7942 1 227 1 10577 1 introvert 9338 1 westside 8367 1 5228 1 9186 1 13674 1 desynchronize 7705 1 11327 1 2036 1 john_stuart_mill 14941 1 4723 1 4084 1 13075 1 unpasteurized 4260 1 stockinged 2331 1 4335 1 4921 1 14488 2 brood_hen 12785 1 2569 1 4041 1 9741 1 meshing 3685 1 3404 1 9209 1 13785 1 11330 1 13118 1 12497 1 10813 1 12439 1 15405 1 10671 1 2778 2 15743 1 13736 1 14152 1 595 1 6093 1 6910 2 13323 1 7799 1 1562 1 6971 1 monday 7366 1 8155 1 11816 1 3608 1 10539 2 12041 1 11693 1 11147 1 15898 1 3676 2 10153 1 5964 1 576 1 4529 1 12757 2 5586 1 5705 1 7376 1 9021 2 10836 1 9291 1 10840 1 12962 1 396 1 15058 2 8408 1 10625 1 4069 2 13883 1 16458 1 9833 1 4406 1 13108 1 14000 3 9717 1 16165 1 2338 1 2911 1 13110 1 405 2 779 1 10633 1 1675 4 6841 1 15487 1 16096 1 4907 1 4355 2 6419 6 11108 1 3342 1 5103 1 11512 1 4543 4 7899 1 13447 1 9971 1 2214 1 11784 1 11578 1 15423 1 163 1 9919 2 6161 1 11939 1 11456 1 2221 20 16471 1 12922 1 9735 2 15502 2 2524 1 15230 1 12011 2 16117 1 8787 1 7210 1 16410 1 4808 1 6864 1 11728 1 5188 1 12927 1 13583 1 9261 1 7914 2 8056 2 5240 2 9926 1 15294 1 5560 1 3283 1 866 1 7413 1 10800 2 16419 1 11327 1 3813 1 11405 1 13395 1 12674 1 15933 1 12795 1 12998 2 8128 1 9665 1 13398 2 12602 1 5814 1 10816 1 4823 1 7864 1 9672 1 4110 1 3375 1 11742 1 13214 2 3223 1 3222 1 15457 1 1130 1 9206 1 3896 1 1982 1 511 1 4506 1 1014 2 1256 1 7558 1 7559 1 9484 1 14104 1 16494 2 11069 1 12036 1 5065 1 8816 1 14044 1 8197 1 12228 1 7436 5 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12848 1 gymnosophy 158 1 10884 1 11147 1 5977 1 ddt 12716 1 389 1 2746 1 13089 1 14931 1 disapprove 16398 1 15886 1 7056 1 14233 1 5187 1 12856 1 8870 1 717 1 6351 1 141 1 11749 1 3493 1 12795 1 12998 1 4907 3 4534 1 15702 1 12620 1 11414 1 7067 1 10318 1 reasonableness 7532 1 2235 6 3145 1 13956 1 14052 1 1024 1 3367 1 8834 1 10247 1 15089 1 7880 1 13801 1 562 1 15436 1 12374 1 7006 1 11339 2 1873 1 14772 1 3032 1 466 1 7178 1 10465 1 8058 1 3744 1 14821 1 12067 1 13765 1 5942 1 3816 1 16484 1 527 1 16144 1 elsewhere 10469 1 5442 1 10275 1 15254 1 15994 1 3243 1 11691 1 884 1 7939 1 3169 1 2436 2 11619 1 9284 1 6830 1 13089 1 7574 1 14196 3 6965 3 4185 1 6654 1 2560 1 11149 1 12822 2 9155 1 3404 1 8458 5 14126 1 8886 4 5455 1 15404 2 10956 1 5083 1 3992 1 15267 1 7308 1 7189 1 14323 1 5217 1 152 1 3489 1 12503 2 10286 1 6716 1 13099 1 641 1 5849 1 8021 4 14932 1 10770 1 1596 1 8408 1 10701 1 16163 1 8581 1 3181 1 8830 1 4468 1 16459 1 13035 1 3416 1 10966 1 9635 1 5718 3 7522 1 3258 2 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1 6346 1 5849 1 2635 1 11500 1 14934 1 6835 1 4466 1 7450 1 2909 1 91 1 8582 1 2008 1 9773 1 3189 2 12059 1 2132 1 11370 1 14664 1 15485 1 16225 1 10846 1 4144 1 6974 1 2284 53 7956 1 5722 1 7319 5 15964 1 12577 1 11230 1 2011 1 3741 1 8717 1 14010 1 13118 2 6846 1 1479 2 213 1 4361 1 12448 1 14527 1 10178 1 11647 1 6359 1 13380 1 14147 1 9787 1 2797 1 12716 1 6853 2 11939 1 600 1 9920 3 11940 1 11119 1 3200 1 3061 1 15859 1 4260 1 7849 1 15502 1 12326 1 14287 1 4616 1 16117 1 4263 1 12849 1 15729 1 15601 1 10316 1 6367 1 1232 1 4157 1 9531 1 9926 2 363 1 10867 1 6740 1 10321 1 9801 1 13784 1 7729 1 2152 1 14820 1 6497 2 9804 1 15736 1 1624 1 8125 1 3362 1 14548 1 1242 1 5745 1 10198 1 5746 1 5245 1 14167 1 13000 1 2815 1 13264 1 16131 1 1634 1 14956 1 869 1 9405 1 1125 1 1246 1 4499 1 14555 1 15878 1 14557 1 11541 1 7992 1 4031 1 1641 1 5506 1 15880 1 9138 1 3223 3 3377 1 508 1 13273 1 11272 1 5756 1 3531 1 9678 1 9142 1 10339 1 381 1 7998 1 2042 1 14565 1 1133 1 16492 1 10466 1 16494 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6894 1 6264 1 middle_temporal_vein 2994 1 7872 1 8989 1 nonstick 7421 2 2422 1 1225 1 4854 1 13649 1 2918 1 12498 1 1596 1 8628 1 conjugate 4925 1 9305 1 4933 1 3793 1 629 1 7933 1 5188 1 14010 1 14352 1 4354 1 8524 1 11403 1 9219 1 12342 1 3776 1 12353 1 9400 1 15236 1 3842 1 obnoxious 9511 1 12142 1 4499 1 9670 1 6287 1 4263 1 945 1 1421 1 14626 1 15916 1 5913 1 2343 1 953 1 2286 6 11400 1 5436 1 1501 1 3992 1 13077 1 12633 1 9444 1 1257 1 13011 1 5422 2 5528 1 10213 1 11896 1 10926 1 9083 1 4546 1 3955 1 10290 1 exonerate 12529 1 12943 1 10820 1 5586 1 5005 1 4466 1 4212 1 11999 1 758 1 2720 1 10340 1 2581 2 8928 1 8088 1 4172 1 7133 1 1393 1 15896 1 9728 1 15898 1 15637 1 14864 1 10549 1 7884 1 14941 1 9889 1 tampon 12324 1 2298 1 13926 1 11308 1 13692 1 16026 1 12943 1 7774 1 5878 1 14612 1 1698 1 4169 1 7189 1 12539 1 13910 1 9059 1 11327 1 14066 1 8180 1 10755 1 7281 1 13752 1 3006 1 4512 1 12020 1 1367 1 6660 1 2111 1 flaunt 12725 1 10492 1 14917 1 2720 1 330 1 13403 1 5918 1 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9115 1 14606 1 4092 1 15789 1 12326 1 15232 1 4263 9 12786 1 8119 1 7471 1 5126 2 5347 1 4678 1 14027 2 3354 1 988 1 6238 1 13583 1 6367 1 12462 1 12666 1 293 3 2873 1 10253 1 1697 1 5295 3 7976 1 10321 1 3757 1 2999 2 225 1 1501 1 13462 4 16124 1 3811 1 8245 1 11129 1 1236 1 5422 6 9463 1 3760 4 14549 2 1237 1 11259 1 2306 1 10993 1 12862 1 4994 1 15181 1 6801 2 11474 2 15574 1 940 1 14298 1 4933 1 13967 1 16057 1 5997 1 3147 1 9477 1 9809 1 14493 1 1709 2 10461 1 2374 2 9479 1 8259 1 14626 1 13471 1 8446 2 953 6 4698 2 1932 2 11956 3 6325 1 7230 1 186 1 7360 3 13734 1 1447 2 7010 1 16314 3 5257 1 9945 3 11684 1 1583 1 15753 2 12546 1 2043 1 1140 1 14047 1 14972 1 11427 1 2770 1 9278 1 10468 2 counterrevolutionary 15886 1 3432 1 10013 1 3145 1 5929 1 whispering 528 1 7762 1 12993 1 13606 1 935 1 13130 1 4950 1 8555 1 15635 1 9937 1 15776 1 2670 1 11684 1 7660 1 11895 1 5589 1 lock_in 12027 2 10226 2 2981 1 12156 1 6171 1 9018 2 3744 1 548 1 12297 1 3045 1 13129 1 12718 1 2581 1 3283 1 3099 3 1660 2 6336 1 11887 2 cm 542 1 15431 1 12326 1 12815 1 4263 1 5235 1 8823 1 12849 2 14712 1 6020 1 14751 1 4986 1 9395 1 13616 1 3176 1 6591 1 14352 3 438 1 16329 1 10900 1 1624 1 7793 1 8245 1 5195 1 3495 1 1818 1 2394 1 8997 3 8581 1 11474 2 7421 1 11671 1 6661 3 13967 1 13626 1 9477 1 2129 1 15911 2 6035 1 11413 2 721 1 2881 1 7007 1 14866 1 9639 2 2426 1 11716 1 9521 3 1134 1 4169 1 7557 1 4323 1 1106 1 10375 1 2292 12 8394 1 11686 2 14778 2 1389 1 14189 1 3269 1 13312 1 quackery 9546 1 12735 1 12424 1 13016 1 slat 9714 1 2928 1 5295 2 4040 4 400 1 5770 1 13893 1 9193 1 6069 1 8954 1 8246 1 7120 1 5424 1 5805 1 4835 2 10131 1 2294 11 4129 1 9172 1 7225 1 6894 1 14946 1 hugger_mugger 5664 1 7598 1 6844 1 3898 1 14213 1 10564 1 10907 1 3113 1 8579 1 4086 1 11182 1 primordial 6836 1 12270 2 8506 2 3329 2 1766 1 10956 4 16492 2 1698 1 10247 2 7218 3 1322 1 13674 4 1096 2 12956 1 7210 1 4451 2 1291 2 7012 1 2326 1 2753 1 10424 2 13176 1 6728 1 9268 2 9655 1 15873 2 1328 1 839 2 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4156 1 4490 1 1964 1 10076 1 13490 1 9189 1 950 1 13191 1 3893 1 13598 1 15916 2 5323 1 3741 1 1362 1 2266 2 2360 5 11315 1 16312 1 9532 1 7649 2 5228 1 13428 1 7360 1 2214 1 1893 1 12382 1 6145 1 11749 1 15933 1 6008 1 678 1 126 1 2814 1 7313 1 2445 1 15317 1 shoelace 573 1 3750 2 6502 1 3979 1 5568 1 8098 1 8929 1 970 1 3320 1 14679 1 3727 1 11572 1 5158 1 2533 1 5603 1 2361 7 8416 1 6697 1 8147 1 1192 3 4064 1 12597 1 12795 1 7799 1 14021 2 4464 1 1022 1 15924 1 respiratory_system 7636 1 5366 1 10421 1 5436 1 11814 1 14500 1 8309 1 11512 1 298 1 16259 1 12797 1 7744 1 8605 1 13595 1 tolerantly 5614 1 905 1 14626 1 16288 1 15517 1 4707 1 ambitiously 11309 1 2745 1 3807 1 11559 1 truncation 945 2 2155 2 4359 2 1534 2 6592 2 dilapidated 3750 1 5038 1 13669 1 9359 1 14651 1 15230 1 2362 6 8869 1 10324 1 3815 1 11406 2 16224 1 7239 1 642 1 3223 1 10418 1 13171 1 9918 1 4035 2 6375 2 4446 1 4223 1 5873 1 5224 1 12033 1 12045 1 2892 1 11815 1 8424 3 phosphorous 5295 1 4016 1 2042 1 3676 1 30 1 7393 1 14275 1 4145 1 1296 1 perspiring 15239 1 4217 1 12508 1 9532 1 15110 1 13443 1 deprived_of 15522 1 3388 1 15993 1 16263 1 7879 1 6898 1 13552 1 3167 1 14453 1 13674 2 15695 1 11147 1 3676 2 7697 1 2703 1 8512 2 3480 1 5586 2 4590 1 2049 1 3679 1 1203 1 8963 1 4531 2 16329 1 8757 1 5846 1 12898 1 7702 1 6220 1 12569 4 13938 1 4658 1 4466 1 12643 1 10628 1 5173 1 9773 1 531 1 2853 1 11370 1 4539 2 4143 1 14593 1 15216 2 8645 1 7839 1 14333 1 5225 1 13177 1 15918 1 5914 1 6788 1 12253 1 12448 1 7042 1 12383 1 7718 1 14016 1 5034 1 13380 1 14018 1 16469 1 15557 1 7261 1 2989 1 12586 1 12391 1 9390 1 12922 1 7851 2 15790 1 13641 1 15089 1 3205 1 12848 1 3578 1 3135 1 15729 2 1907 1 14950 1 4158 1 15863 1 15797 1 5243 2 13912 1 2363 24 3811 1 1180 1 10325 1 4217 1 12795 2 16484 2 13000 1 15987 1 11674 1 13797 1 945 1 10076 1 689 1 2424 1 15115 1 2879 1 11273 1 10208 1 13867 1 3657 4 5953 1 7230 1 13276 1 15459 1 2247 3 312 1 1136 1 4279 1 15191 2 12161 1 11686 1 3025 1 8563 1 14189 1 2826 1 7067 1 hog_peanut 7471 1 2560 1 13970 1 7350 1 transvaal 1635 1 5184 1 9098 1 10223 1 12882 1 4263 1 7002 1 12059 1 13086 1 9203 1 2301 1 7158 1 11728 1 5347 1 9518 1 14712 1 6965 1 1510 1 5756 1 10544 1 293 1 536 1 14667 1 12361 1 2404 1 15219 2 7789 1 3992 1 11512 2 14374 1 5228 1 6890 1 16104 1 13408 1 8502 1 5271 1 2364 5 16494 1 159 1 3508 1 5849 1 5405 1 12600 1 stubby 4588 2 8642 1 13726 1 11873 2 6580 1 13876 2 5999 1 13821 1 339 1 clusia 13681 1 7904 1 8021 1 4238 1 9635 1 799 1 11005 1 6801 1 9278 1 11474 1 evangelical 13795 1 9477 1 3204 1 9036 1 5210 1 10795 1 2757 1 10989 1 362 1 12790 2 2332 2 6678 2 6665 1 7729 1 7178 1 14337 1 8327 1 12448 2 11439 1 5047 2 5229 1 2365 10 15191 1 9107 2 7518 2 14188 2 14068 2 3108 1 pesewa 14181 1 cachet 9305 1 15570 1 15864 1 3676 1 11282 1 3242 1 1054 1 15 1 8582 1 8499 1 10340 1 12623 1 4761 1 1595 1 11308 1 15077 1 birthday_party 13422 1 777 1 1789 1 dryden 10244 1 4030 1 3636 1 3483 1 1582 1 6499 1 2002 1 11282 1 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10100 1 15940 1 4998 1 2915 2 11002 8 14010 2 485 6 11646 2 1167 6 14869 4 6294 1 2983 1 4803 1 11278 3 16494 3 12616 1 16405 2 8393 1 3534 1 8778 1 13411 1 4425 1 7436 1 11182 2 7067 1 defense_department 16494 1 1554 1 12922 1 16419 1 1604 1 14971 1 alone 9011 1 5636 1 4640 1 13668 1 4393 1 10219 2 8272 1 3776 1 1796 2 10349 3 14318 1 14194 1 2556 1 13674 3 14710 1 13676 1 4894 2 4056 1 11693 2 14319 1 7374 1 8881 1 2326 2 3248 1 4398 1 5156 1 6274 1 7509 1 9822 1 763 1 4529 2 8754 2 392 1 14855 2 3546 2 5383 2 13681 6 9021 3 1148 1 5271 1 5166 2 12898 1 15906 1 8021 5 13434 1 6535 1 3792 3 11831 1 8408 1 10772 1 10491 2 399 1 9830 5 4659 2 15772 4 12508 1 12643 2 10363 1 3793 3 16278 1 9297 1 3113 1 2720 1 6781 1 9719 6 10905 1 13440 1 7387 11 2062 1 8031 3 13040 2 8645 2 9723 1 5913 2 9171 1 7256 1 4199 1 13444 2 13178 2 15157 1 11999 1 14600 1 11576 1 2857 1 11315 8 5914 1 7648 1 7649 4 10237 1 10106 2 15657 1 12003 1 659 1 8775 1 9315 1 9728 1 7260 4 4914 2 12653 1 7393 2 11581 2 4423 3 9978 4 3699 1 13581 4 16182 1 14746 1 12391 1 8652 1 15789 1 8786 2 13851 1 7660 2 6924 1 3135 2 6049 3 10794 1 12665 2 5806 2 7539 2 15667 1 4921 1 12268 2 931 1 13712 1 6053 7 7667 1 15294 1 3282 1 4680 2 1305 1 13064 1 2083 1 11327 2 10672 2 6932 1 3815 1 296 1 12795 5 5298 2 4688 1 12020 1 8668 1 9860 6 2369 85 6444 1 9337 1 682 1 15574 1 12282 1 10261 1 4932 1 4933 3 3145 1 8067 1 6448 1 8930 1 945 6 10681 2 5200 2 9349 1 6945 1 2755 1 9076 1 4446 1 5688 2 16199 1 16436 1 15940 1 693 1 12410 1 14498 1 13218 1 3601 1 2613 2 14375 1 2888 1 6328 1 11749 3 5065 2 15579 2 10144 3 11205 2 1860 3 698 1 6577 1 15191 2 12416 1 4118 1 10944 1 3161 1 13482 1 12038 1 8198 2 5442 1 6333 2 10415 3 4944 4 820 2 11901 1 10824 2 8451 1 15469 1 6204 1 13610 1 15470 1 9363 1 2623 1 13490 1 8202 1 6830 1 10951 1 3320 2 7697 1 9891 1 3321 1 11081 1 2262 1 12698 1 5586 1 8702 1 10421 3 10420 2 8458 1 2263 1 13356 1 16217 1 337 1 12563 2 469 2 1595 2 10161 1 1466 1 12569 5 9092 1 15702 1 10701 1 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1 3483 1 trypsinogen 5729 1 3012 1 15846 1 10782 1 hint 11835 1 6116 1 8374 1 12643 2 3793 1 16089 1 4847 1 2957 1 10310 1 9219 2 5960 1 10333 1 12326 2 11132 1 4263 1 2621 1 1836 3 14319 1 1395 1 7782 1 7587 1 13583 1 1130 1 8055 1 2834 11 6292 1 12268 1 11572 1 6419 1 6053 1 13074 2 12410 1 1649 1 9021 1 4160 3 8686 1 6743 1 7360 1 12614 1 67 1 2155 1 8180 1 13720 1 9896 1 3729 1 12811 1 12008 1 7195 1 6043 1 11052 1 1611 1 6520 1 14285 1 7396 1 8829 1 14189 1 3921 1 irreparable 12943 1 2945 2 12410 1 5185 1 3145 1 219 1 14423 1 10265 1 4917 1 13797 1 4156 1 6839 4 12383 1 15503 3 9587 1 10520 3 11547 1 14429 3 12206 3 5427 3 2835 14 7234 1 7772 3 6853 1 baggage 3077 1 6180 1 6080 1 623 1 13745 1 8322 1 6273 1 10206 1 5873 1 7665 1 3766 1 2837 7 15073 2 5475 1 16491 1 13698 2 2546 1 4957 1 767 1 7148 1 1594 1 10325 1 7563 1 126 1 8945 1 8196 2 9861 1 2159 1 6469 1 equilibrium 5443 3 14449 1 13668 4 9819 1 3979 1 15257 1 10608 1 4335 1 6204 1 13552 2 11906 1 2620 1 15470 2 11693 1 6080 1 15636 1 3908 1 5211 1 6830 1 12234 1 16325 2 1999 1 10544 1 2838 39 4955 1 3404 9 1592 1 15403 2 14461 1 14322 1 4648 1 1090 1 15056 1 3616 1 16387 1 2495 1 1667 1 3918 1 13171 1 14658 1 10771 3 12569 1 4657 1 8285 1 11563 1 7832 1 11223 1 14469 1 7585 1 11838 1 531 1 16336 1 12707 1 13837 2 4412 1 2285 2 971 1 4907 1 1604 1 14005 1 8837 1 13576 1 99 1 11714 2 5544 1 8292 2 7716 1 14868 1 918 2 1680 1 2069 1 3125 1 16034 1 1607 1 9912 1 14279 1 4361 1 7042 2 280 1 14015 1 1053 1 2407 1 12068 1 10786 1 2797 1 1357 1 2986 1 9590 1 5921 1 4867 1 8047 1 14746 1 10247 1 5736 1 4156 2 606 1 1836 1 12145 5 13253 1 6170 2 6798 1 2601 7 11464 1 13064 1 6799 1 15671 1 1309 1 6499 1 16050 1 12596 1 12794 1 7983 2 15933 1 373 1 6747 1 14428 1 998 1 15446 1 15572 1 13965 2 7421 1 6116 1 4030 1 4933 1 3145 1 620 1 9477 1 15744 1 62 1 1642 1 623 1 6882 1 13214 1 12406 1 15880 1 12094 1 2822 2 7427 1 6064 8 11339 1 8193 1 12943 1 3531 1 15940 1 4938 1 16438 2 3830 1 10340 1 7744 1 16258 1 9618 1 7745 1 3158 1 1450 2 15191 2 2824 1 11686 4 5694 1 12620 1 13286 1 570 1 magnetic_north 2839 7 2945 1 5901 1 16392 2 14092 2 7218 2 884 2 3490 2 2680 1 12721 2 373 1 14018 1 9018 1 14666 1 471 2 10018 4 9606 1 2467 2 appealable 4143 1 3032 1 9663 1 12543 1 14658 1 13441 1 4562 1 16373 1 genus_eoraptor easter 15192 1 7752 1 4178 1 14606 1 11349 6 12011 5 3983 1 13422 1 12785 2 12424 1 6864 2 11728 1 11147 1 8120 1 8083 1 8749 1 9697 1 15200 1 12590 1 3875 1 1459 1 4307 1 9598 1 6369 1 2873 1 576 1 11593 1 12822 1 8458 1 2840 27 12669 1 2229 1 11085 1 1621 1 6311 1 7514 1 5271 1 11155 1 1180 1 10804 1 15512 1 7830 1 15058 1 7544 1 11731 1 3518 1 444 1 2056 1 3792 1 11666 1 10625 1 15574 1 11474 1 2935 1 12179 1 6778 1 16487 1 13398 1 10265 1 14000 2 13368 1 6287 1 449 1 6035 1 405 1 15487 1 13756 1 14666 1 4355 1 9206 1 11886 1 15749 2 1931 6 5103 1 15118 1 8108 1 14010 1 6788 1 4910 1 7558 1 8981 4 14219 1 4478 1 10442 1 1560 2 8180 1 16494 1 8265 1 16439 1 10977 1 5065 1 414 1 12518 1 6670 1 14188 1 5514 1 16318 1 mealy 12022 1 7485 1 10742 1 13106 1 7904 1 9278 1 scarabaeidae 1457 1 gargle 6173 1 9728 1 6043 1 11403 1 9219 1 9074 1 13718 2 4950 2 spiky 1560 1 4143 1 8886 1 3072 1 15299 1 14338 1 10142 1 12147 1 6287 1 293 1 14493 1 imperiously 8980 1 4373 1 molding 5442 1 15889 1 12229 1 219 1 6704 1 13320 2 11588 1 3983 1 1836 3 8791 1 13461 3 4129 1 10798 2 1459 1 10318 1 3481 1 763 1 12822 1 5295 1 5899 1 14980 3 2303 2 13559 1 5779 1 4159 2 2841 25 12669 1 7344 1 12467 2 87 3 14236 2 11827 1 3616 1 14294 2 15933 1 8666 2 12020 1 10489 1 15645 1 14658 1 4464 1 11474 2 14956 1 2498 1 3716 1 8214 1 3717 2 9772 1 13212 1 7738 1 2312 1 8930 4 14274 1 6351 1 16195 1 14937 2 4734 2 6629 2 779 1 11882 1 11171 2 3896 1 877 1 5057 1 1512 2 4040 1 653 1 10306 2 1480 1 1892 1 1893 2 11928 1 1169 1 12916 1 9728 1 11282 1 15920 1 12353 1 papal_bull 7134 1 11752 1 3383 1 contemplate 7420 1 9021 1 2646 1 15118 1 16182 1 12669 1 3242 1 6794 1 16191 1 16356 1 6433 1 606 1 124 1 489 1 7587 1 1838 1 126 1 8523 1 9861 1 10952 1 11081 1 5004 1 stretchy 14432 1 2662 1 annexation 7944 1 16416 1 1737 1 3444 1 15864 1 987 1 1006 1 1007 1 8482 1 12905 1 8897 1 15878 2 14287 1 12215 1 jammed 4542 1 4987 2 5544 1 323 1 12229 1 15930 1 3525 1 9735 1 15110 1 5431 1 9971 1 10672 1 10352 1 10829 1 595 1 3729 1 12161 1 16096 1 3357 1 4969 1 2986 1 11701 1 polymerase 2542 1 7809 1 12943 1 6828 1 3992 1 12020 1 5530 1 3223 1 13402 1 possessed 11491 1 11901 1 13818 1 14917 1 12231 1 9423 2 9359 1 11753 1 2829 1 10220 1 7692 1 329 1 3396 1 15636 1 11078 1 11819 2 8158 1 15827 1 13490 1 6081 2 10152 2 16080 2 6830 1 13491 1 5964 2 4338 1 8962 1 763 1 5841 1 3103 1 1088 1 11 1 5843 1 13096 1 5328 1 2843 52 7022 1 1595 7 8406 2 5528 1 3490 1 8209 2 9236 1 3730 1 12569 4 11831 1 1404 1 12767 1 4403 1 4466 1 8356 1 10628 1 11707 2 2852 1 13689 2 403 1 1949 1 585 1 14470 1 5099 1 969 1 13575 1 15339 1 14404 1 2285 1 14941 1 9723 1 6292 1 7321 4 2343 1 13445 1 6845 2 3741 1 6230 1 5914 1 1354 1 16174 1 3744 1 8589 2 12383 1 1169 3 595 1 101 1 16405 1 16036 1 11115 1 2797 1 8536 1 2016 2 7722 1 11119 1 9113 1 12586 1 12203 1 2862 1 39 1 5187 1 10985 1 1490 1 3205 2 7210 5 15089 20 16410 1 8379 1 6167 1 6168 1 11589 1 2598 2 4021 1 15601 1 1907 1 10507 1 9655 1 12268 1 4921 1 3582 1 5240 1 611 1 4158 1 12270 1 4925 2 11400 3 6438 1 13064 1 9532 1 9659 1 9928 1 16355 1 7102 1 1309 2 9398 1 12995 1 14236 1 10324 1 11406 2 1067 1 3449 1 4493 1 10128 2 443 1 503 1 373 1 6935 1 6243 1 15447 1 8997 1 374 1 2815 1 10068 1 7169 2 2036 1 5502 2 7421 6 4623 1 10265 1 16305 1 9477 1 2166 1 14243 1 6568 1 7490 3 8322 2 2170 1 14770 1 5888 2 2377 1 14911 2 877 3 11339 1 16436 2 4040 1 2945 1 3830 1 6890 1 6574 1 14838 1 4704 1 11812 2 10213 1 11749 1 7012 1 14044 1 11686 1 10018 1 16144 1 overcoat 4040 1 15817 1 323 1 2303 1 15254 1 3750 1 5071 1 12231 1 499 1 4012 1 4957 1 7768 1 8272 1 7360 1 2842 6 16092 1 8869 1 9400 1 5926 1 11406 1 7505 1 7260 1 7239 3 3501 1 2662 1 roselike 15192 2 6778 1 4238 1 10310 1 2563 1 14965 1 6287 1 4263 1 1560 1 1709 1 15973 1 1200 1 9216 1 2043 1 4698 1 leek 11517 1 14859 3 7625 1 4933 1 1331 1 3112 2 1487 1 1457 1 4730 1 16251 1 5173 1 10023 1 6287 3 10024 1 13422 1 4471 1 2436 1 2489 2 5488 1 6654 3 11956 1 12246 1 9057 1 2980 1 14351 1 1732 1 12792 1 2844 11 11088 1 1560 2 3760 1 10286 1 10941 1 9064 1 2089 1 4001 1 5140 1 2035 1 2713 1 6644 1 13706 1 preset 16246 1 5443 1 16391 1 10660 1 13795 1 3097 1 15729 1 16025 1 6736 1 2960 1 6123 2 8391 1 15812 1 9021 2 3074 1 15075 1 10958 2 2845 5 4928 1 9914 1 9587 2 8971 1 14969 1 11282 1 3710 1 10880 1 14340 1 2277 1 9863 1 9493 1 11833 1 footbridge 538 1 3164 1 12718 1 6720 1 4446 1 serow 11709 1 2613 1 8642 1 509 1 5999 3 897 1 first_crusade 12471 1 7398 1 10342 2 8254 2 4998 1 10903 1 5908 1 288 1 1459 1 9285 1 13110 1 934 1 cant 6011 1 6896 1 7367 1 10345 2 9819 1 14052 1 8747 1 11141 1 9423 3 3242 1 6898 1 1939 1 1145 1 13552 1 13674 1 9426 1 7505 2 1272 1 7374 1 2326 1 3247 1 9285 1 12113 1 7239 1 7759 1 2703 1 15762 1 11821 1 2960 2 14854 1 9429 1 4529 1 1030 1 9955 1 3404 1 9021 1 14980 1 3679 1 5901 1 5648 1 14719 2 12895 1 4649 1 15534 1 15536 1 5528 2 6774 1 10901 1 13684 1 267 1 4189 1 10092 1 2846 63 14587 1 3918 1 13171 1 12368 1 1540 1 12639 2 14984 1 9714 1 906 1 10492 1 12643 1 9964 1 12644 1 9164 1 529 1 2852 1 9032 1 7141 1 9299 1 12372 1 8531 1 6909 1 7387 1 4539 2 2061 1 10495 1 8901 1 8031 1 915 1 9169 1 6291 1 5791 1 2854 1 8645 2 13695 1 8769 3 10781 1 7390 2 9439 1 7256 1 8292 1 4200 2 11033 1 11999 1 5179 1 13700 1 5545 1 278 1 9583 1 12002 1 6667 1 2581 3 10106 1 2214 6 1682 1 1290 2 3429 1 14225 2 1053 1 4083 1 7260 1 4422 2 9045 1 14873 1 2593 2 6791 1 4087 1 4675 1 5183 1 420 1 5184 1 2462 1 12139 1 15561 1 9735 1 12011 3 5411 1 4808 1 11461 1 1300 2 12145 2 11728 1 13058 1 10664 1 3440 1 3703 1 9457 1 15667 1 8055 3 5190 1 12268 2 4211 1 3280 2 931 1 6925 3 4680 1 14612 1 8662 2 5044 1 5935 2 8314 2 4927 1 2083 1 11327 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8221 1 13890 1 12061 1 6604 1 6976 1 4256 1 2509 1 6230 1 10564 1 13635 1 484 1 11235 2 8223 1 8980 1 5228 1 15717 1 11377 1 16104 1 9912 2 488 1 12067 1 13765 1 1750 1 9787 1 9107 1 15225 1 2797 1 599 1 1611 1 7464 1 11789 1 9109 1 2016 2 2296 1 11940 1 2409 1 3749 1 1224 1 12457 1 8591 2 14023 1 10310 1 984 1 15502 1 2300 1 14412 1 1758 1 3511 1 1616 2 4487 1 8237 1 14026 1 15729 1 10456 1 5872 1 3873 1 496 1 14679 1 15976 1 15093 2 5240 1 10866 1 5739 3 866 3 3359 1 9532 1 6499 1 13790 1 7978 1 10875 1 10324 1 9535 1 12859 1 1240 1 9663 1 16484 1 126 1 997 1 6749 1 7731 1 2814 1 10879 1 4994 1 10880 1 15877 3 1004 1 3218 1 14555 3 7990 3 10593 1 6118 1 14303 1 7992 2 6629 1 9544 1 2315 1 3375 1 14558 1 3223 1 14436 1 4035 1 3527 2 1130 1 2682 1 15517 1 12346 1 5888 2 7226 1 14697 1 10888 2 3531 1 2945 1 9550 1 7997 1 10750 1 15119 1 4041 2 16491 1 2546 2 4169 1 13277 2 14838 1 13408 3 7010 1 5365 1 15888 2 8004 1 11959 1 7120 1 5512 1 4511 1 11282 1 8393 1 2695 1 6520 3 10080 1 5140 1 15627 1 11287 1 optometry 2945 1 10772 1 fleshed_out 1635 1 15636 1 3241 1 5319 1 7587 1 15609 1 14173 1 16191 1 12556 1 5847 1 1753 1 yam 11002 1 15994 1 6985 4 9635 3 1621 1 6585 1 12815 1 11256 1 67 1 4263 1 1560 1 10079 1 7574 1 447 1 2847 7 1596 1 2560 1 5140 1 6801 2 8581 4 flawlessly 863 1 12995 1 wilted 10492 1 3913 1 16320 1 11830 1 2546 1 11079 1 2134 1 toady 7698 1 5514 1 8254 1 1868 1 5680 1 7014 1 hungarian 1576 2 13998 1 1756 1 4537 1 5652 3 12800 1 8999 1 389 1 9221 4 12848 1 8870 1 15169 1 14137 1 3676 2 1907 1 5041 1 16080 2 6719 1 12590 1 13229 1 13712 1 14334 1 8702 1 15118 1 4840 1 13635 2 6214 1 4202 1 15716 1 4957 1 16104 1 11595 1 13096 1 897 2 10325 1 7562 1 7135 1 13648 4 13395 2 13380 1 5035 1 15804 2 1667 1 797 1 16496 3 7314 2 2848 19 3076 1 10070 2 embarrassing 4637 1 5233 1 6854 1 14707 1 6203 1 15285 1 3351 1 1939 1 4776 1 9793 1 2697 1 16117 1 12395 1 13089 1 15762 2 5776 1 5702 1 14232 1 2809 1 2873 1 4529 1 3281 1 14062 1 2266 1 3992 1 15765 1 2415 1 7191 1 12761 1 14651 1 1595 1 4027 1 14720 1 15959 1 12085 1 2902 1 13684 1 2444 1 2088 1 12568 1 9860 1 4029 1 2849 14 5427 1 10964 1 869 1 299 2 10809 2 529 1 2007 1 14207 1 9668 1 129 1 8026 1 14301 1 13795 1 3926 1 4006 1 5307 1 1784 1 2061 1 13543 1 12939 1 1099 1 15916 1 11572 1 2577 1 13919 1 1580 1 5474 1 9942 1 15186 1 2646 1 4671 1 918 1 1014 1 6230 2 484 1 8223 1 12003 1 15312 1 5917 1 3568 1 4279 1 14528 1 7362 1 1053 3 14147 1 1750 1 8044 1 14969 1 6389 2 4707 2 9107 1 1055 1 4086 1 8946 2 12352 1 4389 1 2769 5 3238 2 2349 2 elucidate 12537 1 8292 1 15665 1 1660 1 12669 1 12555 1 897 1 11895 1 11147 2 6234 1 9394 1 3764 1 5317 1 5888 1 pitifully 7774 1 5129 1 2945 1 1893 1 15831 2 seance 15450 1 1590 1 4040 1 6023 1 14532 1 9818 1 16491 1 9532 1 9390 1 5986 1 7103 2 4562 1 11308 1 15570 1 10507 2 7313 1 1861 1 15985 1 whorl 5371 1 7365 1 12231 1 3979 1 4179 1 254 1 4335 1 8698 1 13821 1 5 1 16078 1 13086 3 1799 1 15636 1 9014 1 11147 1 2436 3 14712 1 12697 3 6020 1 9284 1 5700 1 7574 1 14196 1 6965 1 6210 1 2560 1 10419 2 7442 1 5452 2 8012 6 8702 1 15404 1 5455 5 3991 1 3993 2 1732 1 5086 1 14929 1 10286 1 9026 5 3618 1 1403 2 6284 1 10491 1 11443 1 2970 1 3555 1 15479 1 12643 1 10628 1 3853 1 10554 1 16459 1 2850 39 91 2 15840 2 13437 1 2568 1 2128 2 3258 1 6287 1 14212 1 4143 1 779 1 913 1 3421 1 6841 1 8645 1 7085 1 6419 2 12578 2 4296 1 8771 1 8980 1 7900 2 13578 1 6727 2 10372 1 1560 1 6232 1 16405 3 12068 1 2797 1 1609 1 599 1 2921 1 13768 8 13581 2 10983 1 1487 1 10054 1 11184 1 12981 2 10310 1 10790 1 14606 3 15230 2 2144 1 3133 1 14607 7 15788 1 666 1 5412 1 4263 3 3134 1 12786 2 11522 1 1836 1 12395 1 5347 1 5126 1 988 1 1907 1 1231 1 7473 2 10918 1 9394 4 13461 1 10920 1 293 1 5618 1 3444 1 4268 1 1840 1 2666 2 10871 1 14092 1 2028 1 4216 1 4436 1 10877 1 551 1 3760 5 6562 2 8550 1 447 2 9860 3 1845 1 7798 1 15573 1 9337 1 4570 1 14623 1 15241 1 8254 1 8737 1 5136 1 1709 2 12608 1 14626 1 8259 1 4698 1 15940 2 11956 1 1649 1 9680 1 1014 1 3227 1 7360 3 13734 1 15461 1 3604 1 1583 2 4511 1 4833 1 6820 1 14047 5 16318 1 13667 2 genus_heteromeles 1560 1 13086 1 12822 1 8458 1 7904 1 11619 1 14196 1 3258 1 11964 1 5361 1 stewed 12585 1 14859 3 5922 1 3112 1 14209 2 6350 1 2851 8 8784 1 10493 1 4335 1 9052 1 13422 2 11617 1 4471 1 9348 1 10663 1 11923 1 5773 1 15899 1 2489 1 4986 1 11956 1 14855 1 12833 1 9430 1 16172 1 15295 1 5496 1 9024 1 15222 1 5954 1 12448 1 14236 1 15931 1 8266 1 9081 1 7904 1 9064 1 16358 1 9752 1 1596 1 5140 2 4365 2 possibly 8506 1 13155 1 6896 1 14449 1 1660 1 10757 1 8153 1 146 1 11691 1 12109 1 13821 1 11819 1 14979 1 8159 2 2329 1 4529 1 7019 1 763 1 3404 1 9955 1 9021 2 5778 1 5901 2 14065 2 2843 1 897 1 4790 1 5528 1 15768 1 2846 1 5390 1 13434 1 10771 1 12900 1 9830 1 6030 1 1406 1 12508 1 15539 1 13939 1 9297 1 2852 33 8529 1 2972 1 6909 1 779 2 9168 1 13176 1 4540 1 14596 1 5176 1 14075 1 13444 1 8040 1 2581 1 2214 1 7043 1 1169 1 2346 1 4082 1 8180 2 9587 1 4300 1 11849 1 1562 1 1685 1 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4169 2 4172 1 6128 1 5366 1 11546 2 3534 1 11961 1 14044 1 16143 2 12353 1 1140 1 14047 1 12620 1 13549 1 6894 3 13814 1 bentham 14941 1 4723 1 4084 1 13075 1 achilles 5184 2 1737 2 3329 5 758 1 15398 2 9833 3 12391 1 8026 1 2990 10 3164 1 13552 1 2525 1 6825 1 2895 1 2326 2 11147 1 21 1 11605 2 11796 2 5334 1 14941 1 14482 1 9075 2 990 1 4529 1 9926 2 4158 1 3678 2 14277 1 4531 1 3088 1 11557 2 8942 1 12672 1 8208 1 9974 2 12383 2 4245 1 11282 1 12102 2 14504 2 13171 2 4599 1 8491 3 233 1 surgical_knife 12297 1 11233 1 5278 1 2999 1 9650 1 8980 1 8875 1 6447 1 12856 1 1812 1 7120 1 13395 1 3065 1 2335 1 7785 1 15917 1 15342 1 signaling 15450 1 2988 1 1344 1 11034 1 12439 1 9971 1 4100 1 4950 1 532 1 4809 1 5500 1 8881 1 3676 1 2496 1 7314 1 16318 1 15059 1 2040 1 peace_of_mind 9830 1 3741 1 8065 1 1363 1 15136 1 3744 1 14555 1 4093 1 11629 1 14243 1 4539 1 5499 1 15010 1 8158 1 2231 1 882 5 126 1 2797 1 10080 1 14636 2 7226 1 1176 1 attraction 7690 1 2385 2 1026 1 326 1 12623 1 14055 1 13821 1 14056 1 8628 3 16078 1 2623 1 13679 1 13490 1 1943 1 7239 1 8160 1 10764 5 15472 1 5776 1 15401 1 6400 3 9153 1 3404 1 1148 1 1088 1 1203 1 6531 1 2563 1 13428 1 15475 1 1595 2 2052 1 768 1 3490 1 9506 1 9711 1 4001 1 13992 1 471 1 5089 1 14270 1 2277 1 8167 1 4659 1 2499 1 7643 2 8214 2 13299 1 91 1 10430 1 7888 2 473 6 4537 1 12181 1 10559 1 11308 1 15846 1 15710 1 969 1 12442 1 4907 1 14941 1 3340 1 1604 1 10233 1 16398 2 4078 1 16399 1 14077 1 8108 1 918 1 14010 2 5228 5 5477 1 9176 1 12003 3 15281 1 8180 1 12918 1 16177 2 8044 1 10978 3 3057 1 2920 1 2921 1 6792 3 14604 2 13312 1 9527 3 5671 5 12922 1 9186 1 7908 1 3638 2 11859 1 7850 1 4487 1 11326 1 3064 1 2991 36 5926 1 3134 1 8479 1 5346 1 1617 1 1300 1 2865 1 9795 1 7052 1 5805 1 3582 2 433 1 293 1 16300 10 2467 1 8056 1 12725 1 673 1 15864 2 6175 1 14352 1 14819 1 13064 2 12792 1 6872 1 3587 1 5500 1 6686 1 2306 1 6747 1 13467 1 2814 1 9664 1 15301 1 15020 1 7486 1 12934 1 7421 3 5684 1 7420 1 505 2 2371 1 5198 1 6059 1 9865 1 7218 2 10816 1 9477 2 1507 1 2678 3 2373 1 13801 2 11739 1 15880 1 10335 2 3223 1 4111 1 12156 1 5888 1 10819 1 3084 2 9272 1 11887 2 12943 1 2426 1 14371 1 15186 1 1014 1 3600 3 7178 3 8325 1 8263 1 2247 8 6453 1 5439 1 14838 1 7009 1 2613 1 16206 1 1988 1 13409 1 11280 1 16494 1 5633 1 11749 2 11895 1 11896 1 8449 2 15315 1 4511 1 15191 1 12416 1 2549 1 11686 4 3025 1 8268 1 13286 3 11612 1 2770 4 15688 1 half_hitch 12143 1 14263 1 3477 1 11012 1 12139 1 1324 1 genus_polystichum 15973 1 8555 1 11956 1 8879 3 15960 1 8434 2 15905 1 9635 1 5173 1 5361 1 14407 2 conditioned 3350 1 2988 1 14085 1 15430 1 4209 2 15470 2 15695 1 6238 1 2992 10 6365 2 3611 1 13091 1 14422 1 4308 1 469 1 12856 1 8632 1 11406 1 12764 1 8969 3 2902 1 15178 1 6596 1 2496 1 7314 1 11474 1 9964 1 8582 1 7522 1 2724 1 8738 1 10775 1 3116 1 6035 1 646 1 5885 1 1099 1 2377 1 5888 1 6477 1 4224 1 8769 2 8292 1 7715 1 3022 1 5103 1 7230 1 16491 2 12380 1 11034 3 5978 2 12099 1 11747 1 138 1 15422 1 12255 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804 1 15808 1 15026 1 5054 2 9074 3 5201 1 9206 3 11886 1 2096 1 4829 2 15940 1 3964 1 9079 2 9210 2 311 2 13606 2 16207 1 16314 7 2107 1 1328 1 1331 1 10149 1 10824 1 13609 1 13742 1 15469 1 329 1 16078 1 11077 1 15584 1 3839 1 15951 3 7574 1 8571 1 7941 1 15047 3 10282 1 5842 1 2263 1 5455 1 10956 1 4957 3 1206 2 16329 2 5457 1 5086 1 14517 1 3729 1 16087 1 7314 1 10701 1 964 1 3038 7 3181 1 2126 1 9097 1 9635 2 4352 2 3623 1 12968 1 12318 1 4143 4 972 1 3501 1 5601 1 4255 1 7457 2 12712 2 14279 1 5608 1 8475 1 850 1 9384 2 12717 1 8373 1 9648 1 5733 1 13521 1 3636 5 8595 2 10310 1 8853 2 12326 1 4263 1 2658 1 8119 1 1906 1 3354 1 13526 1 1760 1 8857 1 6494 1 8243 2 12991 1 12995 4 9400 1 227 1 2306 3 7485 1 4030 3 9405 1 6502 2 13796 1 4499 3 7738 1 6628 1 7355 1 11133 1 8138 1 7865 2 12092 1 1509 3 13656 1 1510 1 5756 1 7007 1 5361 1 11956 1 5761 1 4169 1 7360 1 16258 1 7744 1 4172 2 7746 1 15518 1 5634 1 6520 1 14972 1 7364 1 mannerism 6895 1 15987 1 13299 1 9511 1 8867 1 13797 1 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7239 1 9038 1 7147 2 5722 1 2171 1 5913 1 11400 1 5493 1 15509 1 3741 2 2266 1 5901 1 7413 1 8264 1 4462 1 11404 1 15590 1 470 1 1292 1 8081 1 9083 1 4706 1 446 1 8211 1 9573 1 7733 1 15836 1 mutually 3634 1 820 1 15581 1 7366 1 7365 1 702 1 2299 1 11349 1 3063 1 7692 1 12041 1 7210 1 2865 1 13780 1 4525 1 6134 1 6830 1 2992 1 6365 1 13228 1 4813 2 2260 1 15093 1 293 1 3704 1 3404 1 12466 1 9704 1 578 1 15330 1 469 1 12856 1 10589 1 227 1 8246 2 4684 1 15570 1 14823 1 1844 1 13648 3 15178 1 2306 1 373 1 3552 1 15 1 126 1 9860 1 12569 1 2496 1 14797 1 10491 1 15574 1 10070 1 13687 1 8410 1 4623 2 2818 1 11097 1 15213 2 7316 1 7522 1 15878 2 1475 2 16222 1 3116 15 779 1 13271 1 16168 1 11309 1 2756 1 15916 1 6477 1 15940 1 7555 1 12410 1 1380 1 15219 1 6102 1 15120 1 12412 1 10465 1 8875 1 10940 2 4203 1 10106 2 138 1 14839 1 14281 1 3158 1 4890 1 11720 1 3605 1 13342 1 14147 3 2485 1 14018 1 14047 1 1959 1 13581 1 opulence 5072 1 14281 1 8180 1 12238 1 7238 1 11728 1 4748 1 11002 1 8325 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8359 1 4470 1 3258 2 6287 3 12183 1 7711 1 4143 3 11227 1 10168 1 11167 1 3501 1 3193 3 409 2 481 1 4296 1 9639 1 3123 35 6977 6 12000 1 8980 1 7457 1 7900 1 12711 1 1560 1 7904 1 15082 1 13768 1 2517 2 8985 1 10983 1 1487 2 3750 1 6731 1 10310 1 11184 2 14606 1 1567 1 11859 1 6861 1 2144 1 8853 1 8786 1 4263 2 13458 1 12984 1 3578 1 736 1 7471 2 4428 2 2598 1 5126 2 988 1 14230 1 13461 1 293 3 12666 1 7409 1 2873 1 5295 1 11594 1 3444 3 10321 1 13462 2 13714 1 9263 1 1624 2 6560 1 1971 1 15297 1 3760 9 9064 1 7922 2 7798 2 10131 1 11474 2 7350 3 16056 1 15241 1 1313 1 13970 1 8675 1 872 1 1709 1 6449 1 15026 1 8259 3 9206 1 7225 2 7007 1 7743 1 4698 1 4829 2 1932 1 15940 1 9210 1 7360 3 5954 1 2613 1 3228 1 2951 2 16314 1 9945 2 1583 1 7120 1 4511 1 15753 4 16444 1 2043 2 9216 6 5140 1 ascorbic_acid 13570 1 13973 1 9920 1 transmissible 14609 1 10977 1 10651 1 13380 1 3679 2 3967 1 9298 1 7851 1 1103 1 2972 2 11662 1 2167 1 harmed 4659 1 5443 1 3853 1 10363 2 12139 1 9423 1 10758 1 4156 1 15790 2 4950 1 12534 1 11757 1 13745 1 3223 1 14525 1 15588 1 7256 1 3564 1 3124 6 11999 1 9523 1 9308 1 13577 1 5000 1 8418 2 3997 1 5608 1 594 1 3617 1 1053 1 11408 3 7858 2 6919 1 1450 1 12795 1 6850 1 9976 1 16469 1 12569 1 10880 1 11612 1 fall_out 1932 1 12864 1 14532 1 12229 1 1014 1 5430 1 10 1 4260 1 9219 1 11646 1 16321 1 9886 1 3959 2 16027 1 3378 1 12590 1 15939 1 14047 1 smoked_herring 16329 1 14655 1 2043 1 15473 1 whipsaw 10211 1 nerd 3135 1 2504 1 11709 1 11223 1 7353 1 12848 1 computer_game 3617 1 6248 1 15114 1 3113 1 15026 1 13091 1 ended 9819 1 15398 1 1391 1 253 1 5148 2 13674 2 9821 2 1525 1 7238 3 8881 2 15400 1 4782 1 15134 1 4060 1 4645 1 12361 1 5158 1 14855 2 5778 1 3679 1 5648 1 14198 1 14465 1 15138 1 5271 2 14583 1 4064 1 15139 1 1818 1 4659 1 12643 1 1039 1 5020 1 13035 2 9833 1 7522 1 15910 1 9717 1 14725 1 13689 1 8900 1 4663 1 778 1 779 1 21 1 11450 1 12910 1 7389 3 12244 2 12376 2 12377 1 12249 1 8907 1 1555 1 4079 1 3126 48 14738 1 3568 1 2346 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4146 1 8769 1 12943 1 8292 1 8076 1 1680 2 8939 1 7649 1 3125 13 6951 1 4361 1 14474 1 13309 1 12414 2 14969 1 7393 1 5694 1 14873 2 16143 1 13286 1 6894 2 13706 1 hair_dryer 3097 1 9833 1 6131 1 3165 1 3699 1 227 1 lennoaceae nonviolent_resistance 421 1 6404 1 3679 1 14185 1 13551 1 10899 1 536 1 5271 1 divergent_strabismus cheyenne 16398 1 16003 1 1737 1 15294 1 8471 3 9833 1 14352 1 1755 1 12040 1 6498 1 4818 1 3203 2 11826 1 8092 1 11686 1 13451 1 747 1 9075 1 6801 1 10070 1 northerly 10824 1 573 1 1014 2 12733 1 11602 1 9705 1 9704 2 884 2 6860 1 16355 1 3106 1 16116 1 10904 1 10539 1 13821 2 297 1 6145 1 15933 1 2032 1 5487 1 15382 1 15225 1 7052 1 13994 1 7485 1 2920 1 4446 1 4829 2 family_heteromyidae 6635 1 11147 1 5347 1 1815 1 7457 1 6487 1 7350 1 13312 1 unbeatable 2802 1 1362 1 6123 1 2873 1 melon 3436 1 3200 1 573 2 10983 1 16350 1 219 1 9922 2 10310 8 9496 1 6204 1 6585 1 16078 1 7471 5 14712 2 988 1 8883 1 9428 1 12462 2 2836 1 9458 1 1119 1 13712 1 2873 1 5240 2 11593 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11479 1 12383 1 hilly 421 1 15109 1 3164 1 858 1 6289 1 12343 1 12705 3 5347 1 10119 1 5029 2 3320 1 13063 1 7321 1 11215 1 1730 1 293 2 9004 1 12537 1 11656 1 2263 3 6886 1 14351 1 10956 1 7789 1 4169 1 3768 1 8994 1 2613 1 897 1 5271 1 7105 1 10325 2 1859 1 5861 1 384 1 7796 1 5425 1 6146 1 13434 2 5593 1 13104 1 10070 1 3252 10 elm_tree 5295 1 2303 1 6518 1 11777 1 2873 1 pyknic 9115 1 5793 1 9235 1 tailless 10245 1 4048 2 16350 1 6014 1 12109 1 4335 1 7938 1 14056 1 12882 1 5837 1 9393 1 11972 1 7574 1 8512 1 4185 1 2560 1 13134 1 10225 1 8458 1 10956 1 5901 1 4957 1 10871 1 14065 1 12467 1 8605 1 1534 1 3361 1 7636 1 16220 2 14517 1 12308 1 7543 2 5849 1 1815 1 7798 2 10259 1 11632 1 6178 1 1576 1 2971 1 3253 12 13004 2 3524 1 9635 1 5718 1 8738 1 2063 1 208 1 11414 1 5913 1 6257 1 16492 1 185 2 8369 1 9971 1 14279 1 7329 1 13922 1 4387 1 7746 1 4117 1 1328 1 12952 1 1685 1 2921 1 6487 3 1296 1 9590 1 13312 1 uric_acid 3254 9 4016 1 12732 4 4335 1 2008 1 10116 1 11308 3 531 2 12059 1 7002 1 10906 3 11739 2 14979 1 7836 1 3874 1 694 5 5878 1 15918 2 12192 1 3992 1 16438 1 7189 1 15736 1 7344 1 7133 1 998 1 5694 3 5766 3 15572 3 11424 2 3435 1 6792 1 9920 1 descending_aorta 10706 1 16328 1 4687 1 1667 2 loire 3444 2 13035 6 3255 7 5648 1 9186 2 5228 1 13260 1 12181 1 14055 1 14137 2 6289 6 9490 1 10335 1 447 2 12590 1 6609 1 13063 1 7685 1 3699 2 gastroenteritis 3145 1 5901 1 5718 1 11327 1 9913 1 14066 1 12308 1 12795 1 12374 1 8309 1 5766 1 7314 1 6660 1 6738 1 apologetic 5528 1 3741 1 1292 1 10750 2 1054 2 10265 2 10630 1 2114 1 4369 2 15356 1 selfishness 5442 1 6011 1 12689 1 573 1 4867 1 13299 1 1850 1 4206 1 8821 1 3256 5 15576 1 10266 1 14555 1 13525 1 14592 1 6247 2 9203 1 62 1 7536 1 13490 1 1646 1 7539 1 14771 1 10899 1 13576 1 10544 1 15829 1 10781 1 5240 1 12725 1 12270 2 9955 1 15245 2 13075 1 12380 1 1219 1 8685 1 3483 1 7649 1 265 1 2581 1 1260 1 9400 1 13718 1 4631 1 5528 1 8180 1 2274 2 16037 1 12795 1 8298 1 11559 1 7462 1 7545 1 1140 1 2349 1 unworthiness 4256 1 12643 1 4805 1 12003 1 10097 1 12543 1 7491 1 16159 1 side_effect 9405 1 5443 1 1738 1 3257 5 5467 1 4809 2 15636 1 2190 1 9285 1 3440 1 8323 1 9546 1 11080 1 1872 1 4399 2 14925 1 6419 1 4699 1 6992 1 5878 2 15918 1 245 1 2012 1 10782 1 6620 1 10387 1 2121 1 7744 1 1515 1 10388 1 3505 1 15959 1 7636 1 2014 1 10195 1 2902 1 5681 1 8021 1 13099 1 16387 1 11631 1 13050 1 13223 1 13897 1 16303 1 6162 1 3142 1 13298 1 bradstreet 5512 1 11830 1 5724 1 8408 1 1145 1 210 1 13312 1 4928 1 papyrus 5841 3 448 1 6592 1 12047 1 5648 1 5311 1 7658 1 5244 1 7852 1 10840 1 357 1 10033 1 7012 1 13702 1 11924 1 11796 1 10592 1 10909 1 15028 1 magnesite 8438 1 4433 1 9608 1 5742 1 11327 1 16344 1 3676 1 13703 1 9467 1 12519 1 11261 1 6563 1 naprosyn 869 1 7532 1 5878 1 11851 1 9546 1 15736 1 14025 1 531 1 4390 1 whereabouts 9926 1 16398 1 1014 1 12249 1 13893 1 5664 1 4285 1 2680 1 14319 1 14969 1 13895 1 15920 1 8946 1 8128 1 15901 1 infrequent 1178 1 1565 1 15540 2 15386 1 2568 1 4155 1 11327 1 14838 1 5836 1 16314 1 3259 5 13861 2 1428 1 3676 1 5248 2 10418 1 5766 1 15625 1 3435 1 11634 1 3677 1 3238 2 11231 1 1588 1 dylan 13948 1 9176 1 12102 1 13176 1 7178 1 apocalypse 4243 1 5913 1 10933 1 exchangeable 4466 1 6165 1 15818 1 7626 1 4616 1 1475 1 3260 5 13745 1 3719 1 10727 1 12206 1 4036 1 15386 1 6373 2 14374 1 12792 1 2581 1 1595 3 6760 1 5729 1 2231 1 15251 2 5218 1 6519 1 1055 1 2110 1 11182 1 magnesium_sulfate 10906 1 2902 1 5895 1 15918 1 97 1 14389 1 688 1 3992 1 1088 1 11987 1 16173 1 8156 1 11628 2 gurney 15294 1 9482 1 8498 1 12047 1 10202 1 2546 1 11307 1 9416 1 15910 1 15788 1 14755 1 15590 1 2747 1 14442 1 13674 1 15040 1 14584 1 13430 1 14954 2 4688 1 563 1 3398 1 15877 1 14437 2 2160 1 16144 1 6323 1 perceptive 12471 1 11367 1 7569 1 8463 2 7936 1 5581 1 10537 2 13525 1 2697 2 5737 1 13552 1 8531 3 12625 1 12357 1 2061 1 15528 1 7507 2 5599 1 3261 11 11943 1 4529 1 9021 1 5253 1 5843 1 7649 1 4098 1 1623 1 14066 1 9400 1 5846 1 6760 2 9063 2 6145 1 2859 1 1053 1 4247 1 10391 3 796 1 15805 2 5317 1 15301 1 5180 2 2349 1 genus_erysimum 16314 1 1709 2 4387 1 9945 1 14980 1 2436 1 3176 1 5173 1 1487 1 14712 2 2921 1 293 1 4698 1 7239 1 4263 3 6788 1 noctuidae 12044 1 13609 1 1457 1 data_processing 1246 1 15630 1 3242 1 4123 1 1145 1 13821 1 4852 1 2239 1 12721 1 11728 1 9116 3 14864 2 13903 1 3223 1 8903 3 9701 4 4907 1 3527 1 4196 1 5964 1 7006 1 3262 13 7455 1 2960 3 1787 1 2490 1 8752 1 5240 3 16436 1 10867 1 3404 2 1014 1 11999 1 9143 1 3679 1 2857 1 15187 1 5279 2 2121 1 3800 1 15833 1 15312 1 9914 2 1169 2 11895 1 2783 1 15105 4 5131 2 12516 2 12260 1 8665 1 13263 1 9860 1 9861 1 7486 1 284 1 16074 1 michigan 7465 1 9648 1 4427 2 3164 2 11859 2 705 2 14056 1 13821 4 3134 1 1300 3 7158 1 493 1 11147 2 5347 1 3907 1 11619 1 11123 1 888 1 8512 1 13063 1 16237 1 293 6 7443 2 2303 1 10836 1 13784 1 11555 1 4647 2 14584 4 3789 1 8127 3 639 2 6686 1 5461 2 9664 2 11055 2 6536 2 505 1 13724 1 12284 9 3738 2 10559 1 6601 1 4472 1 9544 2 1006 1 10335 2 1676 3 3263 32 3084 2 9748 1 4829 5 8108 1 4909 2 1134 2 5228 7 10687 2 6818 2 14043 2 2106 1 11895 1 2917 1 10574 1 13312 2 15688 2 lurking 3077 2 11703 1 1921 1 5778 1 5103 1 16182 1 1014 1 3145 1 7191 1 8869 1 13654 1 12305 1 4082 1 5664 1 9672 1 16036 1 4247 2 3917 1 15721 1 12775 2 3426 2 1968 1 c3h7 6498 1 7644 1 14557 1 12059 1 toed 9648 1 11614 1 8506 1 15353 1 5285 1 3775 1 10983 1 15255 2 5733 1 1457 1 3636 3 8595 2 4335 2 15584 1 707 2 7574 1 5964 2 8512 1 3401 3 6656 1 7944 1 8057 1 8702 2 16478 2 9631 2 15733 2 1913 1 3992 2 6175 1 8243 1 12792 2 14065 1 1770 1 6997 1 13096 1 1534 1 6440 1 3361 1 4062 2 13878 2 8435 3 13467 1 2089 1 13434 1 15301 2 6283 1 7314 1 15181 2 9899 6 8438 2 11670 1 8214 2 6502 1 15241 2 15591 2 5718 2 15023 1 8529 2 3013 1 12181 2 4352 1 2972 1 3189 1 7355 2 6540 1 4143 1 5910 1 7113 5 12033 2 5361 2 3084 2 1982 1 12578 1 4078 1 15940 5 3264 25 12410 2 11233 1 9210 1 3302 1 16207 2 16314 1 12066 1 16405 4 10855 2 11612 3 284 3 13312 2 abkhaz 13648 1 10070 1 calan 14487 1 14251 1 731 1 16201 1 2641 1 15251 1 7413 1 10388 1 1179 1 531 1 prison_camp 6359 1 6035 2 10899 1 14650 1 288 1 guide_on 4853 1 3676 1 union_army 16398 1 1737 1 10868 1 813 1 7360 2 454 1 4928 1 3396 1 16494 1 11147 1 7149 1 16484 1 2634 1 13583 1 5168 1 2149 1 gelechiidae 6977 1 1457 1 fractional_currency 6373 1 5934 1 9514 1 8484 2 9415 2 14238 1 12905 1 4261 1 16478 1 14536 1 6801 1 cape_ann 3134 1 13587 1 15759 1 7915 1 159 1 3972 1 10398 1 12284 1 1134 1 10635 1 884 1 1685 1 15020 1 9261 2 2594 1 4865 1 with_ambition 16180 1 7802 1 10363 1 16182 1 8764 1 9400 1 1836 1 1617 1 12810 1 7563 1 14595 1 4222 1 15497 2 14186 1 12177 1 11031 1 8192 1 7751 1 9109 1 sue 10275 1 15430 1 4917 1 12624 1 10611 1 9886 1 1964 2 13779 1 10418 1 8512 1 13711 1 4751 1 16477 1 5240 1 12529 1 5586 1 4186 1 46 1 10283 1 5901 1 7413 1 1065 1 123 1 6533 1 13648 1 10427 1 5945 1 3038 1 13914 1 9830 1 905 1 4466 2 1246 1 3043 1 7488 1 2608 1 15216 1 779 1 8070 1 3152 1 8323 1 3858 1 7039 1 5688 1 2377 1 2882 1 9779 1 2577 1 1552 1 7496 1 5689 1 15886 1 484 1 11680 1 7649 1 10340 1 657 1 2214 3 13736 1 15077 1 13307 1 3265 11 13765 2 15080 1 11282 1 6520 1 12620 1 6264 1 spike_lavender 5551 1 10179 1 5003 1 1513 1 12059 1 4775 1 pin_table 8546 1 4998 1 13606 1 7789 1 heredity 3241 1 13872 1 1903 1 6611 1 8594 1 9390 1 7049 1 4156 2 2525 1 5412 1 15634 1 7237 1 7158 1 11147 1 12721 1 3440 1 8309 1 12266 1 11080 1 12889 1 10955 1 13712 1 5240 1 14126 1 13531 1 3992 1 3483 1 16190 1 11327 1 227 1 14616 1 6218 1 4790 1 12308 1 16244 1 12795 1 1667 1 8997 1 471 1 8316 2 6801 1 4660 1 16056 1 8896 1 11709 1 15377 1 7549 1 531 1 4539 2 4143 1 1710 2 12707 1 5433 1 4855 1 1476 1 16283 1 9039 1 9546 1 4502 1 510 1 13009 1 7809 1 5689 2 7526 1 6257 1 5279 1 6423 1 8875 1 11485 1 8942 1 13011 1 3900 2 12713 1 16494 1 11684 1 15191 2 3266 10 13665 1 14777 1 mosan 9186 1 6498 1 13648 2 10539 1 1571 1 13712 1 6841 1 10070 2 rugged 2384 1 11431 1 5372 1 10539 1 13674 6 12041 1 15399 2 9694 2 14643 2 15200 1 9891 3 12045 1 13354 1 14855 1 8702 1 3404 1 7376 3 16004 1 3992 1 4531 2 14462 2 5214 3 13096 2 11630 1 5783 1 1818 2 12570 3 1403 1 6470 1 12055 1 905 1 1211 1 11162 1 11702 1 10628 1 10430 1 15841 1 12439 1 7585 1 1821 1 3189 1 8765 2 7587 3 11777 6 4356 1 9836 2 13118 1 2858 1 9176 1 2582 1 5231 1 12838 1 3267 28 9918 1 8722 1 9920 2 3059 3 3634 2 490 1 9051 1 2862 2 10380 3 8853 1 859 1 3353 1 115 1 12848 1 12395 1 2598 3 5347 2 9457 3 7408 1 7665 1 7667 1 5295 1 9120 1 2303 3 6437 1 14424 1 14682 1 6310 1 7414 2 8994 1 11406 3 3816 1 126 2 7798 1 15301 1 4567 1 8250 1 12471 1 12800 4 5505 1 15305 1 8869 1 10934 1 6568 1 4031 1 3654 1 13598 2 5252 3 4829 2 7555 1 5689 1 7230 1 7178 1 10938 1 2427 1 2613 1 1136 2 11278 2 4387 1 3158 1 12616 1 6643 1 2826 1 4517 1 3238 2 13925 4 simple_pistil 5041 1 inferior_court 5586 1 2877 1 congregational_church 141 1 14068 1 14337 1 11147 1 13482 1 12848 1 world_wide_web 3685 1 300 1 8998 1 448 1 14689 1 5716 1 388 1 10904 1 12372 1 2621 1 2973 3 1526 1 7005 1 14289 1 6367 1 2960 2 3598 1 8906 1 8292 1 15403 1 5311 1 12669 1 8243 1 9764 1 5936 1 8418 1 4650 1 3971 1 1180 1 11895 3 16010 1 5781 1 12020 1 4013 2 282 1 3268 6 inflammable 14598 1 8458 1 4933 1 15491 1 13859 2 6423 1 9219 3 2523 1 213 1 14557 1 9301 1 9392 1 442 1 2216 1 16494 1 3676 1 3223 1 7393 1 14679 1 10624 1 6367 1 14956 2 13706 3 mousy 3913 1 9922 1 16329 2 2735 1 12326 1 10167 2 227 2 7120 1 3518 1 447 1 8062 1 4185 1 9206 1 6487 1 cut_rate 12885 2 4164 2 14146 1 12067 1 16483 1 10750 1 12795 1 13745 2 12393 1 12076 1 15110 1 14842 1 11339 2 330 1 unco 6535 1 4723 1 10430 1 portion 2186 1 11431 1 13158 1 9819 1 14053 1 9423 1 10473 1 9820 1 2324 1 8156 1 3905 1 13821 4 13674 1 13422 1 15759 1 1799 1 15636 2 1395 1 11147 2 14712 1 6527 1 3676 1 14196 1 9428 2 10617 1 2836 1 11821 1 2960 1 14458 2 3677 1 10088 1 14855 3 8012 1 5016 1 14980 2 10619 2 13682 1 4897 1 16008 1 8631 1 8755 2 13935 1 7133 1 14584 2 11827 1 7380 1 8021 1 1667 1 13031 1 8523 1 16162 1 7638 1 11983 1 6536 1 771 1 12122 1 8285 1 644 1 14209 1 5022 1 8172 1 12905 1 9032 1 15911 7 14212 2 779 1 3929 1 7645 1 14213 1 15544 1 8903 1 3932 1 9723 2 8533 1 15916 1 536 1 13302 1 2577 1 8292 1 13445 1 3798 1 14077 1 14078 1 2857 2 4203 4 2214 1 12003 1 5033 2 1169 1 7904 1 13451 1 12655 1 1562 1 5550 1 9046 1 15922 1 10656 1 5180 2 6161 2 9590 1 3269 88 12521 1 11857 1 14478 1 2861 1 12524 1 9849 1 14606 1 13957 1 9451 1 15788 1 13320 1 9594 1 8787 1 1836 1 15170 1 2864 1 15791 1 2865 1 7159 1 11728 1 8545 1 15171 1 4429 1 13323 1 13461 2 3580 5 13063 3 6053 1 2873 2 12270 1 4925 2 5809 1 14351 2 14352 4 3809 2 15295 1 12792 1 6310 1 1309 1 171 1 790 1 935 1 1180 1 6442 1 11193 1 10128 2 10926 1 12795 1 5298 1 13204 1 4687 1 9860 1 15572 2 7545 1 10131 1 7058 1 13965 1 6178 1 1847 2 16303 1 11474 2 15574 1 7678 1 800 2 7421 2 3716 1 9996 1 10263 1 13726 1 14623 1 6941 1 10812 1 13967 1 11602 1 3820 1 1437 1 5307 2 9203 1 15026 1 2755 1 3596 1 1981 1 15939 1 2882 1 14911 1 9206 1 4829 1 11887 2 15940 3 9208 2 3460 1 1080 1 3601 1 14913 1 8559 1 2483 1 13736 2 12809 1 9354 1 8449 1 15579 1 5694 1 3025 1 2108 1 13482 1 7436 3 11490 3 9493 2 6821 1 1331 1 10824 1 8336 1 12691 1 15821 1 8338 1 10947 2 9219 1 12693 1 3164 1 9087 1 6077 2 2114 1 12556 1 16078 1 8823 4 9364 1 4588 1 6081 1 78 1 13490 1 14120 1 11353 1 4129 1 11493 1 7071 1 3480 1 5841 1 15328 1 3175 1 14126 1 2263 1 6713 1 16328 1 6591 1 12047 1 11625 1 4343 1 15833 2 11217 1 9765 1 12308 1 10424 1 13099 1 4722 2 4001 1 2394 1 12569 1 1596 3 8829 1 13363 2 15704 1 4728 1 16333 1 11221 1 15481 1 15062 1 5720 1 8361 1 10561 1 4734 1 2399 1 4254 1 15339 1 14665 1 7957 1 7321 1 8365 1 15071 1 9639 4 3049 1 484 2 2012 1 8588 1 5725 1 8223 1 14278 1 8980 1 9643 1 15077 2 15078 1 13379 1 5860 2 12978 1 2292 1 10979 1 12716 2 2653 1 2142 1 4365 2 9920 1 5115 1 3200 1 4017 1 5733 3 15858 1 3636 1 12586 4 9650 1 4487 2 12326 1 12460 1 4263 1 6106 1 2658 1 6614 1 4490 1 14026 1 12721 2 9116 1 3354 1 3065 7 14027 1 16234 1 9797 1 6238 4 2531 1 9395 1 2806 1 13388 1 5618 1 222 1 11126 1 16122 1 10867 1 12853 1 16478 1 3757 1 10321 1 14424 1 1120 1 10731 1 7477 1 2931 1 6498 1 1770 1 12467 1 1624 2 11528 2 1627 1 8246 2 3362 1 14823 2 2671 1 2158 1 13264 1 7486 1 4622 1 2036 1 14956 1 13914 1 7350 1 1635 1 1920 2 15741 1 4030 2 618 1 15241 4 1636 1 5503 1 14173 1 14555 1 5358 1 7990 1 7354 1 11133 1 8256 1 8137 1 1784 1 7489 1 623 1 3376 1 1130 1 7006 2 5756 1 9549 1 2172 1 7007 1 3084 4 512 1 8743 1 2426 1 3086 1 4040 1 12744 2 1649 1 4041 1 3898 1 1014 1 10751 2 3665 1 16492 1 4169 1 3900 1 13011 1 7744 1 14838 1 1789 1 11811 1 2691 1 14181 3 1934 1 7120 1 2694 1 11961 1 6129 1 14044 1 13665 1 11424 1 6520 1 12353 1 1389 1 1140 1 14047 4 11427 1 12228 1 14189 1 6644 1 sugary 11593 1 14239 1 12281 1 1596 1 9888 1 2703 1 12809 1 cave_in 4387 1 12705 1 10628 1 13035 1 10131 1 3862 1 8543 1 2613 1 rose_pink 4468 1 3270 8 91 1 1755 1 9186 1 15693 1 16251 1 5556 2 3983 1 16078 1 1709 1 7711 1 7992 1 4143 1 1799 1 2436 1 5347 1 988 1 12958 1 14196 2 6965 1 7321 1 2560 1 2244 1 4529 1 12822 1 8012 1 243 1 5455 1 4169 1 13119 1 2613 2 11217 1 1560 2 9945 2 9602 1 10128 1 10286 1 7904 1 8021 1 1667 1 2590 1 447 1 10701 1 14915 1 1685 1 9266 1 14972 1 14189 1 11474 1 2970 1 7350 1 kick 4944 1 11290 1 387 3 1866 1 252 2 6015 2 702 1 6131 2 6900 1 4335 5 12815 2 10349 1 13422 1 10475 1 15321 2 6080 1 2190 1 8749 1 15637 1 9285 1 3779 1 9228 1 15762 1 12959 1 4129 2 6211 1 10617 1 10154 1 2836 1 14121 4 2490 1 14197 1 2330 1 5082 1 3404 1 9021 1 3678 1 1148 1 14650 1 9632 1 11495 1 16008 1 13495 1 961 1 4343 1 14928 1 13096 8 14585 1 4401 1 11830 1 3493 1 1818 6 13171 1 8408 1 12368 1 7314 2 14270 3 2277 2 4464 1 5714 1 14934 1 4659 1 4466 1 9096 1 11307 1 15591 1 4848 1 12439 2 5173 1 10632 1 531 3 3188 1 15215 1 14274 1 4143 1 13837 1 4412 1 11309 6 6416 1 12970 1 2065 1 1164 1 7774 1 4670 1 8292 1 13633 1 6230 2 11999 1 12249 1 12973 3 7089 1 2214 3 5107 1 2650 1 10443 1 12067 1 8983 1 4300 1 10109 1 2921 1 8914 1 12657 1 3271 51 12141 1 12658 1 9186 1 4982 1 4155 1 10380 2 2802 14 8853 1 11461 1 1300 1 11727 1 10916 1 13526 1 13461 1 2873 1 5876 1 7541 4 436 1 10585 1 4160 3 14423 1 3074 1 10189 11 14160 1 8483 1 12792 1 3211 1 9534 1 6499 1 14163 1 3815 1 996 1 5500 2 15933 1 5424 2 6933 1 1242 1 6748 1 15740 1 10067 1 10131 2 10880 1 11666 1 7733 1 10068 2 8735 1 869 1 1635 1 4933 1 1004 1 10397 1 2311 1 1370 2 11602 2 14173 1 13795 1 15742 4 2418 1 12404 1 5307 1 8738 1 11133 1 9203 1 2608 1 8068 1 12032 1 7804 1 239 1 16196 1 8739 1 14905 1 7224 1 8322 1 2755 1 9546 1 12608 1 2880 1 4446 1 3084 1 6123 4 16199 1 8391 1 2426 1 4998 1 8558 1 16065 1 2760 1 9209 1 1134 1 4169 1 5362 1 10340 1 14838 1 13735 1 13736 2 455 1 16494 1 5366 1 10755 1 5002 1 15390 1 12543 1 8393 3 14776 1 11282 1 15752 2 11686 2 9419 1 16316 1 10018 1 13870 1 12620 1 1140 1 16144 1 hoax 11614 1 5872 1 14117 1 2846 1 11235 2 13171 1 4701 1 14873 1 5810 1 polymerize 15636 1 3272 5 14151 1 13736 1 malaria 15994 1 13609 1 15821 1 4179 1 6462 1 12815 1 15259 1 9760 2 4777 2 9559 1 1395 1 3170 1 6080 1 15041 1 14921 1 13089 2 8884 1 1999 1 14458 2 958 1 893 1 7189 1 13026 3 1401 1 5271 1 9707 1 2902 1 12308 1 13684 2 1596 1 4723 1 6660 1 7765 1 12122 1 3853 1 9576 2 15841 1 9635 1 531 2 8361 1 1671 1 4143 1 6419 1 15966 2 15918 4 3692 1 9308 2 15656 2 6542 1 12916 1 8180 1 9728 1 8776 1 14338 1 13706 1 9920 1 5407 1 5184 1 13521 1 3273 26 7403 1 12011 2 14025 1 1963 1 12848 1 3276 1 5288 2 5347 1 8309 8 8603 1 1061 2 4024 1 12270 1 5878 2 673 6 5494 1 10387 1 8123 1 11327 3 14030 1 7167 6 10195 2 996 1 7731 2 11804 1 2159 1 12732 1 3524 1 6318 1 11541 2 7992 1 9546 1 7006 1 7496 1 15244 2 11002 1 14500 1 3600 1 1259 1 14104 2 14634 1 4512 3 4833 1 5766 1 13223 1 14047 1 11612 1 12228 1 unmechanized 4158 1 15441 1 2303 1 15329 1 unexcitingly 7274 1 3862 1 3258 1 10438 1 snitch 6641 1 6399 1 hitchhiker 2547 1 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16145 1 4847 1 12472 1 327 1 12011 2 12372 1 16116 3 8137 1 14319 1 5153 1 16322 1 9377 1 5965 1 4503 1 7114 1 2262 1 13730 2 6530 1 2509 1 8558 2 7444 2 3599 11 14650 1 1381 2 3087 1 15459 2 15279 1 14791 1 8314 1 12101 1 595 1 2859 1 8180 1 4064 1 13648 1 2366 1 15058 1 14969 1 3816 1 6244 1 11686 1 2814 1 1541 1 7765 2 12296 1 15877 1 10218 1 resort_to 4466 3 1125 1 7286 1 15254 1 10363 1 3145 1 219 1 13832 1 6525 4 3186 1 8156 1 2525 1 3169 1 12721 1 562 1 15436 1 950 2 3859 1 5176 1 12410 1 3032 1 3566 1 3602 6 14545 1 5214 1 6498 1 368 1 14755 1 2581 1 4385 1 7042 1 7560 1 3815 1 4044 1 8209 1 7012 1 10144 1 11686 1 5694 1 13434 1 7313 1 6444 1 12620 1 1389 1 8621 1 10049 2 thwart 4623 1 1660 1 4026 1 13343 1 9833 1 4327 1 6499 1 4605 1 11209 1 14163 1 preemptive_bid 11207 1 7045 2 overtly 11534 1 rivet_line 7139 1 13717 1 149 1 7210 1 discontented 8354 1 10321 1 6230 2 219 2 5648 1 16491 2 9532 2 10211 2 14081 2 8154 1 13743 1 10022 2 16084 1 9400 2 3603 8 10934 1 8797 2 11147 1 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12410 1 1014 3 5437 1 14565 2 13276 2 1256 3 5439 1 14838 4 4889 1 12102 1 6389 3 13982 1 11612 1 1863 1 2111 6 trollope 10163 1 4034 1 9639 1 7393 2 15167 1 3404 1 8674 1 4099 1 12977 1 13035 1 13709 1 349 1 470 1 8849 1 179 1 mechanical_energy 14071 2 3715 1 14674 1 11368 1 9219 1 14469 1 14193 1 12554 1 9246 1 9558 1 3609 7 5686 2 344 3 5011 1 6120 1 15952 1 9018 1 13008 1 9429 2 4256 2 6230 1 13931 1 13713 1 10226 1 10645 1 11327 1 4323 1 6499 2 9971 1 3971 1 7793 1 5729 3 6389 1 7393 1 6792 1 saint_emilion 185 1 10663 1 12284 1 7587 1 558 1 coma 14226 1 12324 1 12391 1 11691 1 8628 1 12145 1 2598 1 14979 1 13460 1 3065 1 11436 1 3610 14 7697 1 6172 1 3404 1 10421 1 10321 1 7413 3 1767 3 11403 1 14579 1 7578 1 11327 1 469 1 833 3 14585 1 10195 3 16127 1 1667 1 15673 1 11021 1 4002 1 11261 1 3367 1 2852 1 4876 1 4964 4 4352 1 531 1 4143 1 14495 2 12374 1 5434 1 972 1 4146 1 12943 1 9640 1 14078 1 5953 4 2789 1 13076 1 1134 1 8559 1 66 1 6542 1 8180 1 11848 1 9728 3 6389 1 599 1 13768 1 188 1 6072 1 13770 3 controlling 5770 1 1793 1 13158 1 14053 1 8153 1 13552 4 16267 1 13674 1 1394 1 11819 1 15897 1 8881 1 3676 1 9285 1 7239 2 11821 2 1805 1 2960 2 892 1 3404 3 6770 1 15403 1 10484 1 1148 1 15903 1 3679 1 4531 1 9705 1 6279 1 5271 1 11826 1 11830 2 13432 1 3918 1 1818 1 10771 4 11985 1 11701 1 14071 1 4660 2 5906 1 14590 1 13436 1 3113 1 4661 1 4537 1 14469 2 1282 2 5654 1 13689 1 15913 1 5027 1 8030 1 10778 1 5655 1 1163 2 16284 1 11571 1 5176 1 11172 1 7774 1 13944 1 4203 3 3800 1 6542 1 5033 1 1169 5 1053 1 16177 1 9587 1 9728 1 7260 1 1055 2 10242 1 6546 3 14604 1 2988 1 4090 1 12658 1 5672 1 14875 1 12393 1 37 1 1836 1 14480 1 6924 1 2598 1 2992 1 4813 1 6170 1 13461 1 13063 1 12528 2 3582 1 2997 1 3138 1 7164 1 676 1 4562 1 14616 1 790 1 6441 1 14236 5 1180 1 14892 1 12795 1 9604 1 5682 1 14360 1 6445 1 7169 1 938 1 4933 1 7802 2 9201 1 7923 1 9203 1 16196 1 11884 1 10521 1 8323 1 14771 1 14626 1 11339 1 10686 1 15940 1 12034 1 8076 1 5436 1 13076 1 1325 1 3971 1 10014 1 11486 1 9083 1 4707 1 8561 1 2615 1 1328 1 4835 2 13982 1 7436 1 11612 3 15688 1 4943 1 4944 1 6075 1 9219 1 702 1 6204 2 16321 4 10023 1 15470 3 11757 1 2624 1 8202 1 1336 2 10951 1 3611 49 5450 2 1999 1 1591 1 11760 1 12430 1 15831 1 15958 1 2121 2 5214 1 13495 1 13096 1 15833 1 4843 1 9092 1 12570 1 11769 1 8829 1 472 1 5331 1 10429 1 15591 2 12315 1 12439 1 9375 1 13888 2 6228 4 14005 1 6230 2 2789 1 213 1 12448 1 1104 1 15077 4 7458 1 595 1 11239 1 13246 1 15720 1 4016 1 12077 1 8238 1 15729 1 3065 2 6991 1 5873 1 11126 2 10867 1 4753 1 11400 1 11657 1 9801 1 3074 1 6373 1 8863 1 6499 1 12856 4 12082 1 11662 2 11406 2 12596 1 5500 1 10196 1 7730 1 9663 1 5745 1 13263 2 2815 1 9665 1 12730 1 4164 1 1246 1 1370 1 7218 1 12475 5 15878 1 7358 1 10206 1 1646 1 13008 1 510 1 135 1 10891 1 15884 1 11002 1 7230 1 1134 1 10465 1 5362 4 6890 1 7362 1 629 1 15251 1 12102 1 6389 1 10343 3 11686 3 11424 1 15992 1 12620 1 6894 2 10605 1 melt_down 14226 1 13003 1 14428 1 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10211 2 4630 1 6890 1 13408 1 1260 1 15751 1 5366 1 12617 1 12616 1 6389 3 12620 2 reed 11431 1 5555 1 7657 1 13899 3 5771 1 884 1 4982 2 7269 1 6733 1 15230 2 12815 1 10724 1 4488 3 4950 2 15695 1 11292 1 7505 1 3276 1 2598 1 2436 1 3839 2 5347 1 3354 2 1028 1 1943 1 7574 2 360 2 2260 2 1909 1 2560 1 1999 2 13354 2 8012 1 8702 3 2263 2 4591 1 8382 2 14351 1 7240 1 7166 1 4187 6 2563 1 8281 1 11156 1 3814 48 3616 1 14892 16 14132 1 9160 3 7799 4 9860 5 6535 1 15301 1 556 1 15181 1 6801 1 4464 1 11474 2 1576 1 5906 1 12731 1 4072 1 4933 1 9635 1 12342 1 6287 1 10632 1 3015 1 7492 2 952 2 16435 1 9172 1 953 1 5202 2 2244 1 12537 1 4998 4 9680 1 15118 2 9079 2 11999 1 11782 1 8368 2 1354 1 8875 1 3744 13 16174 2 2581 1 13736 3 1957 1 16314 1 7012 1 13664 1 11282 1 8393 2 13895 2 5831 3 12518 1 11686 2 2921 1 418 2 14972 2 12717 1 6894 10 boisterously 5594 1 11008 1 10956 1 8821 1 14023 1 14159 1 2208 1 4701 1 3229 1 15320 1 11282 3 12795 2 helpfully 1930 1 15604 1 gavial 9875 2 5718 1 1770 1 5285 1 16334 1 pinus_californiarum 6007 1 9452 1 3444 1 14092 2 293 1 9400 1 4829 1 au_fait 4164 1 4149 2 7366 1 6204 1 12041 1 8209 1 6747 1 13214 1 3874 1 14249 1 13576 1 1656 1 6853 1 chuang 15170 1 3404 2 15207 2 12184 4 13214 1 10430 1 1907 2 14332 2 265 2 11316 2 6367 2 13674 2 rehabilitated 2567 1 11141 2 9067 1 15866 2 15137 1 10503 2 12181 2 13408 2 13674 1 12937 1 11523 2 6359 3 5911 2 8298 2 16387 3 1274 1 15301 2 14636 2 2036 1 9665 2 superpower 10457 1 8065 1 15235 1 15961 1 10430 1 13900 1 8559 1 5104 1 6278 1 10985 1 1559 1 10325 1 1053 1 5151 1 7581 1 1907 3 1667 1 3223 1 13608 1 11935 1 trapezoidal 4591 1 13314 2 13367 1 5718 1 4720 1 13021 1 7012 1 7120 1 3676 1 12094 1 14108 1 7058 1 14047 1 809 1 4464 1 tighten 4944 1 9056 1 3679 1 14336 1 7923 1 2581 1 6499 1 14199 1 3614 1 12849 1 12193 1 3616 1 623 1 4853 2 7565 1 1561 2 6244 1 12723 1 8436 1 5784 1 3703 1 4994 1 3817 7 5702 1 11474 1 2425 1 1369 2 vienna 15741 1 6786 1 5760 1 190 1 7178 1 10811 1 7269 1 14838 1 5810 1 12848 1 8363 1 6168 1 9116 1 414 1 4110 1 2086 1 359 1 11686 1 13191 1 12939 1 9546 1 9420 1 16323 1 10154 1 3818 5 9817 1 kashmiri 2613 2 3582 2 14232 2 12848 2 8433 2 717 2 commodity_exchange 9906 1 7302 1 9717 1 sweet_pea 884 1 6501 1 9216 1 8886 1 9210 1 retributive 1635 1 15729 1 12141 1 8442 1 1055 1 14107 1 5180 2 7839 2 16051 1 7722 1 holy_of_holies 15718 1 3404 1 11108 1 2865 1 13684 1 1608 1 6531 1 468 1 1646 1 14121 1 smudged 8438 1 4588 1 6011 1 300 1 14974 1 5288 1 3436 1 15933 1 15459 1 9532 1 1357 1 15110 1 3544 2 10833 1 15155 1 mildly 7532 1 16471 1 8542 1 7183 1 12882 1 4263 2 3983 1 12849 1 2658 1 13422 1 5126 1 16411 1 11796 1 12590 1 12207 1 1087 1 2263 1 3706 2 5878 1 9057 1 5455 1 3992 1 2152 1 9291 1 5213 1 7634 1 1621 2 4269 1 1624 1 9765 1 897 1 13394 1 3760 2 15700 1 11259 1 10959 1 5972 1 1403 1 6801 1 14830 1 8581 1 11474 2 9768 1 14986 1 9405 1 3793 1 6661 1 16459 1 13967 2 3819 15 9635 1 11674 1 5173 1 3258 1 9719 1 5307 1 531 1 3623 1 1709 1 10906 1 12968 1 7711 1 4143 1 13836 1 12680 1 97 1 9138 1 14365 1 3893 1 9270 1 809 1 14866 1 4969 1 2134 1 2244 1 2096 1 13867 1 6977 1 2945 1 10463 1 7230 1 16438 2 12540 1 5915 1 311 1 1893 1 6727 1 67 1 12809 1 8774 1 1560 1 1291 1 10373 1 7904 2 850 1 1685 1 14972 1 6894 1 5115 1 7124 1 gouge 7421 1 3158 1 6025 1 8346 1 5180 1 14169 1 8853 1 11467 1 12321 1 8474 1 critical 14052 3 7368 1 8157 1 11693 2 7374 1 6527 2 4783 1 7759 1 10617 1 15901 1 7509 2 6139 3 9021 3 5778 1 9430 1 9956 1 14719 1 12898 2 14066 3 3107 1 3918 1 8761 1 8524 1 13171 2 1669 1 9830 1 906 1 4660 3 9296 1 11702 1 12643 1 14328 2 16391 1 2337 1 5535 1 5654 1 531 1 15915 1 5543 1 7774 1 9305 1 15157 2 16288 1 918 1 8178 1 13577 1 1166 2 7649 1 13305 1 6667 2 8650 1 14738 1 8775 1 4912 1 15000 1 8180 2 14225 1 1053 7 1055 1 1054 2 14338 1 10377 1 5180 1 6793 1 2349 1 3269 1 15561 1 5412 1 13959 1 15170 1 12145 4 6168 2 6924 1 5041 2 12268 1 5415 1 2997 1 7788 1 6053 1 10253 2 12270 4 4925 1 6678 2 13064 4 9989 4 6929 2 11327 1 6930 1 10672 1 3813 6 7919 1 15443 2 12795 1 15933 3 8436 1 1973 1 7419 1 10395 1 5300 1 8438 1 13209 1 16428 1 3145 2 11602 1 10265 2 3820 77 9477 6 14904 3 9203 1 944 1 62 1 10681 1 13214 3 8322 1 8192 3 4828 1 10686 4 3022 2 13973 1 7178 1 14913 3 8940 1 813 1 10524 1 15312 2 12159 1 4706 2 12416 1 8946 1 12419 2 5317 1 10018 5 8332 1 2111 1 9493 3 5957 1 3834 1 1330 1 10415 1 14112 2 5960 3 12421 2 12556 2 2115 1 15470 3 4950 1 12041 1 9886 1 14643 1 13490 3 5319 1 7824 1 15199 1 8202 1 10952 1 15472 1 11620 1 5702 1 8344 1 7189 1 2002 1 6715 1 9506 1 2003 1 1466 1 6969 1 3036 1 14657 1 4136 2 6833 6 1596 3 201 2 14395 1 14271 2 4728 1 9243 1 8834 1 8582 1 9775 1 6974 4 14140 1 12970 1 5102 2 15849 2 9521 1 483 1 8108 1 5475 1 484 2 8223 1 1607 1 15077 1 5480 1 13380 1 8984 1 1611 1 11935 1 6853 1 7969 2 15228 4 9650 1 1615 1 9793 1 8853 1 6988 1 12848 1 15089 3 6106 1 4264 1 8479 1 737 1 5347 1 1907 2 3874 1 4023 1 6367 1 12724 1 3072 1 14292 1 1501 2 14682 1 5244 2 14030 5 16356 1 9400 1 9663 1 5628 1 3075 2 1916 3 10879 1 5882 2 2036 1 9665 1 1920 1 4164 4 3218 1 12735 2 4761 1 3889 1 15743 1 7992 1 1784 4 622 1 12742 1 15115 2 5888 1 3225 1 2041 3 6633 1 2945 3 6257 2 15120 1 16492 1 13276 1 3087 1 4887 3 10340 1 6890 1 1650 1 7009 1 3229 1 6635 1 16494 1 5366 1 11684 1 4511 1 629 1 11282 1 6262 2 11963 1 1656 2 12228 1 12877 1 13814 3 last_judgment 13794 1 15440 1 15459 1 5214 1 13280 1 12011 2 11539 1 1523 1 13674 1 16494 1 2804 1 6685 1 1229 1 2326 1 15729 1 2504 1 13192 1 7434 1 plum_tree 1560 1 2468 1 1667 1 13760 1 4907 1 4286 1 14047 1 brunch 1931 1 3377 1 10866 1 7367 1 4332 1 invigorating 1331 1 4017 1 15670 1 13076 1 4531 2 9704 1 12051 1 7430 2 11674 1 9601 1 14323 1 2613 1 12011 1 12147 1 7126 1 5271 1 1595 1 7793 1 3821 5 2061 1 14319 1 14 1 15233 1 3377 1 6894 1 lan 14077 1 7157 1 5131 1 1409 1 6265 1 14864 1 8523 1 6596 1 11396 1 4169 1 14658 1 2581 1 10005 1 6279 1 4459 1 8869 2 collateral 15581 1 820 2 13872 1 12139 1 8915 1 9423 1 8822 2 702 1 14259 1 7851 1 7210 1 15695 1 11819 1 10456 1 8308 1 6238 1 6211 1 3480 1 4751 1 673 1 14424 1 11 1 11763 1 15330 1 6404 1 5272 1 8435 1 8021 1 10770 1 15572 1 14759 1 16192 2 3884 1 12733 1 11987 1 15484 1 3822 12 7002 1 4573 3 3192 1 14665 2 4907 1 7256 1 12943 1 7496 1 7526 1 6423 1 8647 1 6980 1 2613 1 14376 1 8813 1 5512 1 8449 1 6389 1 10786 1 31 1 11789 1 12620 1 ludicrous 7569 1 15397 1 10310 1 15129 1 14750 1 2352 1 13674 1 12848 1 11693 1 12145 1 16119 1 13490 1 7239 1 8380 1 1147 1 7410 1 3951 1 10088 1 8314 1 4594 1 227 1 12085 1 3881 1 996 1 3816 1 12795 1 10328 1 6146 1 7314 1 13103 1 7639 2 7170 1 4932 1 4568 2 8998 1 5302 1 1004 1 13795 1 12602 1 8097 1 3824 9 3652 1 7956 1 14626 1 9778 1 16436 1 14372 1 14077 1 16202 1 1985 1 5227 1 10972 1 5364 1 2214 1 3229 1 13606 1 595 1 7903 1 6576 1 13765 1 317 1 4707 1 8946 1 2111 1 13183 1 sift 5579 1 8726 1 12718 1 7148 1 1260 1 3701 1 3396 1 3823 5 1395 1 14969 1 623 1 13263 1 126 1 13007 1 12177 1 12352 1 5756 1 953 1 11022 1 13383 2 mahatma_gandhi 421 1 6404 1 3679 1 14185 1 13551 1 10899 1 5688 1 536 1 5271 1 dive 4944 1 7365 1 16182 1 8592 2 5444 1 3979 2 702 1 4946 1 1961 1 4335 3 148 1 2802 1 13552 1 14228 1 7692 2 16078 13 3169 1 15897 2 10725 1 3398 1 9055 1 13133 1 5616 1 11437 1 12463 1 14121 1 2465 1 6400 1 5618 1 4529 4 5383 1 10870 3 10511 2 6871 4 1595 1 8405 1 2052 1 4401 2 15406 1 2903 1 15058 1 13432 1 6933 1 1242 1 2444 1 16055 1 397 1 2089 1 6905 1 7798 1 1596 1 10771 4 2277 1 3038 1 233 1 8438 1 7421 1 9470 2 7140 1 12643 1 8213 1 11307 1 4995 1 13968 1 5653 2 1925 1 12799 1 12439 1 6568 1 4143 1 13801 1 15455 1 3825 24 2315 1 12092 1 7291 2 7587 1 3376 1 8322 2 3223 1 6354 3 7958 1 13008 1 4446 1 12577 1 781 1 11641 1 9639 1 7326 1 8108 2 12973 1 16065 1 1134 1 3302 1 4228 2 5858 5 14838 2 658 1 1136 1 6542 1 14634 1 4387 1 14281 1 16494 1 14106 3 15390 1 2548 2 15784 1 1055 1 14504 2 11686 1 2797 1 6894 1 13581 2 fortaz 7532 1 14251 1 15918 1 16201 1 6831 1 15251 1 16026 1 ethics 9387 1 13956 1 14918 1 11552 1 12165 1 15287 2 5483 1 3133 1 8156 1 12555 1 5412 1 15634 2 12848 2 14879 1 3839 2 2326 1 15729 2 14057 1 1801 1 10899 1 10989 1 1873 1 10385 1 4590 1 2332 1 2266 1 8314 1 16330 1 16191 1 1065 3 12672 1 8088 1 8632 1 12398 1 1180 1 16274 1 13029 1 8209 1 2749 1 12569 1 9861 1 2815 1 6030 1 4164 2 10809 1 529 1 11635 1 14398 4 10430 1 12735 2 2337 1 16019 1 1784 1 7222 1 9868 2 12534 1 10001 1 3826 16 2038 1 13806 1 9270 1 12776 1 16436 1 8176 1 4256 2 15940 1 4041 1 6230 1 13075 1 10564 1 16031 1 14600 1 10304 1 15459 1 16438 1 4169 1 4802 2 11316 1 4701 1 15656 1 1607 1 5918 1 11812 1 12617 1 15191 1 6668 1 9976 1 14338 1 1055 2 8946 1 10018 1 5317 1 13285 1 5957 1 2956 1 3671 1 keratosis 8180 1 11189 1 16196 1 7368 1 5771 1 15785 1 12439 1 6226 1 2873 1 diabetes 12585 1 1211 1 12689 2 9097 1 12732 2 10757 1 14258 1 9922 2 4004 1 3539 1 15695 2 14979 1 8739 1 10521 1 13089 1 3827 14 7441 1 2882 1 5240 1 4529 1 12943 2 7715 1 15918 1 13973 3 7413 1 2121 1 10387 2 4169 1 14579 2 7189 1 15441 1 974 1 13408 1 10525 2 15870 1 6575 1 1169 1 8180 4 3729 1 6328 1 6389 1 8114 2 15497 2 12261 2 5766 1 15572 1 3435 1 10772 2 15123 1 13770 3 in_stock 12942 1 6633 1 7810 1 15012 1 3112 1 6180 1 2208 1 10569 1 5608 1 16410 1 13720 1 5583 1 10633 1 2564 2 2797 1 4118 1 4994 1 14626 1 3828 5 13249 1 6452 1 alcidae 3282 1 13878 1 1457 1 cultured 4728 1 15987 2 3145 2 3113 1 9359 1 5094 1 13174 2 9542 2 7769 1 12680 2 13214 1 13253 1 4317 1 15115 2 2560 1 2173 2 3829 7 3072 4 4202 1 12633 1 12498 1 8718 1 8775 1 1560 2 7544 2 15611 1 13706 1 juncaceae 11292 1 6449 1 3760 1 11147 1 8983 1 2436 1 5347 1 1457 1 12109 1 9449 1 15181 1 harem 10182 1 13171 1 8751 1 14832 1 11269 1 add_together 8436 2 1602 1 9819 1 universal_solvent 7992 1 4361 1 12681 1 6623 1 scapular 13422 1 10408 1 2263 1 14160 1 8822 1 3980 1 5616 1 989 1 13504 1 1953 2 normally 4283 1 16374 1 5261 1 7366 1 8879 1 12230 1 3672 1 12231 1 2828 1 9423 1 14574 1 12623 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1 16418 1 5935 1 2229 1 6681 1 15567 1 2083 1 11327 8 14616 1 935 1 6932 1 441 1 3815 1 6442 1 2366 2 4437 1 15933 1 12795 2 795 1 11054 1 13467 1 15572 3 10131 1 5299 1 15302 1 6563 1 9337 1 3712 1 11474 1 8438 1 4933 1 6179 1 11734 1 9342 1 10265 1 14241 1 6318 1 14902 1 3957 1 14904 1 14243 1 5686 2 11332 1 10521 1 14365 1 10685 1 3016 1 5056 1 14911 1 2096 1 12943 1 7810 1 15812 1 9079 1 4938 1 5436 1 14103 1 8939 2 14913 1 3830 81 12540 1 1986 1 3464 1 10525 1 3970 1 13736 1 14634 1 11204 1 6328 2 8449 1 1860 1 698 1 15190 1 4707 1 12416 1 16315 1 13608 1 5694 2 9491 5 11489 1 8563 1 11612 5 11490 1 7818 1 16210 3 4583 1 9359 1 15467 1 9219 2 14259 2 6958 2 11349 1 10541 1 15470 1 16078 1 11755 1 3171 1 6080 1 2117 1 8083 1 4951 3 11758 2 12043 1 7824 1 10543 1 8456 1 15472 2 13613 1 10544 1 14121 1 8084 1 16082 1 5586 2 10421 3 10698 1 4591 1 2263 2 16217 1 3992 5 1593 1 7189 1 7308 1 5214 1 4343 1 11914 1 14130 1 11217 1 10161 1 12308 4 8209 1 3493 1 15058 1 12566 2 13099 1 2495 1 13499 1 4001 1 1596 1 11631 2 2635 1 1736 1 12964 1 11367 1 4139 1 8356 1 8463 2 13996 1 9635 1 4730 1 10293 1 2008 1 10559 1 13110 1 12059 1 14403 1 969 1 7587 1 4856 1 15339 1 11105 1 7319 1 6100 1 8837 1 14006 1 4606 1 16098 1 11232 1 484 2 2136 1 13893 1 3744 1 5104 1 5608 1 7718 1 14147 1 11239 1 10978 3 2797 1 12716 1 8115 1 2016 1 13897 1 9920 2 8726 1 12718 1 16349 1 4867 1 4017 1 15725 2 11120 1 3061 1 4260 2 732 1 4368 1 12586 1 8595 1 16113 2 4156 1 14948 2 7724 1 8379 1 5346 2 13779 1 9116 2 5347 1 14419 1 11796 1 6238 2 13904 1 6367 2 10583 1 989 1 121 1 4751 1 6369 2 5739 1 2928 1 10867 6 5878 1 4159 1 5128 1 15235 2 1501 3 8124 1 9928 1 6498 1 6241 1 6997 1 1915 1 227 1 8246 1 13648 1 5993 1 16483 2 4989 1 9663 1 9664 1 15875 1 7733 1 15877 1 869 1 15515 1 1635 1 12730 1 6501 3 12732 2 15987 1 13004 1 3367 1 11672 1 12472 1 10741 1 7218 1 10883 1 14691 1 10333 1 1638 1 13402 1 14693 1 3082 1 7740 1 8740 1 13272 1 3223 1 9546 1 9270 1 13656 1 7006 1 5756 1 12870 1 1011 1 3766 1 2175 1 11002 1 14964 1 10465 2 13150 3 1385 1 2321 1 16258 1 3900 3 12745 1 2691 1 6760 1 6515 1 11812 1 7119 3 12225 2 14969 1 2953 1 3534 1 3386 1 4512 1 6130 1 13665 3 11424 1 12353 1 1389 2 14047 1 12228 4 6894 1 5368 1 14190 1 1657 1 dazedly 13076 1 10247 1 1802 1 8255 1 5573 1 16356 1 10051 1 standardization 7362 1 4164 2 8732 1 13380 1 13415 1 11395 1 2652 1 11141 1 16419 1 4123 1 7923 1 9469 1 9920 1 eriocaulaceae 3555 1 3613 1 3310 1 2436 1 13343 1 1457 1 3992 1 16438 1 2866 1 11123 1 6871 1 6708 1 family_phyllostomatidae 1457 1 projective_geometry 14387 1 3158 1 10018 1 9033 1 1459 1 computationally 1593 1 7649 1 3046 1 elf 13898 1 14993 1 5883 1 12965 3 12456 1 12326 1 14324 1 6760 1 1595 1 13800 1 2608 1 8555 1 1943 1 15026 1 12418 1 12959 1 6444 1 16028 1 1834 1 15713 1 10780 1 fascinate 5226 1 6713 1 13428 1 13552 1 12363 1 759 1 15932 1 7012 1 9613 1 6946 1 5180 1 12863 1 11572 1 mandibular 13063 1 11171 1 8202 1 11887 1 bart 174 1 8529 1 11147 1 9168 1 15356 1 7033 1 11584 1 15155 1 9442 1 14186 1 12524 1 mesial 3158 2 8868 2 4446 1 7882 2 14519 2 communist_china 2161 1 9420 1 11971 1 1755 1 parable 905 1 13696 1 8680 1 2155 1 1229 1 9107 1 13192 2 12021 1 9496 1 1611 1 7733 1 8173 1 8055 1 13863 1 66 1 5810 1 3476 2 1176 1 15312 1 amygdalin 14658 1 4933 1 15636 1 6980 1 10491 1 6392 1 14380 1 11017 1 incoherently 7828 1 2325 1 9112 1 12795 1 4886 1 2703 1 1209 1 9233 1 4234 1 5307 1 5138 1 festivity 3396 1 11068 2 14319 1 9079 1 8120 1 13027 1 14082 1 10697 1 6693 1 charybdis 4463 2 3329 1 10628 1 2306 1 6347 1 8026 1 1502 1 8272 1 10131 2 10745 1 8128 1 10905 1 mounting 4944 1 14742 1 13484 1 3061 1 3672 1 12231 1 287 1 13551 1 289 1 8627 1 14849 1 15131 2 12848 1 4263 1 5235 1 1618 1 8307 2 4743 2 5584 1 10899 1 12987 1 2262 1 4529 1 14855 1 9800 1 3679 1 9705 1 4532 1 8517 1 7103 1 11695 1 7636 1 13202 1 9506 1 14201 1 4873 1 12020 1 9711 1 13171 1 6444 1 9094 1 4464 2 1002 1 11833 1 11367 1 3793 1 12643 1 7218 1 343 1 7032 1 11882 1 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1583 1 12811 1 7904 1 1327 1 12546 1 9216 1 1685 3 2043 1 13982 1 1656 1 2921 2 oncology 15310 1 10884 1 1969 1 stodgy 11753 2 9795 2 6371 2 14854 2 6791 2 3393 2 15535 2 11358 2 7410 2 3938 2 loop 5442 1 6198 1 7365 1 16447 1 10824 2 13668 1 572 1 12229 1 5071 1 14844 1 3979 56 14846 1 4335 2 7692 3 3477 6 7505 1 2190 1 15322 1 12557 1 8571 1 3320 1 1802 1 1999 1 9153 1 2838 1 4529 4 6713 1 5779 1 14461 2 2270 2 4530 1 8515 2 9704 1 9705 1 5649 2 14927 1 7190 2 5385 1 6279 1 5271 1 8018 1 14267 1 3616 1 13752 1 8021 1 5784 1 12569 1 10771 4 1736 1 9094 2 4464 2 13567 1 13939 1 2850 1 10965 1 9510 1 2500 1 14469 1 13571 1 5653 2 5023 1 2008 1 4143 1 7145 1 9037 1 13887 1 4253 3 7837 4 1604 1 14005 1 11312 2 9909 1 3121 2 7256 1 12971 1 4672 1 14009 1 2857 1 2136 1 4358 1 9175 1 2510 1 8980 2 13893 2 2581 1 14279 1 15423 2 1957 1 9728 1 14147 1 12978 1 1561 2 14083 1 14021 3 16178 1 599 1 5180 1 4154 1 2986 1 5182 1 8373 1 15228 1 602 1 11119 1 12718 1 3750 1 13314 1 9184 3 425 1 664 2 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9176 1 5033 1 4738 1 14225 2 7962 1 13765 1 8779 1 4423 2 11517 1 12585 1 9527 1 219 1 856 1 6794 1 15285 2 7049 1 12845 1 3204 2 3064 1 9795 1 13779 1 14536 2 7536 1 4429 1 7783 1 8380 1 15093 1 10989 1 3582 1 1176 1 7410 1 11188 1 12725 1 3072 1 7729 1 13064 3 6498 1 13260 1 1309 1 6499 1 16356 2 227 1 8246 1 10324 1 8799 1 5500 2 15105 1 12795 1 6747 2 10328 1 4688 1 2369 1 13863 1 4495 1 7170 1 6801 1 2036 2 12090 1 15574 2 4932 23 3077 2 2161 1 8998 1 13795 1 12602 1 4499 1 9477 1 2419 1 4761 2 12220 3 3824 1 14434 1 12534 2 6945 1 14905 2 4110 1 14626 1 15115 1 12940 1 5888 1 15517 1 5688 1 7114 1 3084 1 16436 1 13075 1 13078 1 15942 1 14838 1 13606 1 4230 1 6576 1 6328 1 7012 1 10144 2 15191 1 9947 1 12416 1 317 1 10343 1 12415 1 11686 1 1328 1 8946 1 5694 1 5068 1 5317 1 1861 1 10081 1 dada 8292 1 16494 1 12471 1 9506 1 5500 2 7729 1 288 1 11270 1 13758 1 13260 2 7181 1 1947 1 15663 1 hyperfine 1590 1 1562 1 4099 1 12353 1 7120 1 11349 1 10561 1 7210 1 13064 1 wolverine 7571 1 13726 1 15331 1 1212 1 2742 1 9688 1 15426 1 4829 1 926 1 recitation 7582 1 4237 1 15987 1 3242 1 15459 1 10364 1 7762 1 12003 1 4093 1 5908 1 4950 1 7135 1 6630 1 996 1 10907 1 14 1 1997 1 2797 1 13543 1 3527 2 5913 1 12571 1 14232 1 11322 1 13104 1 990 1 dehisce 14131 1 1932 1 3760 1 5849 1 9635 1 254 1 5173 1 1140 1 2997 1 presidentially 16419 1 125 2 6841 1 gross 0 1 9112 1 4712 1 13186 2 9650 1 15129 1 10789 1 11520 1 13610 1 4156 1 2863 2 7371 1 13130 1 7238 1 16322 3 2624 1 5319 1 4528 1 7239 1 10418 1 6171 1 14884 1 5190 1 14854 1 16149 1 6657 1 6713 1 13621 1 14461 1 8314 2 7762 1 6929 2 4958 1 13169 2 8282 1 7133 1 16052 2 13648 2 2366 1 10674 2 12569 2 15572 1 6283 1 14204 1 2057 1 4003 1 1635 1 906 2 2907 1 448 1 4934 17 15241 1 7585 1 3293 1 7834 2 4694 2 15025 2 62 1 8362 2 12126 1 16338 1 1711 1 9479 2 7587 1 7741 1 10043 1 953 1 7455 1 1552 1 16398 1 12579 1 11716 1 5474 2 3428 1 15220 1 11235 2 5279 1 7999 2 4577 1 4701 1 16344 1 12810 2 629 1 14969 1 11112 1 6043 1 2407 1 4707 1 144 1 3161 1 12353 1 6161 1 4516 2 14189 1 14049 1 11584 2 13581 1 suharto 13422 1 1837 1 4521 1 15746 1 14686 1 8502 1 11847 1 16117 1 6835 1 12059 1 personality_test 8292 1 5233 1 1211 1 1053 1 14147 1 13011 1 12992 1 13299 1 14969 1 15840 1 dissociate 9755 2 15228 4 6011 1 15430 1 5120 2 9342 1 8637 3 10453 1 10816 1 4935 9 3258 1 7585 1 389 1 10541 1 11922 1 1836 2 10906 2 1395 1 3676 1 13214 1 15880 2 2504 2 7784 1 4502 2 6367 1 2705 1 4447 2 9153 2 4357 1 16438 7 15715 1 11403 2 13408 1 11139 1 15738 2 13380 1 11284 1 10656 1 1834 1 6792 2 15806 1 7934 6 6894 1 farmland 3436 1 927 1 7266 1 10473 1 4155 1 10538 1 13821 1 15232 1 15563 1 15695 1 5487 1 3874 1 3875 1 13063 1 11620 1 2149 1 3444 1 5648 1 14716 4 12274 1 13647 1 16244 1 1207 1 13564 2 13434 3 14658 2 471 2 7421 1 4659 2 4933 2 3145 1 15212 1 5332 1 4936 12 15911 1 3189 1 9936 1 6289 1 2062 1 3796 1 13576 1 3859 1 15028 1 7255 1 14078 1 1014 1 625 1 14964 1 13118 1 5857 1 14838 1 16104 2 13449 1 3158 1 12543 1 5036 1 convallariaceae 15241 1 5173 1 5849 1 4295 1 11414 1 phase_iii 16164 1 62 1 5633 1 5366 1 14918 1 6010 1 11446 1 15339 1 9546 1 10131 1 1915 1 5215 1 531 1 7322 1 portable_computer 2720 1 10191 1 7191 1 5033 1 12031 1 11406 1 12145 1 11373 1 13273 1 15093 1 13354 1 incrimination 12529 1 12216 1 11330 1 12249 1 14682 1 2122 1 5263 1 5365 1 3360 1 13851 1 9203 1 6849 1 9427 1 16177 1 10590 1 14018 1 14540 1 2110 1 11701 1 nondollar 3260 1 2231 1 15251 1 5218 1 7626 1 2581 1 1475 1 1595 1 dirigible 1211 2 13785 1 10535 1 1884 1 16065 1 8907 1 2547 1 4864 1 14738 1 14812 1 280 1 13801 1 16494 1 6080 1 7983 1 2045 2 12353 1 12360 1 scanner 7800 1 5245 1 13844 1 7806 1 11330 1 7497 1 1494 1 14464 1 14828 1 9920 1 oral_stage 5442 1 12270 1 15245 1 13299 2 8685 1 1219 1 16047 3 12669 1 3256 1 8821 1 2247 1 6247 2 8180 1 1428 1 16494 1 11559 1 4707 1 3223 1 10067 1 7545 1 13576 1 battle_of_marathon 13505 1 5221 1 10868 1 3785 1 13284 1 8942 1 4323 1 14527 1 lass 4198 1 9532 1 2052 1 6411 1 5180 1 5830 1 7230 1 saint_thomas_aquinas 1180 1 920 1 5951 1 5210 1 7605 1 8040 1 4162 1 5648 1 gemuetlichkeit 5934 1 13648 1 14225 1 3483 1 1099 2 7733 1 4823 1 14738 1 wag 4588 1 9340 1 12718 1 9826 1 1738 1 7899 1 10131 2 7314 1 4352 1 2142 1 10991 1 sweeten 12838 1 15724 1 6731 1 6480 1 8223 1 16081 1 4228 1 14707 1 10742 1 8678 1 1087 1 12809 2 eumycota 3404 1 14499 1 16478 1 3200 1 11730 1 1510 1 5361 1 prattling 5570 1 9830 2 1601 1 2325 1 7324 1 13360 1 12795 1 10020 1 16055 1 16259 1 13335 1 9886 1 lieutenant_commander 12123 1 1379 1 2136 1 15561 1 6652 1 4029 1 11584 2 4304 1 woodcutter 3320 1 340 1 2179 1 8530 1 7876 1 12218 1 7310 1 13089 1 unobstructed 7935 1 4659 2 4089 1 2771 1 3310 1 4846 1 8987 1 4017 2 7368 1 11537 1 704 3 15230 1 11839 1 14576 1 1641 1 13405 1 10743 1 15880 1 12425 1 4881 1 3320 6 6211 1 10779 1 4129 2 8290 1 3546 1 13493 1 6386 1 1032 1 4937 9 10956 1 10305 1 7649 1 10871 1 3744 1 6026 1 15423 1 10325 1 13497 1 16494 1 7581 1 2231 1 2306 1 553 1 10392 1 14044 2 2918 1 13434 1 10067 1 8408 1 7486 1 therefore 12813 1 14255 1 1142 1 3241 1 4332 1 1660 1 13669 2 14918 1 8153 1 15948 1 702 1 15399 1 13490 1 9228 1 8512 1 5321 2 10544 1 2705 1 11081 2 2838 1 4529 1 6713 1 9021 1 5778 1 3032 1 5901 1 3992 2 5648 1 6344 1 6345 1 4790 1 6969 1 10092 2 10426 1 14658 3 3792 1 10903 1 9830 1 16164 1 10492 1 4466 1 1737 1 12643 1 529 2 11773 2 13996 1 3416 1 15145 1 13626 1 2852 1 8834 1 2337 1 2338 1 343 1 3335 1 4965 1 11308 1 5470 1 778 1 9167 1 16026 1 14596 2 1099 1 5913 1 5225 1 211 1 14600 1 14009 2 9247 1 6725 1 10439 1 15548 1 3744 1 9971 1 1893 1 3938 1 8226 3 14410 1 2918 4 9648 1 15283 1 6165 1 12325 1 4867 1 10983 1 15665 1 15430 1 13956 1 4260 1 9390 1 929 1 6236 1 14949 1 12785 1 1228 1 13780 1 12395 1 15436 1 5872 1 10664 1 3874 1 863 1 5240 1 5491 1 10120 2 5877 1 10321 1 4159 1 7413 1 12019 1 1064 2 5810 2 12994 1 16356 2 14616 1 10874 1 3815 2 6685 1 2366 1 936 1 996 1 15933 1 5628 1 13467 1 15572 1 4689 1 13792 1 4271 1 10809 2 10072 1 10265 1 6446 1 8928 1 945 1 3295 1 2422 1 6249 1 9940 1 14558 1 14626 1 2377 1 7226 1 12537 1 2245 1 15885 1 5254 1 2759 1 4938 23 7230 1 383 1 3830 1 8942 1 15312 1 11812 1 4771 1 10342 1 14969 1 1450 1 68 2 8393 1 8946 1 10018 1 6071 1 10218 1 charmed 484 1 14002 1 12020 1 12380 1 4345 1 5068 1 15279 1 3536 1 12620 1 12011 1 6161 2 833 1 1595 1 179 1 oleic_acid 4652 1 9608 1 14010 1 3992 1 8338 1 1357 1 12426 1 10473 1 1596 1 12353 1 11327 1 3084 1 impasse 12813 1 11309 1 2426 1 14748 1 1053 1 10018 1 7581 1 15472 1 15330 1 tape 7752 1 12297 1 5833 1 3473 2 8153 1 9759 1 4335 1 6340 1 7371 1 4950 3 13422 1 3907 1 4589 1 4059 1 8750 1 5379 1 2960 3 6655 1 13614 2 13354 2 14855 4 9021 1 15903 1 1205 1 16008 1 8281 2 14464 1 10840 3 14465 1 1340 1 13752 1 8021 1 9236 1 1208 1 2277 1 1211 1 6149 1 5171 1 10628 1 91 1 6032 1 3333 1 15910 8 14725 2 10559 2 12059 1 13110 1 15485 1 4143 1 4008 1 9518 1 780 1 14864 1 2284 1 14666 1 7837 1 15414 2 12774 1 10043 1 9724 1 10233 1 13443 1 5544 1 12377 1 10968 2 7257 2 15714 1 11646 1 8980 2 2581 1 1893 1 4361 1 539 7 1957 1 9586 1 12067 1 2406 1 10108 2 5035 3 7332 1 1562 1 10182 1 10244 1 5733 1 12325 2 7658 1 5124 1 4261 1 16114 1 11325 1 859 2 11461 1 7157 3 3874 1 14679 2 2662 1 13255 1 7665 1 2467 3 11462 1 4680 1 3138 1 5493 1 5620 1 16355 2 12995 5 9400 1 7608 1 13648 1 10804 1 14892 1 8666 1 12020 2 8800 2 7799 1 7800 1 10260 3 2417 1 15875 1 13002 1 14956 2 1369 3 4933 1 1246 5 619 1 12866 2 4761 2 7990 2 10818 1 16195 1 1641 1 623 1 4762 5 563 1 8322 1 3527 1 8873 1 2684 2 14910 1 12537 1 15940 1 13218 2 3156 2 16491 1 6812 3 12540 1 12349 1 6697 1 4939 46 13736 1 4172 1 5064 1 4279 1 8503 1 11280 1 7120 3 14969 2 11282 1 7565 2 68 1 10273 1 9421 1 14047 1 12620 2 12228 1 3832 1 alcaeus 9609 1 11796 1 655 1 12480 1 4359 1 938 1 2950 1 7210 1 richards 2639 1 9242 1 3242 1 14173 1 5147 1 4761 1 12371 1 2312 1 1078 1 778 1 11618 1 4110 1 14419 1 4907 1 3423 1 611 1 12410 1 3483 2 14352 1 6914 1 8718 1 12792 1 10063 1 4974 1 13648 1 12996 1 281 1 6749 1 11665 1 11442 1 1779 1 11182 1 2488 1 11771 1 ratification 12757 1 523 1 2255 1 13166 1 1862 1 9439 1 intentness 14950 1 4867 1 2833 1 1595 1 indoor 713 1 2384 1 5586 1 7752 1 4998 1 10483 2 1014 2 16182 2 15335 1 11859 1 6871 2 6294 1 6287 1 10541 1 14584 2 1709 1 1560 1 1200 1 9116 1 5861 1 2486 1 7703 2 9169 1 4940 10 9467 1 9428 1 4129 1 14525 3 2277 1 15028 1 14506 1 gigahertz westminster_abbey 905 1 4728 2 14136 1 4179 2 12181 2 13552 1 10350 1 4180 2 9221 1 5598 2 7374 2 13322 1 12302 3 14855 1 2332 2 7178 2 3483 5 7512 1 13079 1 14353 1 7132 1 6772 1 9710 2 16495 2 1537 1 4941 8 679 1 9664 2 4002 1 2203 2 16445 2 andrew_huxley 7715 1 14449 1 10118 1 5131 1 7729 1 13089 1 3483 1 10816 1 1459 1 16336 1 semiotic 2627 1 expropriated 13434 1 9203 1 16355 1 8156 1 11819 2 12003 1 780 1 14329 1 10825 1 1907 1 15317 1 wear_and_tear 5057 1 4040 1 12410 1 323 1 2303 1 5071 1 4012 1 15248 1 2842 1 15305 1 4732 1 9400 1 5926 1 11406 1 8305 1 7045 1 7239 2 1646 1 4534 1 14361 1 14956 1 pivot 4943 27 4944 1 12718 2 5770 1 8592 2 13314 1 12658 1 12231 1 16377 1 4946 1 14193 1 13129 1 4335 1 13552 1 7912 1 10541 1 13422 1 12145 1 15897 1 13059 1 10381 1 2045 1 3320 1 12267 1 9018 1 6171 2 3611 1 4129 1 6398 1 4646 1 5704 3 14855 1 2303 1 4026 1 3074 1 1809 1 16047 1 9705 2 14718 3 7344 2 6499 1 12994 1 14163 1 5271 1 3788 1 6346 1 5500 1 13328 1 5745 1 10392 1 3141 1 2394 1 14360 1 2815 1 12675 1 9094 1 13002 1 4659 1 941 2 12027 1 5331 2 57 1 10812 1 15591 1 10556 2 14173 1 8066 1 4537 2 7318 2 12439 3 4142 1 15676 1 4143 1 12604 2 7492 2 16096 1 6064 1 13008 1 9723 3 14005 1 15342 1 15966 1 14696 1 16286 1 16399 1 8292 1 11063 1 2945 1 4040 1 10641 1 16101 1 2857 2 1134 1 9837 1 13277 1 11110 1 1323 1 14014 1 13578 1 15281 1 7010 1 3971 1 5608 1 10690 1 15968 2 215 1 16494 1 14106 1 7260 1 14410 1 offended 4942 10 14675 1 14415 1 14260 1 11796 1 13904 1 10252 1 13461 2 7340 1 6655 1 2873 1 2490 1 9021 1 1766 1 5419 2 9532 1 16482 1 10875 1 11090 1 6219 1 15933 1 10674 1 7884 1 14687 1 10492 1 16277 1 4692 1 10333 1 13595 1 6062 1 6568 1 1784 1 5506 1 5221 1 7836 1 3083 1 14626 1 9270 1 15916 1 8769 1 11511 1 15118 1 14010 1 2546 1 2511 1 4228 1 8942 1 8810 1 2794 1 1516 1 9525 1 10372 1 11749 1 7012 1 4707 1 8946 1 6894 1 irresistible_impulse 892 1 459 1 12077 1 2598 1 6318 1 1669 1 pickings 2918 1 7068 1 14555 1 16182 1 3375 1 actons 4529 1 11108 1 2212 1 10333 1 6161 1 ninety 15940 1 5705 1 16405 2 15193 2 8663 1 2535 1 4945 12 8529 2 10330 2 12841 1 2983 1 12578 1 jitters 5183 1 673 1 1224 1 1004 1 14612 1 7413 1 9342 1 8314 1 4294 1 7141 1 12011 2 4808 1 1037 1 11749 1 1466 1 2846 1 10428 1 10880 1 1540 1 15517 1 10832 1 asymptotically 2699 1 14243 1 16399 1 12718 1 4933 1 5553 1 13900 1 9526 1 13064 1 9705 2 11105 1 straighten 4943 1 4944 1 5331 2 16360 1 11587 1 4946 14 1961 1 12439 1 1616 1 2338 1 2282 1 5654 1 1708 3 7549 1 3825 1 8322 1 14121 1 6230 1 2857 1 13277 1 5858 1 8875 4 7344 2 6241 1 10873 1 1534 1 227 4 2444 1 13263 1 2335 1 12727 1 1209 1 598 1 9863 1 petulant 5528 1 11749 1 6325 1 11435 1 yodel 10265 1 6630 1 13792 3 13519 1 6043 1 3248 1 magnesium 7015 2 14226 1 14449 4 9922 1 732 1 9650 1 10310 7 12694 1 11349 1 4261 2 12326 2 4947 41 8157 1 389 1 5868 1 1395 1 3676 5 12396 1 7473 1 15975 1 13583 5 13063 1 2873 1 2467 2 10253 12 4096 1 16155 1 3992 1 1088 26 10387 1 3483 1 5742 1 6498 3 11327 4 12083 1 11628 3 6442 1 6800 1 11259 1 14389 3 8021 1 8666 1 13533 3 525 2 15572 1 6535 1 6444 1 6563 1 4464 2 8438 1 9608 1 9097 1 10965 1 910 1 9744 1 7218 1 5718 1 6598 1 13401 1 7585 1 5505 2 10559 10 16336 9 7992 1 10906 2 6569 1 12510 4 7427 10 3764 1 4274 1 15916 1 5913 1 9206 3 6419 2 1929 2 11842 2 2945 1 1827 1 2098 13 918 1 6422 1 15595 2 12192 1 15714 1 8939 1 6788 1 13150 1 13733 2 4417 9 186 1 7360 2 5061 1 12613 1 11811 1 10442 1 1136 1 15578 2 11176 6 8590 1 13951 1 12353 1 2043 2 8723 4 6792 1 7436 1 moron 10195 2 9112 1 fall_over 9672 1 779 1 15254 1 2306 1 8579 1 972 2 12011 1 8452 1 3941 1 scrub_brush 7421 1 3510 1 13273 1 4338 1 12229 1 9413 1 wired 11504 1 3715 1 2499 1 8477 1 8199 1 10828 1 7768 1 5173 1 8930 1 4948 7 6228 1 7537 1 2490 1 7256 1 5158 1 4040 1 12410 1 16065 1 8040 1 13909 1 14233 1 1426 1 1626 1 13736 1 14464 1 8147 1 11406 1 11895 1 7260 2 7565 1 6244 4 13263 1 937 1 2797 1 14020 1 6375 1 6876 1 6643 1 7596 1 tablet 71 1 10660 1 13418 1 8156 1 546 1 9885 2 14025 1 4949 11 1300 1 7158 1 10915 1 14120 1 13461 1 14458 1 16082 1 5878 3 6831 1 11152 1 2269 2 262 1 1205 2 11403 1 4957 1 468 1 9061 1 5528 1 6467 1 15700 2 8408 1 9664 1 6178 1 10491 1 7421 1 869 1 2936 1 400 1 7803 1 8066 1 5173 1 531 2 8930 1 719 1 7740 1 12374 1 688 1 16026 1 1824 1 9074 1 15383 1 10337 1 9273 2 15918 1 11576 1 14009 1 5311 2 8111 1 15854 1 4360 1 11647 1 6157 1 14251 1 7012 1 15251 1 3386 1 5694 1 15688 1 1296 1 phrasing 5689 1 918 1 8108 1 6950 1 12683 1 12391 1 2448 1 15056 1 945 1 1784 1 6167 1 6261 1 11383 1 14409 1 2589 3 13084 1 7956 2 8946 1 11963 1 6070 1 504 1 petersburg 11517 2 211 1 10305 1 9600 1 9186 1 884 1 13079 1 13551 1 11540 1 14056 1 9203 1 881 1 13342 1 16215 2 11123 2 2921 2 5888 1 7304 1 13312 1 sinfulness 12034 1 7774 1 2266 1 3735 1 9601 1 13826 1 8654 1 897 1 8892 1 10950 1 3906 1 5871 1 8100 1 10147 1 6937 1 14540 1 811 1 chbr3 9219 1 13859 1 scrape 9521 1 8987 1 15541 1 4766 2 8852 1 12264 1 13968 1 4962 2 1616 1 2581 1 7744 1 561 1 12617 1 7260 1 9654 1 8883 1 4952 7 10199 1 14101 1 4129 1 5965 1 10300 1 2704 1 11287 1 mallee 12811 1 15254 1 3524 1 14847 1 3258 1 9206 1 6419 1 12847 1 unornamented 8803 1 14980 1 12643 1 13785 1 7729 1 15692 1 11709 1 2931 1 12540 1 2649 1 11868 1 10325 1 13801 1 14224 1 125 1 12126 1 10119 1 16269 1 13583 1 6367 2 4765 1 15277 1 5169 1 2195 1 7667 1 13002 1 hypocritically 3367 1 14054 1 11753 1 911 1 16378 1 13676 1 1300 1 779 1 13270 1 14881 1 8192 1 9270 1 3626 1 136 1 12410 1 7475 1 4131 1 9400 1 1595 1 10374 1 12639 1 10080 1 8249 1 consultation 483 1 1969 2 13842 1 4887 1 6794 2 12735 2 1915 1 14793 1 8942 1 7488 2 11036 1 7076 3 10860 1 3568 1 6685 1 15570 2 14969 1 9285 1 8946 2 4953 7 4250 1 1588 2 maroon 421 2 11431 1 12783 1 15994 1 13708 1 9922 1 10310 1 12075 1 15693 1 4335 1 5556 1 12326 1 12847 1 4263 1 13086 1 15973 1 1200 1 7471 2 6080 1 2436 3 14712 1 4678 1 13985 1 16411 1 14196 1 9986 1 2531 1 6965 1 13583 1 4954 11 4529 1 12822 2 11527 1 8458 1 15404 1 5455 8 13462 1 9291 1 15267 1 9601 1 3760 1 1237 1 10286 1 15299 1 14297 1 447 2 1818 1 16453 2 10094 2 1403 1 10701 1 176 1 8581 2 2036 1 11474 3 7350 2 9540 1 4933 1 12283 1 2718 1 5304 1 10742 2 5173 1 3258 1 13970 1 1709 1 6449 1 14138 1 3421 2 15026 1 8259 1 2134 1 1932 2 11956 1 9005 1 9681 1 11577 1 1560 1 9587 1 10079 1 7904 2 12260 1 9841 1 1685 1 2921 1 9047 2 3348 1 ps 8946 1 2049 1 4540 1 4398 1 4165 1 tackling 9429 1 6011 1 7649 2 12286 1 11783 1 4143 1 12636 1 12260 1 373 1 13490 1 4001 1 7507 1 10952 1 10068 1 gabby 14810 1 8410 1 15087 1 9044 1 7587 1 reaching 10274 1 5516 1 11008 1 6200 1 5518 1 15318 4 14918 1 15128 1 8153 1 9359 1 14192 1 3164 1 13551 1 703 1 4335 1 8156 1 10351 1 12556 2 15470 2 13676 1 3171 1 14979 1 6527 1 6081 1 8571 1 9228 1 5078 1 2838 1 5586 1 4955 30 5778 1 2263 1 1592 1 5648 1 3992 1 4898 1 4957 1 16008 1 5214 1 15766 1 14199 1 13169 1 1089 1 1812 1 8892 1 13749 1 2902 1 2444 4 8211 1 14658 1 13881 2 1209 1 9767 1 10492 1 906 2 4466 1 11773 1 529 1 5094 1 4537 1 6226 1 7769 1 10042 1 4603 1 20 1 779 1 10097 1 5029 1 7587 1 11309 1 12970 1 16398 1 16227 1 8292 2 11108 1 3741 2 15852 1 14010 1 1680 1 4358 1 11110 1 5228 1 7649 1 3125 1 9583 1 10106 1 14279 1 2214 1 4361 1 6980 2 8912 1 1561 1 1055 1 14018 1 7966 1 13897 1 540 1 9920 1 5233 1 13454 1 9527 1 3350 1 3750 1 4426 2 12523 1 16471 1 10453 1 7270 1 606 1 7210 1 9259 1 12145 4 5413 3 13526 2 5929 2 13323 1 9457 1 13461 1 13583 1 8381 2 10253 1 7541 1 5128 2 8382 1 6620 1 14819 1 14160 1 10511 1 6929 1 11327 1 15511 1 10062 1 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1099 4 15847 1 15964 1 99 2 6356 1 5103 1 6230 1 14009 1 211 1 13512 1 8223 1 15077 1 5729 1 9250 1 12067 1 2407 1 10978 1 1611 1 2016 3 9920 1 15228 3 8726 1 5736 1 7851 1 14152 1 4019 1 3352 1 15089 2 12848 2 6107 1 2301 1 5346 1 12721 2 3065 2 6367 1 15861 1 5240 1 611 1 10867 1 11400 1 866 2 9532 1 11403 1 13260 1 5244 1 8246 1 9805 1 10325 1 3214 1 13648 4 4990 47 5131 1 7483 1 16484 1 3883 1 6374 1 7486 2 12600 1 9665 1 14956 1 1920 1 4164 1 15987 2 9667 1 3080 1 10333 1 15744 1 8137 1 1926 1 14693 1 9939 1 14558 2 15880 2 3223 2 7114 1 11003 1 383 1 15249 1 7009 1 1260 1 1136 1 5366 1 14969 1 11419 1 629 1 12620 1 14047 1 9421 1 12228 2 13814 1 4891 1 esophagogastric_junction 8220 1 10579 1 2012 1 4142 1 adolescence 4944 1 11367 1 299 1 4583 2 15629 1 4091 1 3367 1 13627 1 776 1 6247 1 7210 1 717 1 2658 1 3753 1 2190 1 13490 1 4110 1 15637 1 12234 1 7646 1 15115 1 5776 1 15472 1 13576 1 1011 1 1459 1 12270 2 10523 1 13809 1 6230 1 5604 1 8108 1 15120 1 1363 2 8325 2 12669 1 3830 1 1593 1 7189 1 2247 2 9368 1 11327 1 2691 1 12857 1 2901 1 7718 1 8180 1 1291 2 4989 9 12795 2 4707 1 10018 1 2635 1 8563 1 15521 1 scratchy 15910 1 4950 3 scorbutic 5766 1 camera 4944 1 4284 1 573 1 9882 1 7368 1 15630 2 12299 1 252 1 1391 1 9359 1 704 2 15470 1 2325 1 7302 1 15759 1 3676 3 7239 2 3320 1 10418 1 10951 1 2260 1 15762 1 4338 1 16325 2 2705 1 4646 1 13354 1 5586 1 9021 1 6713 2 960 1 5648 1 4530 1 15958 3 6217 2 4243 1 14199 1 1089 1 16158 1 5969 2 1812 2 3615 1 4790 1 6219 1 12638 1 3036 2 6281 1 4189 4 14132 2 8523 2 526 1 11917 1 1539 1 12122 1 4250 2 10096 1 4466 1 10429 1 836 1 14207 1 9510 1 15591 1 8026 1 14210 1 8529 1 8531 1 2132 1 2912 1 3192 1 8468 2 14941 1 8533 1 11572 2 10099 1 7256 2 5544 2 7774 1 8292 2 13243 1 4672 1 11999 1 11034 1 11110 1 3194 1 13893 1 7899 1 2214 1 5547 1 539 2 1169 3 101 2 1290 1 12067 3 9587 3 11849 1 2920 1 5180 1 7464 1 11939 3 5181 1 4154 2 284 1 12137 1 2922 1 10182 1 12718 2 351 1 16112 1 9049 1 663 1 13521 1 9849 2 12075 1 4982 1 7912 1 2863 1 6863 1 9116 2 11863 2 11728 1 5872 1 8545 1 1907 1 4813 1 120 1 6367 1 7856 1 9926 1 5878 1 12466 1 7669 1 16417 1 14613 1 12669 1 14352 2 6498 1 12995 2 6499 1 12083 1 8995 1 11130 2 11408 1 5500 1 5941 1 15933 1 8435 2 14892 2 1242 1 8609 1 4991 48 13263 1 13467 1 1574 1 9861 1 5245 3 2815 1 556 1 11474 1 14956 1 7421 1 4933 1 2164 1 11602 1 11538 1 4499 1 14555 1 62 1 16195 3 9478 1 5200 1 4109 1 12607 1 15747 1 13273 1 2038 1 11955 1 2881 1 6321 2 13338 2 5689 1 2760 1 15120 2 3601 4 16491 2 2546 1 16312 1 8559 1 10211 1 4277 1 6069 2 7812 1 10340 1 1016 1 11811 1 2691 1 3971 1 4172 3 4889 1 11419 2 16495 3 3386 1 2614 1 10344 1 5694 1 8005 1 13016 1 1140 1 16318 1 anthracite_coal check_in 14823 1 2246 1 4976 1 11400 1 2598 1 1014 1 6596 1 14155 1 10206 1 8942 1 14270 1 8787 1 5926 1 7002 1 illuminate 4933 1 5770 1 5716 1 9735 2 5718 1 9087 1 8556 1 2598 1 3676 3 7006 1 11188 1 8962 1 14408 2 8292 1 12270 2 3136 1 893 1 2263 1 4041 1 483 1 10936 1 15120 1 7559 1 7978 1 8246 1 3617 1 12020 1 4001 1 1735 1 4992 8 6876 1 349 1 consonant 16261 2 6011 2 9494 1 11491 1 2385 1 4714 1 8695 2 9282 1 12041 1 4950 9 13422 1 11351 2 8749 1 13491 1 11081 2 3322 1 4529 1 3404 1 10836 2 10485 1 9291 2 10767 3 15834 1 11558 1 3495 1 3918 1 8022 1 6596 2 11917 1 9575 1 7706 1 9830 1 4466 1 529 2 1039 1 16390 5 1738 4 5093 1 12905 1 8834 1 2450 1 5173 1 6287 2 1602 1 2504 1 4907 1 1099 3 13576 1 2644 1 6356 1 6230 1 13042 1 14010 5 14009 1 11110 1 11646 1 4359 1 2581 1 657 1 14738 2 8180 3 1291 2 10650 2 9728 4 1055 1 1054 3 2918 1 16178 2 10854 1 13768 2 3348 2 7598 2 10113 5 15665 1 11586 1 4368 1 16471 1 12981 1 6045 1 8596 2 15286 1 5925 1 14287 2 1115 2 4019 2 12145 2 11728 1 2741 1 9797 1 15506 2 4307 1 2997 2 5240 1 10865 1 12726 1 6558 1 5493 2 5494 1 5244 1 11327 2 13718 1 9662 1 13648 4 12399 9 4493 3 12674 13 5500 1 12020 2 8489 3 4993 53 13863 1 3711 2 10070 2 9666 3 8438 1 10517 1 3885 4 4933 1 3145 1 10265 1 7988 1 12342 2 5505 1 4761 3 5631 1 14363 2 8556 1 9940 3 6186 2 7805 1 8323 1 11606 3 12939 1 9074 1 13598 1 2882 1 6189 2 3661 1 382 1 16437 1 15459 5 3667 1 312 1 13082 1 9945 2 16494 2 1137 1 10755 3 4705 1 12416 1 10018 2 13286 2 13813 1 9554 2 3238 1 6894 2 romagna 14584 1 3638 1 11619 1 4829 1 15688 1 captivating 5493 1 6779 1 8796 1 8517 1 846 1 8254 1 4761 1 2513 1 13490 1 1656 1 9898 1 3626 1 kainite 10965 1 5742 1 6498 1 15055 1 11327 1 1797 1 11280 1 2306 1 3676 1 641 1 9841 1 1055 1 9467 1 13301 1 15517 1 6894 1 depersonalize 7774 1 906 1 8292 1 5254 1 23 1 12228 1 2960 1 spatial 4944 1 6011 1 2385 1 8747 1 5444 2 16076 1 6958 1 4335 1 705 1 15695 2 16078 11 3398 2 6830 1 5155 1 6962 6 5964 4 3677 2 2490 1 7304 2 5323 1 14126 1 5383 1 9021 3 5328 2 10161 2 2903 1 13432 1 13684 1 5218 1 13434 1 13296 1 2394 1 12569 5 11831 2 2277 2 10361 1 11367 4 10492 1 12643 2 7140 1 12645 1 13035 1 4141 1 11307 2 8361 5 717 1 8363 2 11924 1 4907 1 7958 1 7896 1 14525 1 781 1 16227 1 6230 6 12973 3 10304 1 8980 1 7528 2 6232 3 14281 8 1291 1 1053 1 13380 1 1055 4 31 2 2797 2 16178 1 10655 2 14340 1 12521 1 9527 3 16182 1 15665 1 12586 1 9390 1 12203 1 2802 2 5412 1 4808 1 7210 2 15089 3 15170 1 11728 1 5347 2 4306 1 12527 1 2022 2 5487 1 9055 1 6239 1 5616 1 12268 1 5618 2 12270 1 10867 1 5128 1 7789 1 6620 1 2535 2 1309 2 227 1 11662 1 935 1 10324 1 442 1 297 1 10258 1 10064 1 5500 1 7796 1 373 1 6935 1 5356 1 12675 1 6444 2 1632 1 7486 2 15877 8 233 1 800 4 7421 2 8438 1 3143 1 2162 1 1370 1 2164 1 12733 1 4995 53 1707 1 5815 7 14555 1 1925 1 15744 1 6448 1 2680 2 4272 1 9478 2 3825 1 15455 1 9350 2 13214 1 7291 1 14247 1 11334 1 12609 1 7742 1 7006 2 877 2 11339 1 878 2 5689 4 15940 1 13808 1 2945 1 11002 1 1014 2 3661 2 14563 2 16491 1 15459 2 1134 2 8875 1 11004 1 10340 1 15942 2 14838 3 13736 1 14104 1 2691 2 3970 2 6760 4 1136 2 14634 2 16494 4 9354 1 141 1 10755 2 5828 6 14106 2 15390 1 2548 1 13343 6 12416 4 7749 1 14044 6 15816 1 14047 1 13482 4 12228 3 2826 2 14704 1 14506 1 subtraction 4659 1 13034 1 8975 1 1565 1 13313 1 12905 1 8156 2 4996 7 5926 1 10664 1 13575 1 13781 1 4446 1 12578 2 12579 1 15940 2 4925 2 15864 1 484 1 14009 1 7477 1 15833 1 14181 1 1972 1 12416 1 4136 1 3161 1 3008 1 overwhelmed 4659 1 10483 1 5270 1 13408 1 15121 1 141 1 11749 2 14979 1 6043 1 12787 1 14395 1 6195 1 in_theory 11495 1 4660 1 4502 1 14626 1 6230 1 11121 2 suborbital 16494 2 4591 1 12229 1 12416 1 12733 1 10771 2 9533 1 5385 1 8448 1 12292 1 15514 1 3231 1 examiniation 8946 1 11812 1 4466 1 4623 1 5911 1 3493 1 5475 1 6815 1 envisioned 7960 1 14034 1 4661 1 9717 1 12895 1 16191 1 16495 1 15518 1 5319 1 8192 1 3875 1 11936 1 864 1 morphologically 13228 1 1593 1 6011 1 8812 1 15459 1 7977 1 pepperoni 12246 1 pyongyang 11693 1 11232 1 12720 1 2070 1 1134 1 5228 1 6444 1 11216 1 big_hand 11483 1 9532 1 11755 1 6444 1 5633 1 13470 1 14005 1 14838 1 5075 1 9690 1 8864 1 8576 1 cardiac_cycle 4529 1 14589 1 8076 1 1246 1 7359 1 3454 1 15421 1 14948 1 15935 1 62 1 9587 2 1291 2 6924 1 9728 1 12527 1 13214 1 13503 1 15731 1 13581 1 sea_poacher 884 1 9384 1 cataclysmic 11512 2 4169 1 14820 2 10247 1 4099 1 4155 1 15878 2 9477 1 15470 1 547 1 11654 2 9146 1 16072 1 14643 2 5426 1 1328 1 12396 1 4502 1 14604 1 medicare 3069 1 3646 1 8314 1 8980 1 4840 1 4110 1 12556 1 14643 1 9956 1 hustle 4270 1 12718 1 5493 1 6485 1 2159 1 591 1 tailorbird 1437 1 14533 1 9210 1 14370 1 3636 1 buttonhole_stitch 8796 1 14092 1 5718 1 1076 1 10840 1 4171 1 7010 1 8147 1 2598 1 1573 1 15933 1 16120 1 16178 1 3501 1 8381 1 4622 1 14956 2 15986 1 family_tenebrionidae 12001 1 13609 1 fend 7387 1 13209 1 9021 1 15518 1 11686 1 972 1 15305 1 2613 1 4732 1 481 1 assigning 4466 1 1183 1 7269 1 4616 1 5519 1 14904 1 3245 1 945 1 7302 2 11757 1 4907 1 10356 1 7839 2 5688 1 510 1 4997 7 2688 1 10253 1 5953 1 6343 1 2511 1 2229 1 5166 1 12159 1 2765 1 14892 1 14776 1 16484 1 10216 1 12569 1 12353 1 14047 1 14049 1 5957 1 fractional_distillation 2988 1 2070 1 5648 1 14685 1 2881 1 12236 1 searles 16287 1 5459 1 4033 1 9612 1 16165 1 11287 1 fourth_ventricle 3799 1 10628 1 349 1 7359 1 unaffected 219 1 15430 1 11011 1 10453 2 2114 1 12924 1 12659 1 12815 1 16378 1 13674 1 12145 2 2598 1 13490 1 4745 1 120 1 6367 1 16236 1 4158 2 11400 1 9021 1 10732 1 15833 1 11695 1 9400 1 1595 1 14325 1 9264 1 2335 2 6029 1 7169 1 11160 3 14798 1 869 1 529 1 3145 1 10430 2 9965 1 10741 1 8133 1 13800 1 7835 2 12534 1 10076 1 10681 2 1786 1 9546 2 11335 1 8192 1 5913 1 7742 1 11572 1 13375 1 11679 3 4999 16 11927 1 2546 1 16492 1 5227 1 15279 1 1166 2 2916 2 10600 1 9176 1 1607 1 2214 1 2767 1 16439 1 4705 1 8081 1 3571 2 diphtheria 1635 1 8438 1 673 1 7754 1 5436 1 8685 1 12669 1 12509 1 13463 1 12924 2 7523 1 833 1 6828 1 2598 1 8309 1 3025 1 8275 1 5794 1 10120 1 badly 10469 4 5372 2 13668 1 758 1 4049 1 9423 1 4394 1 252 1 10758 1 1145 1 10223 1 9558 1 12357 1 13422 2 15636 1 7757 1 5153 3 3676 1 7239 3 15762 1 9286 1 3913 1 3404 2 5778 1 9021 4 15136 1 6279 1 12895 1 1534 1 5271 1 13429 1 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9875 1 12066 1 11346 4 9590 2 reagan 602 1 7604 1 3979 1 4898 1 3025 1 12215 1 16117 2 raffle 9164 1 frenchwoman 6134 1 13930 1 taiwan 421 1 15558 1 5671 2 7069 1 8879 1 10825 3 15431 1 1755 1 5556 1 13552 1 6236 1 16117 1 12848 1 12847 1 11460 1 15973 1 13422 1 8881 1 14289 1 4986 1 7697 1 10899 3 2560 1 293 2 11216 1 13292 1 4529 1 763 1 7699 1 10868 1 15473 1 14065 1 7634 1 7103 1 15138 1 14584 1 13648 1 8434 1 3618 1 16484 1 8997 1 799 1 10701 5 904 1 14094 1 5502 1 15574 4 5091 16 505 2 1737 1 11267 1 12866 1 9032 3 2128 1 10333 1 9834 1 1475 1 9203 1 1709 1 11000 1 3048 1 9748 1 3766 1 4829 1 9415 1 15118 1 6479 1 484 1 5228 1 7900 1 13079 1 8502 1 12383 1 13380 1 11785 1 15753 1 1685 2 9420 1 16373 1 14022 1 540 1 13581 1 15688 2 like_the_devil 5883 1 6530 1 5184 1 9359 1 11646 1 6279 1 14838 1 2214 1 6019 1 15933 2 126 1 9767 1 molecular_formula 10816 1 4323 1 9400 1 9153 1 611 1 leon 6836 1 3145 1 7850 1 11754 1 6351 1 428 2 9937 1 2023 1 13089 1 5687 1 6655 1 1982 1 11188 1 11925 1 7230 1 8942 3 1169 1 3815 1 1291 1 13381 1 7485 1 2034 1 7234 1 8819 2 mobile_river 2052 1 3980 2 3164 1 5017 2 5880 1 16321 1 1773 1 4540 1 modifier 3944 1 7367 1 5118 1 219 1 1391 1 12041 1 1836 2 12424 1 7660 1 13780 1 12887 1 7972 1 5041 2 13491 1 258 1 10899 1 11081 1 10867 2 14126 1 10189 1 7413 1 866 2 8314 1 2229 3 7189 1 5457 1 12995 1 2083 1 12563 1 9465 1 4001 1 15301 1 6444 1 3792 3 12639 1 15836 1 6801 1 12934 1 9575 2 5883 1 7199 1 2205 1 10703 4 9609 2 11920 1 5092 17 10265 1 4661 1 2337 1 5173 1 13574 2 945 6 15306 1 16338 3 9071 1 8739 1 1547 1 2854 1 1099 4 510 1 15965 1 1350 1 9679 2 9305 2 7428 1 1014 7 6230 2 2857 2 2042 1 13408 1 6294 1 12003 1 566 3 2582 4 13843 1 12255 1 4578 1 11893 1 489 1 11205 6 3534 1 107 1 1656 1 incidental_to 7802 2 14192 1 5093 7 5483 2 7438 2 8069 2 12145 2 1642 2 14979 1 6186 1 13190 2 16026 1 13228 1 4748 1 1787 1 4529 2 15186 2 3022 3 14062 2 1013 1 4530 2 7649 3 5104 1 10340 2 11327 2 8503 2 14016 2 4451 1 1466 2 1055 2 126 2 4086 2 6071 2 4993 1 6162 4 2826 2 2349 2 16372 1 ballet 15818 1 1023 2 9819 1 5518 1 9359 1 5834 1 9149 1 4335 2 8954 1 12815 1 3393 1 9221 1 12041 1 13021 1 3171 5 13490 2 7239 1 10418 1 9018 3 12959 3 1459 1 763 1 14197 3 5841 1 3546 1 4955 1 9955 1 7376 1 7240 1 4530 1 16008 1 4243 1 2444 1 15701 1 13881 1 2445 3 9899 1 4659 1 11706 1 15407 1 11307 1 11709 1 11223 1 5094 35 4537 1 15912 1 6226 1 6035 5 779 1 6292 1 11572 1 14007 1 8716 1 9441 1 6230 1 211 2 4202 4 14220 1 15077 1 1749 1 595 1 4975 1 14147 1 9588 1 1561 1 15784 1 14018 1 5181 1 2986 1 8914 1 2349 1 13706 1 8373 1 16182 1 14675 3 2300 1 2802 1 12848 1 9116 6 13253 1 13526 1 13323 1 1762 1 14348 1 7541 2 10867 1 12929 1 11189 1 11730 1 7729 1 15097 2 14160 1 14159 1 10513 1 9400 1 6932 1 6442 1 5500 2 3816 2 6686 2 12795 1 15445 1 679 1 7799 1 6177 1 10131 1 6375 1 6801 1 1504 2 16303 1 4933 1 6059 1 301 1 12219 1 4499 1 4761 3 8869 1 6448 1 14243 1 8138 1 16195 1 4272 1 12740 1 14905 1 13214 1 4317 1 12609 1 4882 1 1250 1 6321 1 9677 1 3829 1 5760 1 16492 1 15459 1 13079 2 14838 1 12159 1 4511 1 11282 5 5957 1 first_step 11119 1 5095 5 2419 1 12011 1 13674 1 945 1 7374 1 2940 1 9377 1 2627 1 7697 1 3858 2 11188 1 16300 1 2490 1 811 1 1931 1 14197 1 2759 1 16239 1 12048 1 2229 1 8473 1 6890 1 12003 1 2214 1 2691 3 9212 1 11192 1 10444 1 16494 1 4451 1 11419 2 13981 1 8128 1 12620 1 10903 1 4404 1 typical 4638 1 15757 1 13552 1 1796 1 5519 6 7369 1 6526 1 13821 1 13822 1 13674 1 11693 1 1395 1 7238 3 14853 3 2190 3 15637 1 3676 1 10763 1 7239 2 4896 1 8512 1 5776 2 6398 1 3403 1 9822 1 5381 1 3404 2 3914 1 10485 2 1148 7 14065 1 9706 1 1947 6 152 1 12764 1 768 1 12120 1 2053 1 12899 1 267 2 8523 1 5390 2 1818 1 10771 1 6535 1 8408 2 9714 1 9830 1 13034 1 13686 2 15407 1 12643 1 5532 1 12123 1 15774 1 11709 1 7767 1 12905 1 1408 1 4537 2 3258 1 11838 1 13835 1 16281 1 6035 1 779 1 13575 1 2063 2 13040 1 12773 1 5399 1 11312 4 16398 2 7774 1 3427 1 11999 1 12249 1 12380 1 6788 1 14997 1 13843 1 6426 3 7528 6 4082 1 5918 1 8180 1 1413 6 16177 2 10374 1 9316 1 9728 1 7260 6 1292 1 6159 1 7393 1 16178 1 5180 2 11182 1 13706 1 9590 1 10245 1 7532 1 5185 1 11457 1 2989 1 5555 1 10503 1 10661 2 5556 2 12786 1 7660 1 2865 2 3135 3 7661 1 2353 1 7536 1 14481 6 3806 1 14232 7 293 1 1176 1 13712 3 10253 1 4308 1 7413 7 12792 6 16191 1 1309 2 10513 1 11327 1 5678 1 1065 1 14616 1 1310 1 12674 1 3816 2 12795 1 6934 1 2089 1 9860 1 15572 1 1432 1 799 1 6801 9 300 1 9996 1 6317 1 6180 1 9609 1 11330 1 5815 1 9477 1 2373 1 62 1 11739 4 9350 1 563 1 10521 1 12939 1 3596 2 16435 1 1982 1 4698 2 308 1 15940 1 9208 1 9079 1 8325 2 13076 1 14374 1 2247 2 11485 2 312 8 15312 1 10407 1 9874 1 14251 1 13342 1 4706 1 14776 1 68 1 4707 2 8946 1 2617 1 5317 1 4234 1 6194 1 13482 1 5443 3 1331 1 14111 1 15192 1 6458 1 3310 1 13609 1 1457 15 15318 1 15467 1 10539 1 4950 1 15695 1 11618 1 5074 2 6080 1 14782 2 1870 1 13490 1 13745 1 1997 1 7824 1 5964 1 7941 1 7697 1 575 1 5584 1 2262 1 7304 1 2120 1 577 2 5214 1 5215 1 4343 1 10032 1 1595 1 9506 1 10161 1 13878 2 1596 3 1093 1 14270 1 14797 1 8829 1 9899 1 9372 1 13505 1 4726 1 8093 1 13755 1 9635 1 11223 1 9245 1 204 5 5096 133 10560 1 13369 1 3189 1 6473 1 6351 1 11102 1 11227 1 15710 1 4009 1 14941 1 1099 1 2402 1 8838 1 589 1 13116 1 11925 1 12322 1 9640 1 4971 1 7590 1 13512 2 14944 1 4359 2 3862 6 1893 1 9644 2 101 6 12837 1 2513 1 13379 1 10978 2 14018 2 15225 1 7092 2 9383 1 12070 1 5982 1 12454 1 2921 1 2016 2 12324 1 3200 2 8235 2 14414 1 357 1 12848 14 2301 1 9795 1 15729 1 5347 1 987 7 6238 1 11796 1 1760 1 4986 1 9531 1 13134 1 361 1 6869 1 6494 1 611 1 864 1 9926 1 10867 1 13646 1 4871 3 2303 1 9532 1 6498 6 13260 1 10871 1 6241 1 1770 1 7102 1 16356 6 9400 1 10325 2 5993 1 16483 2 14428 1 126 1 5628 3 10067 1 8997 1 7485 1 11668 1 2036 1 9665 6 1920 1 4164 3 8998 1 12341 6 4878 6 14690 1 15110 1 10333 1 6628 2 11413 1 1006 1 7224 1 2316 1 1510 2 9270 2 14833 1 5888 2 877 1 510 1 2175 1 9415 1 136 1 2689 1 13150 1 5362 6 13408 1 7009 1 3229 1 1260 1 5365 1 4172 2 8618 1 10342 1 13283 10 14969 1 4511 2 6262 1 5513 1 6520 1 4634 1 1656 1 13549 2 4891 1 6264 1 abhorrent 12534 1 15073 1 528 1 14715 1 4707 1 9563 1 12796 1 10932 2 8851 1 8134 1 8769 1 4912 1 12752 1 9259 1 exponential_function 4529 1 7995 1 1788 1 slapped 9509 1 6896 1 5959 2 10363 1 927 1 8255 1 5654 2 5097 7 4143 1 13021 1 11738 1 3320 4 14941 1 6253 2 5888 1 15588 1 13116 1 13712 1 15028 2 5876 1 12973 2 14913 1 8827 1 2511 1 9532 1 6241 2 12951 1 8984 1 13767 1 12795 1 14325 1 4546 1 14490 2 16318 4 2036 1 web_site 5336 1 9823 1 300 1 8998 1 10363 1 15403 1 12669 1 8243 1 8418 1 2583 1 2973 1 11895 1 16010 1 11789 1 3268 1 12798 1 2960 1 family_lycosidae 1457 1 accountable 10415 1 8093 1 11900 1 12643 1 3061 1 14052 1 6838 2 13552 1 9984 1 13821 1 1963 1 9203 3 4539 2 5346 1 7374 1 5098 9 5347 1 6830 1 6652 1 7441 1 5176 2 5586 2 4340 1 5899 1 2759 1 2049 1 6230 1 10140 1 6068 1 11485 1 11749 1 5904 4 16177 1 13765 2 1466 2 12795 2 12488 1 2815 2 7776 2 12727 1 268 1 12620 4 momentum 12585 1 9527 1 8592 1 3242 1 12393 1 9623 1 8628 1 12848 1 12145 1 6080 1 8659 1 5929 1 6135 1 6830 1 15952 1 7473 1 6170 1 12206 2 5873 1 260 1 6400 1 16327 1 9153 1 5240 1 9429 1 8204 1 10421 1 3404 2 9021 1 7976 1 14819 3 12331 1 4531 1 8483 1 9705 1 6497 1 6499 1 11327 1 14163 2 13028 1 4789 1 13395 1 6747 1 15301 1 4250 1 9996 1 473 1 13796 1 14469 1 12439 1 9477 2 11675 1 3082 2 5100 13 13575 1 3152 1 3223 1 4502 1 14005 2 15517 1 16436 1 8292 1 11002 1 6230 1 4169 1 9971 1 5033 1 14181 1 15423 1 7260 1 8449 1 15191 1 11686 5 9552 1 1389 1 6792 2 2826 1 frenzy 3509 1 12585 1 8301 1 15500 1 1486 1 14507 1 3145 1 3113 1 6525 1 5505 1 13538 1 3396 1 15436 1 5099 6 7490 1 4743 1 15601 1 14231 3 4338 1 11231 1 7391 1 11998 1 16436 1 12529 1 9823 1 9056 1 4040 1 11400 1 6843 1 5778 1 7230 1 1148 1 15867 1 2843 1 593 1 595 1 4401 1 11068 1 8435 1 15933 1 15687 1 14070 1 5669 1 extinguisher 9830 1 15491 1 1242 1 3684 1 8637 1 15263 1 971 1 213 1 10816 2 3837 2 11714 1 fibrillose 2426 1 13967 1 16329 1 10900 1 12326 1 12849 3 8394 1 2292 1 3495 1 7922 1 1818 1 4986 1 2881 1 3699 1 14866 1 johns 3061 1 13035 1 3104 1 9186 1 253 1 8314 1 7634 1 13280 1 13261 1 6287 1 4263 1 4770 1 13283 2 1053 1 2436 1 12102 2 16215 2 13214 1 106 2 1099 2 3792 2 10147 1 5101 7 9549 1 4698 1 11943 1 harshly 573 1 1660 1 15397 1 6984 1 6765 1 16117 1 13674 1 6988 2 3755 1 8824 1 13490 1 7375 1 3779 2 2873 1 4752 1 12171 1 9430 1 9956 4 10485 1 14682 1 745 2 8125 1 3003 1 14199 1 15443 2 637 1 2054 1 7245 1 13171 1 6564 1 16015 4 3145 1 15541 1 10265 2 3820 2 10433 1 13797 1 10559 1 15913 1 1784 3 15776 1 622 1 10846 1 13301 1 5102 16 8105 1 11483 1 15918 2 7178 1 880 1 6788 1 16492 1 4701 1 8328 1 1607 1 7361 1 9873 1 2378 1 12617 1 11282 1 954 1 4580 1 6793 1 tudor_arch 8180 1 3816 1 8040 1 7789 1 6382 1 9723 1 2203 1 15538 1 7773 1 12578 1 7210 1 pollock 2522 1 5264 1 4923 1 12920 1 7230 2 5179 1 unassuming 16250 1 11762 2 3491 1 585 1 1148 1 15933 2 4546 1 azimuth 9527 1 14077 1 959 1 702 1 12082 1 13081 2 12035 1 3158 2 14892 1 373 1 2220 1 508 1 9723 1 1389 1 16198 1 watergate_scandal 8251 1 7604 1 12215 1 observed 10757 1 11551 1 14844 1 253 1 11816 2 8628 1 15896 1 9017 2 2703 1 8512 1 5644 1 14855 1 2840 1 5778 1 9021 4 12898 1 152 2 4790 1 14201 1 3917 1 11017 3 10771 1 6283 1 11703 1 8527 1 529 4 3113 1 2448 1 4537 1 14469 1 12124 1 14211 1 14212 1 7387 1 6035 1 9168 1 6910 1 10097 7 15414 1 10637 1 13576 1 536 1 11172 1 16399 1 8039 1 5279 1 8178 1 7899 1 2214 1 5664 1 4082 1 16036 1 9728 1 7904 1 7393 2 6792 1 542 1 16182 1 2989 3 2221 1 14343 1 9451 1 12011 3 15791 1 12145 1 1964 1 13460 1 10664 2 8309 1 1965 1 7275 1 2992 1 14883 1 1968 1 6925 1 9598 1 10253 1 12270 2 4925 2 15797 1 12669 2 5676 1 8314 1 11327 1 3812 1 14616 1 14236 1 3288 1 442 1 16052 1 10128 1 11596 2 12795 1 14358 1 15445 4 15805 1 15572 1 7800 2 15301 1 5427 1 11472 2 4933 1 12283 1 56 1 3145 2 10264 1 12936 1 5950 1 8190 1 11739 1 8556 1 9478 1 13214 1 8443 1 8677 1 2881 1 2377 2 5573 1 6321 1 15940 1 4448 2 5311 1 13076 1 16205 1 9212 1 4451 1 9083 1 4707 2 11487 1 12416 1 5694 1 10018 1 8563 1 5578 1 5441 1 11347 2 5442 1 573 1 13486 1 15692 1 5072 1 11349 1 12555 3 15695 1 11907 1 6080 1 6828 1 4951 1 6962 1 13089 4 9227 1 13491 1 7697 1 4338 1 2262 4 10698 5 7946 1 959 1 12561 1 85 1 7191 1 14651 1 15330 1 12305 1 1466 1 15058 1 10424 1 835 1 2496 2 5088 1 15702 1 4723 1 7314 1 13106 1 8462 1 10965 1 13626 1 14935 1 5717 1 14000 1 10559 2 3189 1 4143 1 718 1 7712 1 585 4 2399 1 14002 1 2504 1 2134 1 11641 1 5336 1 9640 2 5602 1 7715 1 3342 1 5103 57 6230 2 8108 1 11644 2 13512 1 13893 1 2647 1 213 1 13762 1 1104 1 14016 2 14147 1 15721 1 1611 1 4014 1 9919 1 2920 3 8851 1 11517 2 1111 1 13900 1 12203 1 4156 1 4263 1 6864 2 12721 1 8120 1 15978 1 14540 1 6738 1 12465 1 611 1 15731 1 223 1 10867 2 6739 1 16354 1 3359 1 11403 1 7102 1 9400 1 15104 1 10195 1 4990 1 996 1 5500 1 14428 1 6244 1 126 1 11410 1 3077 1 9405 1 1921 1 15987 1 1782 1 14173 2 14555 1 13654 1 15744 1 2312 1 13800 1 9672 1 2422 1 131 1 12478 1 3223 1 3527 3 10462 1 1014 1 11003 1 16492 1 9274 1 4169 2 139 1 13661 1 7010 2 16494 3 11280 1 7749 1 14186 2 5766 1 14188 1 11427 1 13814 2 genus_tarsius 1261 1 13693 1 immanuel_kant 16455 1 windpipe 12529 1 4944 1 5883 1 7946 1 6495 1 1688 1 6502 1 252 2 16008 1 12439 2 5925 1 6980 1 4609 1 8545 1 14410 1 12535 2 7312 1 12186 1 6245 1 destitution 16398 1 11400 1 9702 1 3218 1 7413 1 13428 2 9971 1 16078 1 3729 1 6849 1 15898 1 10424 1 2424 1 14658 1 cyprus 9415 1 11716 1 505 1 484 1 1755 1 8484 1 5228 1 4309 1 5105 7 8918 1 717 1 10325 1 7712 1 7012 1 2423 2 11796 1 8997 1 650 1 9420 1 6693 1 13312 1 15688 1 donar 8421 1 13898 1 12094 1 9714 1 13808 1 374 1 7960 1 2586 1 2326 1 13673 1 9398 1 8313 1 nutritious 9540 1 3436 1 14623 1 10828 1 1755 1 9635 1 8595 1 9115 1 9219 1 14493 1 13834 1 14949 1 4951 1 2560 1 6977 1 8886 1 15404 1 5455 1 15267 2 4343 1 10142 1 16104 1 5106 5 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6314 1 2089 2 447 3 7485 2 799 6 6801 3 15574 2 13468 1 7350 2 9540 1 6379 1 13004 3 15376 1 9345 1 1709 1 8614 1 3457 1 14626 1 10007 1 7869 1 9206 1 4698 3 15028 1 1932 2 11956 2 12410 1 311 1 6814 2 9945 1 2106 1 12811 1 4388 1 12812 1 9216 2 14047 1 11964 1 11612 1 2770 1 9278 1 biscayne_bay 505 1 10398 1 5228 3 11859 2 3391 1 3189 2 8044 2 937 1 3873 1 7485 3 8537 1 2882 1 1485 1 inaudible 9830 1 12270 1 8716 1 3546 1 9021 1 15135 1 56 1 483 1 6032 1 14810 1 4335 1 1893 1 4761 1 12082 1 13267 1 4950 1 5395 6 13082 1 9728 1 1292 1 12939 2 12206 1 6178 1 9109 1 3058 1 827 1 fluorine 14449 1 9065 1 7644 1 10965 1 8637 1 10816 1 3837 1 5397 7 1395 1 14979 1 1396 1 13228 1 971 1 7427 1 12928 1 3280 1 10891 1 242 1 10253 2 12972 1 1088 2 16438 1 7189 1 4417 1 12613 1 1064 1 6443 1 6800 2 768 1 16405 1 7119 1 2515 2 9321 1 10050 1 7934 1 9920 1 moluccas 10253 1 9834 1 1755 1 indian_lettuce 12508 1 11189 1 2263 1 9922 1 4169 1 4238 1 9186 1 7900 1 1799 1 5347 1 1845 1 10094 1 7350 1 4829 1 epithelial_tissue 3785 1 13018 2 3556 1 6412 1 5033 1 12956 1 9945 1 5398 6 11017 1 9860 1 7731 1 7473 1 11080 1 14047 1 11312 1 3084 1 3942 1 8830 1 15028 1 12578 1 7285 1 curtsy 7774 2 5883 2 13000 2 16318 2 4242 2 1148 2 7878 2 5926 2 genus_plantago 15039 1 1659 1 11052 1 15267 1 10918 1 6965 1 4698 1 jerboa 6442 1 15940 1 11956 1 607 2 1457 1 1596 1 5399 6 5096 1 9899 1 9590 1 fickle 9926 1 12725 2 1004 1 15799 1 3145 1 12380 1 11358 1 3594 1 8867 2 14323 1 1627 1 12212 1 3615 1 13663 1 2216 1 12145 1 8901 1 12534 1 4707 1 15762 1 7441 1 order_isoptera 7744 1 12848 1 pyrrhus 1226 1 1362 1 3078 1 6974 1 11924 1 14747 1 536 1 betray 11901 2 7069 1 16263 1 4238 1 11010 1 6650 1 6825 1 12556 1 6395 1 13674 1 11907 1 11147 2 8749 1 7239 1 5838 3 4398 1 10153 1 3173 1 11215 1 5078 2 11081 1 14855 1 4590 1 8702 1 2049 2 5901 1 4531 2 5214 1 6090 1 88 1 13498 1 2056 1 13171 2 7886 1 9509 1 10363 3 11710 1 9035 1 8900 2 12704 1 2009 1 646 1 647 1 13371 2 11924 2 1099 1 8645 5 5400 22 3626 1 2509 1 12378 1 1048 1 11646 1 10646 1 2793 1 7899 1 14526 1 4480 1 10444 1 3429 1 10376 1 7260 1 2797 1 13452 2 9109 1 11855 1 16182 1 4017 1 9650 1 9390 1 4093 1 860 1 15503 1 1229 1 1964 1 9116 2 5804 1 13323 1 13192 2 14482 1 15861 1 10989 1 9926 2 12726 1 4617 1 11400 1 6498 2 10872 1 15175 2 227 1 3879 3 10325 2 442 1 15237 1 6685 1 1844 1 2749 1 3450 1 4220 1 9467 1 12797 1 16303 1 5883 1 8442 1 5750 2 9267 1 15110 1 8930 2 16137 1 10077 1 12534 1 8322 1 877 1 6123 1 3531 1 755 1 4998 1 2483 1 5000 2 14309 1 5312 3 13659 1 14838 1 141 2 3605 1 11684 1 1448 1 7120 2 7122 1 11547 1 15034 2 14107 1 15123 1 14638 1 saint_kitts_and_nevis 505 1 15688 1 blackberry 6073 1 15192 1 10535 1 12586 2 4238 1 4263 1 12847 1 9560 1 1395 1 15636 2 1907 1 11436 1 2414 2 13228 1 6169 3 13583 4 6367 1 9057 1 10321 1 5455 2 15267 1 1770 2 3684 3 12795 2 8021 1 8061 1 11017 1 7485 1 1403 1 6801 1 8581 2 7350 1 8166 1 906 2 5906 1 4623 1 15612 1 14959 2 10704 2 10742 2 9477 1 3258 2 6182 1 14137 1 11507 2 3375 2 1099 1 5756 2 6419 2 4698 1 4275 1 5401 24 14077 1 1513 1 3661 2 11574 1 1255 1 13698 1 13760 1 5915 3 7618 2 8650 3 1560 2 11647 1 12008 1 7904 10 11897 1 9950 1 3025 1 8197 2 12353 1 14047 2 2921 1 13482 1 larch_tree 5295 1 new_zealand_cotton 2335 1 13086 1 2598 1 15788 1 5928 1 14297 1 15840 1 3524 1 assurance 6011 1 4866 1 5671 1 573 1 8152 2 16182 1 12325 1 219 1 8821 1 1992 1 10220 1 1939 1 8542 1 12815 1 929 1 2863 1 15824 1 7404 1 16117 1 9886 1 4588 1 3839 1 9116 1 12043 1 12233 1 14482 1 4129 1 5078 6 3174 1 14197 1 5841 1 6770 1 5493 1 5899 1 4680 1 16239 2 1875 1 15765 4 9400 1 1947 1 338 1 7983 1 7544 1 443 1 9826 1 9264 1 16484 1 15445 1 13434 1 526 4 10879 1 8408 2 2204 1 16013 1 1635 1 7734 1 3592 1 12643 3 3145 2 12437 1 474 1 9407 1 7142 1 14691 1 9717 1 8028 1 10559 1 4110 1 14864 1 11742 2 4035 1 8192 1 7226 1 7455 2 3626 1 2945 1 2646 1 5402 15 14009 1 13698 1 14913 1 7648 1 10465 1 13376 1 4169 1 8447 1 15075 1 695 1 7899 1 10340 1 9971 1 13408 1 12448 1 14838 1 10370 1 10570 1 7593 1 1292 1 11963 1 13949 1 8476 1 13847 1 14636 1 16318 1 6792 1 13706 1 stupendous 824 2 2945 1 12175 2 7012 2 3354 2 9613 2 5249 1 11686 2 14352 2 10758 2 6497 2 4384 2 2885 2 14838 2 16051 2 4965 2 11133 2 8361 2 nazified 4738 2 12534 2 4871 2 3660 2 10119 4 13635 2 11686 2 15033 2 3446 2 unexacting 4164 1 7750 1 4057 1 explain 14973 1 6648 2 7504 1 13415 1 8153 1 3242 1 253 1 1939 3 12623 1 9425 1 14977 1 8628 2 13422 3 4523 1 11693 1 7372 1 2326 2 2190 1 13678 1 15898 1 4525 1 3676 1 9285 1 13553 1 8751 2 14060 1 3401 1 5776 1 10617 1 2836 1 11821 1 6655 1 13930 1 2329 1 9154 1 892 2 9955 2 3404 2 894 1 10483 2 3679 1 5901 3 15533 1 9705 1 5165 1 5271 1 14465 1 12898 1 7132 1 6145 1 5528 1 15642 1 14201 1 9826 1 16387 1 8408 1 1538 1 4069 1 4659 1 11835 1 10492 1 13034 1 906 1 4660 2 11163 1 12643 2 10363 1 529 6 9297 1 8026 4 2568 1 12646 1 3926 1 3925 1 16019 2 2337 1 4537 2 7029 2 5173 2 1282 3 533 1 11309 1 12775 1 13576 1 5794 1 536 2 7256 1 11172 3 653 1 11999 1 11033 2 14600 2 8040 1 1680 1 5279 1 278 3 7649 3 2581 1 12003 1 2214 2 8909 1 14738 1 12255 1 5403 75 4912 1 4082 1 1290 1 158 1 8180 2 2859 2 1053 1 9587 1 4084 1 1054 1 1055 3 7393 1 6791 2 10788 1 14604 1 9048 1 1959 1 6296 1 12521 1 13052 1 2989 1 13315 1 6794 1 12392 1 16298 1 6045 1 10247 1 7154 1 11325 1 11860 1 16410 1 5674 1 11461 1 1964 1 15436 2 1301 2 10664 1 4813 1 9055 2 11592 1 4555 1 9457 1 8055 1 7665 1 2466 1 8922 1 673 1 13064 2 6439 1 6929 1 3003 1 14236 1 6442 1 2366 2 553 2 10926 1 12795 2 4688 1 5813 1 13722 1 15805 2 12796 1 15301 1 5427 1 7170 3 10262 1 11057 1 10397 1 12677 1 5305 2 4823 2 15934 1 9203 3 62 1 5816 1 9478 1 2754 1 11883 1 4110 1 13214 2 8073 1 1318 1 12939 1 8192 2 4446 4 2377 1 1579 1 2882 1 1982 4 15940 1 13075 1 12158 1 15459 1 12540 1 5439 1 7932 1 9486 1 10407 1 9212 1 4451 1 10144 2 15191 1 68 1 5205 2 4707 3 8946 1 12953 2 16316 1 2111 1 11491 1 16211 1 13486 1 4332 1 1333 1 6336 1 326 1 6462 1 12556 1 4950 1 2620 1 8083 1 7824 1 8571 2 7942 1 15046 1 9891 1 5702 1 11081 1 16327 2 2490 1 5323 1 10421 2 4340 1 14126 1 4839 2 12561 1 5967 1 1204 1 15590 1 12563 1 469 2 1595 2 8351 1 6716 1 7581 1 12308 2 8209 1 10424 2 5849 1 2004 1 10426 1 14658 1 11917 1 9509 1 15839 2 4466 1 8831 1 15334 1 9242 1 14802 1 8463 1 10430 2 13754 2 13626 2 473 1 9517 1 98 1 12707 3 4855 1 6720 1 14806 1 9779 1 10436 1 9519 1 4860 1 12578 1 6976 1 11108 1 8716 1 2646 3 6845 1 6102 1 14009 1 211 1 11927 2 8718 1 5727 1 3507 1 11381 1 4975 1 14281 1 14015 1 11931 1 13765 3 15720 1 14018 1 2797 1 14411 1 1611 2 15856 2 851 1 7966 1 8851 1 3634 1 4017 1 1902 1 1903 1 8591 2 11120 1 5120 3 2143 1 16472 1 14948 1 7851 1 1490 1 15089 1 6238 1 9655 1 5616 2 10584 1 2532 1 12081 1 5240 2 11943 1 741 1 12208 1 4267 1 7729 1 5621 1 1501 1 7342 1 7477 1 2415 1 11403 2 13260 1 11404 1 6241 1 5244 1 16356 1 9400 1 8246 1 9535 2 11406 1 15737 1 14427 1 5131 1 8609 1 13263 1 1243 1 14031 1 1778 1 6375 1 4495 3 2159 2 11264 1 1920 1 12730 1 3218 2 13795 1 6627 1 4499 1 14555 1 4761 1 7990 1 6247 1 8137 1 11268 1 1784 1 2423 1 4273 2 8739 1 8258 1 15880 1 11954 1 3223 1 9139 2 3377 1 508 1 4502 6 15989 1 1510 1 5888 1 3225 2 3766 2 13919 1 6384 2 14041 3 5254 1 4041 2 11002 4 1014 1 2546 1 10465 1 13277 1 7360 1 13280 1 5889 1 6260 1 1136 2 4172 1 7875 1 16142 1 629 1 10343 2 11686 1 13812 1 6520 2 14047 1 12620 3 1656 1 13814 5 historical_linguistics 6849 1 2423 1 15334 1 10841 1 8122 1 1611 1 1099 1 1408 1 14060 1 12255 1 14931 1 financial_condition 5183 1 10698 1 8950 1 3924 1 8941 1 8246 1 13422 1 6249 1 2652 1 761 1 3101 1 536 1 5240 1 esthete 8438 1 8081 1 16492 1 librium 9546 1 14251 1 15251 1 8443 1 15923 1 boob 1510 1 510 1 eskimo 9666 2 1737 1 15523 1 13819 1 3 1 10539 1 4 1 13801 1 12534 1 11147 1 6463 1 3907 1 3676 1 14002 1 1231 1 4502 1 7408 1 1909 1 8010 1 2560 1 1579 1 877 1 8934 1 4829 1 13712 1 7256 1 3404 1 13785 1 10102 1 5311 1 1501 1 3088 1 5179 1 9291 2 14717 1 7634 1 5164 1 13606 1 11067 1 1558 4 16314 1 7636 1 13648 2 2106 1 5404 9 12020 1 15518 1 5461 1 9664 1 1685 2 15573 2 11612 1 13897 1 9768 1 10070 1 homo_sapiens 573 1 4123 1 4155 1 13552 1 5187 1 14363 1 12848 1 13674 1 2301 1 7158 1 10418 2 14940 1 1510 1 5756 1 9190 1 2960 1 10544 1 931 1 1982 1 11297 1 14600 1 3992 1 6890 1 2364 1 8246 1 13648 1 16495 1 5849 1 5426 1 5405 5 13965 1 11612 1 12600 1 2488 1 unclothe 9587 1 7658 1 98 1 coccyx 906 1 4591 1 12981 1 10306 1 1354 1 1651 1 1836 1 16405 1 6428 1 7058 1 7474 2 11474 1 1131 1 tracheophyte 15387 1 13063 1 14600 1 luxemburger 5228 1 6059 1 15688 1 knotty 12813 1 10492 1 8822 1 14936 2 7154 1 12966 1 7851 2 9135 2 7158 1 15997 1 2755 2 10918 1 15681 1 15976 1 2560 1 3501 1 11081 1 2873 1 8702 1 14041 1 7376 1 11594 1 8343 1 9175 1 8243 1 538 1 7649 3 5607 1 5349 1 7360 1 15832 1 1426 3 2214 1 1516 1 14838 1 12898 1 16051 1 9535 1 3760 1 12036 1 13648 1 1053 1 10548 1 792 1 4310 1 7904 1 2407 1 3435 1 12353 1 1685 1 11769 1 14636 1 13068 1 10468 1 7350 1 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996 2 11830 2 769 2 12795 2 1596 6 14933 1 11055 1 4874 1 13298 2 5594 2 10965 1 14209 1 9477 1 9610 1 9775 1 719 1 9167 2 7587 1 11777 1 12608 1 8678 3 11171 3 12186 3 16099 1 3463 1 1479 1 8980 3 5954 1 14838 1 1355 1 12809 1 13845 1 9081 1 7296 2 68 2 2918 2 7816 1 3536 1 11964 2 11612 2 4365 1 12295 3 3238 1 11287 1 5832 1 3348 1 shock_wave 4529 2 12596 1 2011 1 4612 1 6830 1 12677 1 14461 1 10880 1 6279 1 4950 2 birdnest 3377 1 8197 1 6890 1 tempering cochlea 2126 1 4570 1 6907 1 1128 1 16391 1 5771 1 13821 1 4950 1 9886 1 1836 1 13422 1 9055 1 12609 1 1805 1 15966 1 16327 1 536 1 14992 1 7256 1 366 1 1134 1 12669 1 7556 1 3968 1 5328 1 14526 1 5033 2 227 2 1168 1 9914 1 14584 1 1180 1 5563 10 15058 1 1450 1 13099 1 15803 1 6178 1 12353 1 1484 1 11985 1 11474 1 gadaba 12343 1 10070 1 cabin 4944 5 2519 1 12718 1 15254 1 8592 1 8117 1 12077 1 8183 1 5264 1 11818 1 6830 1 6652 2 8512 1 4211 1 6925 1 16003 1 44 1 891 1 3136 1 1591 1 15403 1 5128 1 14351 1 5648 1 14461 3 14352 1 1876 1 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11914 1 7702 2 2005 1 8353 1 6717 1 14658 1 1596 1 5088 1 4464 1 11771 1 4466 1 8831 1 11368 1 13626 1 8582 1 2008 3 2502 2 344 1 15846 1 11777 1 14665 3 3858 1 7454 1 12579 1 12972 2 15491 1 484 1 2012 2 13635 2 15714 1 13511 2 4736 1 15854 2 5104 1 9644 1 15078 1 9784 1 5729 2 9250 1 10977 2 12068 2 12978 2 2407 1 1357 2 1484 1 11939 1 9920 2 1221 1 15228 1 4260 1 856 1 7850 1 8238 1 1491 1 1906 1 9393 1 6989 1 3065 1 10456 1 11395 1 13643 1 6366 1 5616 1 10989 1 5240 1 5878 1 7729 2 8991 1 9122 1 13392 1 6498 1 6497 1 12467 1 7607 1 8863 1 13648 1 4990 1 12597 1 12599 1 2159 2 8129 1 16485 1 13264 1 14956 2 1635 1 13536 1 12731 1 4164 1 620 1 3367 1 11672 1 7218 1 14557 2 9411 1 6120 1 3223 2 7226 2 1513 3 244 1 13150 1 13011 1 1136 1 11420 1 1654 1 6389 1 5766 1 16259 1 13665 2 14188 2 11424 1 1390 1 13286 1 5258 1 10218 1 despitefully 911 1 2374 1 15815 1 samara 4293 1 11619 1 9032 1 3401 1 10158 1 11964 1 freemasonry 7069 1 10430 1 5718 1 13079 1 12181 1 14220 1 14719 1 14199 1 3029 1 8031 1 13490 1 1570 1 3199 1 12728 1 kalamazoo 14601 1 1331 1 1112 1 12553 1 1755 1 de 13034 1 4090 1 7142 1 12439 1 3064 2 10906 1 7645 1 2023 1 13089 1 3874 1 13461 1 5687 8 6655 1 11925 1 10891 1 9021 1 2732 1 7230 1 1149 1 15459 1 11512 1 14352 2 10853 1 7133 1 3815 1 13381 1 6833 1 7485 1 2034 1 7234 1 16109 1 9767 2 a_bit_much 14979 1 10681 1 4714 1 9943 1 9918 1 2703 1 15110 1 12268 1 5169 1 genus_hymenophyllum 12786 1 12605 1 5347 1 8879 1 9635 1 10141 1 fisheries 8319 1 5897 1 2877 1 10335 1 2220 1 3164 1 11390 1 5271 1 7516 1 genus_sterculia 10176 1 5173 1 3258 1 6790 1 10983 1 15272 1 9206 1 relentlessness 15944 1 6781 1 8180 1 koto 11666 1 11282 1 13538 1 gastric_ulcer 5766 1 11080 1 6456 1 2012 2 12416 1 12515 1 13150 1 6003 1 sensual 8426 2 14532 1 4293 1 9981 1 7202 2 4761 2 605 1 1886 2 13238 1 10232 1 6108 1 8103 2 12959 1 1176 1 10781 1 12776 1 592 3 2320 1 7669 1 5914 4 13259 1 2546 1 5690 11 85 1 9532 1 15279 6 11870 2 15191 1 1092 1 14338 1 6283 2 11612 2 13265 1 3348 1 nurser 15918 1 7098 1 14838 1 623 1 kinesthesis quadruple 7125 1 8997 1 6274 1 15812 1 1562 1 1389 1 8449 1 6464 1 6244 1 13888 1 12578 2 theory_of_organic_evolution 874 1 7794 1 5849 1 15637 2 9268 1 4394 1 16019 1 15472 1 2525 1 9519 1 12889 1 12848 1 american_revolutionary_leader 5184 1 1737 1 1407 1 3350 1 9242 1 13035 1 6200 1 253 1 8029 1 16117 3 13422 1 8216 1 11147 1 13088 4 6527 1 1996 1 5929 1 15200 1 7389 1 5223 6 13598 1 3427 1 3679 1 484 1 7729 4 11034 1 7178 1 7789 1 973 3 2001 1 1555 1 5691 15 8502 2 5271 1 3128 1 3213 2 1292 1 267 1 5745 1 14795 1 11897 1 13451 2 12700 1 13949 2 8408 1 9214 1 145 2 6801 3 by_artificial_means 1635 1 5816 1 7068 1 12584 1 2662 1 16492 1 st_james 1053 1 12726 1 1229 1 15941 1 6032 1 2056 1 1072 1 12268 1 10049 1 cestoda 13712 1 icily 1331 1 10750 1 14572 1 1501 1 1203 1 12326 1 4737 2 15906 1 12795 1 12102 1 2652 1 2918 1 5317 1 11789 1 8365 1 2149 1 buzzer 12731 1 14015 3 13702 1 12274 1 16096 2 2785 1 2408 2 13606 1 8632 1 11298 1 pervade 13177 1 1014 1 1362 1 5305 1 9219 2 6860 1 12623 2 1595 1 15089 2 4031 1 11406 1 3135 1 9334 1 6243 1 13889 1 15225 1 1833 1 12352 1 12743 1 2016 1 16319 1 6894 2 indispensable 7969 1 14071 1 12644 1 4141 1 9423 1 3242 1 11223 5 1616 1 12059 1 13674 2 737 1 13271 1 4473 1 4338 1 5543 1 16416 1 4200 1 1014 1 5725 1 6498 1 6667 1 5692 6 7065 1 16405 1 8385 1 746 1 554 1 1596 2 7169 1 201 2 11939 1 16231 1 4483 1 12038 1 14956 1 9920 2 obsessively 5261 1 12097 1 4850 1 7353 1 12383 1 314 1 3617 1 13490 1 13431 1 15933 1 2727 1 13412 1 13091 1 6138 1 15849 1 order_parietales 8898 1 13712 2 backward_and_forward 1635 1 12718 3 7140 1 13531 1 9704 1 9705 1 2924 3 7744 1 9971 2 14930 1 15170 1 3815 1 5028 1 1521 1 10727 2 4858 1 1685 1 4921 1 9899 1 14506 1 root_canal 12472 1 9047 1 13821 1 1698 1 file_transfer_protocol 6011 1 16355 1 2960 1 14864 2 assassin 763 1 5082 1 8302 1 10139 1 3145 1 9833 2 8472 1 6497 1 5439 3 15249 1 8942 1 8134 1 13820 1 16117 1 3879 1 13674 1 2608 1 7184 1 8384 1 1735 1 5693 5 13171 1 3025 1 10899 1 1632 1 7742 1 6801 1 11612 1 reprehension 9240 1 8362 1 3533 1 2759 1 fibonacci_sequence 4925 1 glooming 12424 1 10956 1 10298 1 7239 1 2250 1 9416 1 6419 1 evergreen_plant 6778 1 1738 1 10310 1 2563 1 1893 1 9312 1 6287 1 3938 1 12786 1 1200 1 14548 1 8083 1 9074 1 9216 1 6419 1 martian 7218 1 7697 1 2345 1 7102 1 11849 1 5929 1 15445 1 9665 1 13581 1 chimariko 13648 1 9102 1 10070 1 caddisfly 13609 1 7413 1 3425 1 1437 1 3875 1 7769 1 8675 1 malingering 13209 1 13275 1 14113 1 15933 1 10846 1 10092 1 1907 1 dutifulness 905 1 4539 1 16079 1 13000 1 12643 1 abalone chrome 5332 1 12054 1 6983 1 3556 1 leisured 11461 1 2865 1 12410 1 1642 1 12241 2 1855 1 5214 1 7181 1 3744 1 3949 1 510 1 3952 1 13110 1 13674 1 selenium 151 1 14374 1 13392 1 15038 1 10369 1 7658 1 4261 1 11327 1 897 1 15636 1 8666 1 6910 1 5041 1 9467 2 13273 1 13453 1 formula 7567 2 14918 1 12231 1 6525 1 5695 27 10351 1 389 1 4523 1 15636 1 12111 1 3397 1 15898 1 1526 1 13490 1 3320 1 15263 1 8751 1 4529 1 14648 1 5841 1 3679 2 7190 1 9566 1 14929 1 12898 1 7133 1 152 1 8090 1 15642 1 13029 1 3684 2 9826 4 835 1 11017 1 6349 1 13034 1 6779 1 8094 1 11920 1 10430 1 2058 1 2337 1 3794 1 8529 1 1282 1 5654 1 14211 1 15712 1 14215 1 9908 1 5913 1 16398 3 11172 1 15491 2 14009 1 11376 1 7649 1 12003 2 8719 1 2650 1 8180 1 12008 1 13182 1 33 1 7464 1 14226 1 2462 1 3060 1 9527 1 4017 1 663 1 15430 1 8047 1 4260 1 9451 1 4093 1 7852 2 5926 1 1836 1 1228 2 2865 1 12721 2 15729 3 5126 1 7536 2 3874 1 6367 1 5240 1 990 1 10253 1 1499 1 1364 2 12669 1 12792 1 14161 1 6498 1 6241 1 1770 1 1065 1 12856 1 10192 1 9662 1 5498 1 13395 1 15444 1 15933 1 13263 1 12020 1 11533 1 4164 1 5883 1 7802 1 1370 1 10265 2 10741 1 12286 1 10816 1 5815 1 8869 1 7222 1 61 1 12534 1 15880 1 3376 1 2173 1 510 1 2175 1 10820 1 2176 1 4041 1 7294 1 4169 1 8264 1 11278 1 6328 1 13283 1 11686 1 5068 1 13285 1 1021 1 bask 1457 1 236 2 4661 1 5696 7 13214 1 10743 1 358 1 15339 1 7697 1 1510 2 5756 1 2261 1 11554 1 5361 2 536 1 13712 1 4829 1 10596 1 7457 1 15279 3 657 1 2214 2 7134 1 848 1 12838 1 6545 1 13878 1 8211 1 11936 1 12178 1 unflagging 1850 1 4331 2 8821 1 6781 2 5697 8 10794 2 11309 1 1645 1 12959 1 4338 2 753 1 11081 1 9429 1 4130 1 15509 1 9367 1 11453 1 7430 2 265 2 16330 2 9400 2 14181 2 228 1 5918 1 1448 1 13684 1 12543 2 9537 2 12569 3 7313 1 2445 2 12620 2 15877 1 16013 1 9181 1 2956 1 family_punicaceae 1261 1 prison_term 945 1 13422 1 4659 1 6359 1 9021 1 7376 1 14126 1 11849 2 13076 1 1678 1 4155 1 4120 1 1730 1 16356 1 plead 9340 1 250 1 4466 1 9964 1 6378 1 10824 1 3636 1 10115 1 2300 1 8897 2 7488 1 15195 1 5698 9 8322 1 16080 1 5888 1 2872 1 811 1 5586 2 3404 1 4340 1 3032 1 4097 2 7413 1 7178 1 3483 1 265 1 8144 1 14838 1 13736 1 11406 1 3287 1 10755 1 6716 1 2485 1 11848 1 12795 1 14795 1 11500 1 12352 1 10772 1 7169 1 11612 1 6801 1 6835 1 cautionary 4247 1 3382 1 5926 1 amusing 6011 1 15818 1 7264 1 219 2 15397 3 9359 1 14975 2 10247 1 3839 2 5699 21 13163 2 4429 1 16081 1 8857 1 2661 1 7130 2 11081 1 8793 4 3951 1 2998 1 9955 2 14062 1 5493 1 6770 2 10483 1 14351 1 2707 1 82 2 12051 1 8314 1 13428 2 2083 1 12995 3 10324 1 2366 1 5941 2 15445 1 447 1 4402 1 8408 1 2815 1 9862 1 13863 2 4656 1 4250 1 14934 1 12435 1 1406 1 9340 1 10492 1 10363 2 12283 1 1738 1 13797 2 5536 2 6972 1 10775 1 13110 1 1784 2 6630 1 16225 2 13371 2 1510 1 2343 4 10269 1 7323 1 10889 1 4202 1 7178 1 2546 2 813 2 8143 1 486 2 8810 2 4701 2 3229 2 3382 1 16344 1 6545 1 8984 1 4673 1 1656 1 2016 1 5983 1 11612 1 6852 1 9817 1 2111 1 umbrella 8879 1 6336 1 14975 1 702 1 10221 1 9760 1 12041 2 14510 2 13086 1 10475 1 1395 2 6080 1 6828 2 8881 1 5700 31 3320 1 14196 1 7697 1 257 2 13091 1 4129 1 8277 1 5382 1 14263 1 8012 1 8702 2 2263 1 13356 1 3406 1 3991 1 1732 1 8827 1 4957 1 16008 1 11496 1 5271 1 11827 3 14267 1 769 1 1667 1 16389 1 1818 1 4002 1 4659 1 1344 1 2850 1 13626 1 5718 4 7522 1 8764 2 8835 1 7453 2 10295 1 779 1 12910 1 8903 1 12970 1 7896 1 11509 1 11312 1 4542 1 1047 1 6230 1 11033 1 14009 1 484 1 3126 1 10970 1 3744 1 6727 1 1104 1 2514 1 7260 1 11320 2 2797 4 599 1 1685 1 4550 1 981 1 421 1 16112 1 14675 2 11244 1 7657 1 10983 1 3636 1 15431 1 2655 1 9115 1 7658 1 12786 1 1300 1 12395 1 5347 1 7537 1 13461 1 4986 1 3703 1 293 1 12270 1 4925 2 10867 2 14888 1 3758 1 7789 1 2152 1 6176 1 6498 6 14092 1 10872 1 10672 1 10874 1 4928 2 13395 1 7983 1 14297 1 13204 1 11666 1 5427 1 7169 2 176 1 11474 1 7350 1 11875 2 6180 2 3218 1 11476 1 14555 2 11268 1 10998 2 2241 1 7683 1 5689 1 2760 1 11275 1 4169 1 13277 2 2247 1 6697 1 2613 1 14502 2 15461 1 8560 2 3158 1 15579 1 4707 1 12353 1 9277 1 6894 2 15756 1 meticulously 15967 1 11876 1 3862 1 8246 2 4272 1 2680 1 2623 1 16495 1 14140 1 14247 1 8523 1 7824 1 14481 1 863 2 14007 1 11975 1 7494 1 servitor 2326 1 1248 1 6498 1 7218 1 7313 1 13096 1 10177 1 violin_section 16494 3 4998 1 768 1 6105 1 14351 1 14892 1 8965 1 6596 1 7269 1 4761 1 11878 1 14047 1 6656 1 face_value 5934 1 13680 1 1132 1 11773 1 5120 1 8153 1 6373 1 15075 1 13552 1 152 1 16078 1 14823 1 585 2 13490 1 13888 1 8468 1 4001 1 14626 1 2672 1 11571 1 3480 1 7006 1 16303 1 cop 3472 1 9847 1 5197 1 14134 1 6011 1 4916 1 1074 1 15818 1 10363 1 9097 1 573 1 10658 1 4712 1 758 1 3636 1 2448 1 6781 1 8254 1 2114 1 3258 1 4093 1 1300 1 9776 1 2598 1 5701 5 3480 1 14075 1 5645 1 2490 1 6530 1 9289 1 1014 1 932 1 11646 1 2214 1 12994 1 1259 1 1136 2 10324 1 12334 1 12101 1 9063 4 5849 1 8665 1 11686 1 11949 1 4534 1 747 1 12620 1 14984 1 7678 1 7014 2 increased 13414 1 14193 1 4180 1 8157 1 4397 1 13674 1 5377 1 14979 1 4398 1 520 1 4185 1 14197 1 763 1 9429 1 3103 1 10483 1 15765 1 10226 1 1276 1 15269 1 12898 1 12635 1 7636 2 6144 1 4653 1 13684 1 2335 1 2849 1 6147 1 12643 1 11307 1 8637 1 2337 1 777 1 531 1 7893 1 1547 1 16026 1 2571 1 2066 1 536 1 6419 1 2577 1 7390 3 14994 1 538 4 9176 2 539 1 5403 1 12916 1 8180 3 2859 1 4422 2 1055 1 10786 1 7393 1 1562 1 3435 4 8424 1 542 1 13953 1 12393 1 15285 1 9451 1 15790 1 3701 1 5927 1 6168 1 3135 1 12395 2 1301 1 7663 1 10252 1 3703 1 7665 1 7164 1 8663 1 6929 1 11327 1 7167 1 16051 2 6442 2 13465 1 6443 2 11870 1 5942 1 795 1 13722 1 15572 7 7169 1 800 3 8438 1 7421 1 4933 2 10809 1 14243 1 62 1 4446 1 2377 1 2881 1 11339 3 12943 3 14772 2 3459 1 8939 1 9483 1 5439 1 15312 1 12159 1 313 1 11896 1 8449 1 4706 1 4707 1 12161 1 8946 1 5694 7 3469 1 7934 1 4944 8 15583 1 2386 1 12164 1 9219 2 6958 1 2114 1 16321 1 2623 1 13489 1 6464 2 13745 2 15828 1 2627 1 5964 1 6211 1 11080 1 15046 1 5702 76 16327 1 827 2 81 1 15048 1 5705 1 10836 1 3992 1 15831 1 11625 1 7189 1 15833 1 1595 1 12962 1 15056 3 3729 1 8970 1 15058 2 11631 1 9767 1 4250 1 7707 1 15839 1 13505 1 14800 1 341 1 16333 2 11920 1 15840 1 5717 1 5220 5 3499 1 585 3 3191 1 10848 1 6477 1 10851 1 4735 1 4476 1 2646 1 2405 1 2135 2 1893 1 2650 1 11931 1 2407 1 2652 1 14018 1 4014 1 11939 1 10576 1 15348 1 601 1 13770 1 5233 1 11119 1 15354 1 5735 1 15089 2 10861 1 12723 1 6736 1 15093 1 5240 3 6739 2 6495 1 6498 1 11662 1 10324 1 7730 1 5745 1 4756 1 10067 1 8997 3 4029 1 2159 2 869 1 2161 1 12732 3 3218 3 3524 1 13795 1 16136 1 7002 1 4031 1 6508 2 4273 1 5248 1 13804 1 11677 1 13008 2 7006 2 2425 1 13808 1 2945 1 14835 1 13277 1 14838 1 1136 1 7233 1 11686 2 5766 3 9552 1 3237 1 10605 2 6264 1 11966 1 7015 1 10082 1 13018 1 3242 1 1939 1 13552 4 3539 1 13821 1 15635 1 7757 1 14319 1 2190 1 6274 1 5776 1 2960 1 7509 1 892 1 392 1 5778 1 9021 1 4531 1 14579 3 14066 1 12764 2 4534 1 6536 2 2715 1 9830 2 15908 1 15407 1 9833 1 7523 1 3258 1 5027 1 8032 1 5029 1 10098 1 15917 2 7772 1 7774 1 9305 1 918 1 5279 7 657 2 2214 2 6542 1 5033 3 2983 1 1169 2 10372 1 11177 1 9587 1 16405 2 16177 1 14873 1 1959 1 13581 1 7532 1 927 1 14605 1 3803 1 1419 1 16183 1 8309 2 13323 1 9055 1 15666 1 8547 1 2466 1 8056 1 11462 1 14352 3 5045 1 2229 1 15441 2 15933 3 3817 1 6801 1 15674 4 11474 1 16425 1 6059 1 6565 1 12284 2 3820 1 944 1 12534 1 11739 1 8072 1 7291 1 12804 1 9074 1 14626 1 14911 1 3299 1 1982 1 15940 1 15186 2 8559 1 14374 1 5313 1 15461 1 4323 1 6580 1 14636 1 2769 5 2488 1 8333 3 15948 1 11753 1 11755 1 4588 1 8824 1 10421 1 2263 1 4839 1 10161 1 7581 1 10424 2 13099 2 13100 2 12826 1 9897 1 9899 1 11773 1 10965 1 4847 1 8582 1 7585 1 4851 1 8361 1 4143 1 14940 1 7319 2 11779 1 9909 2 12578 1 12579 2 16227 1 484 1 11512 1 1479 1 3862 1 14147 2 11238 1 8114 1 9107 1 11789 1 730 1 11517 1 982 1 3350 1 12324 1 6854 1 13900 1 4155 4 3351 1 14948 1 4156 2 3064 1 5870 1 3065 1 13903 1 3874 2 5127 1 7855 1 2809 1 9118 1 10457 1 5128 1 15235 1 2535 1 8862 1 15736 1 10195 1 13648 1 16483 1 996 1 2036 2 9665 2 5883 1 15987 1 14691 2 16489 1 1787 1 6384 1 9942 1 15244 2 9142 1 16491 1 4169 2 8392 1 6890 1 12617 1 3386 1 13665 2 4634 1 12352 2 12620 3 corokia bough 8438 2 6377 1 4293 1 5518 1 12231 2 9833 1 12733 1 9135 1 2912 1 15759 1 13801 1 9116 1 2326 1 10297 1 3376 1 14196 1 8192 2 5964 2 3875 2 15472 1 877 1 2809 1 5703 15 6230 1 3195 1 5576 1 15000 1 5257 1 4401 1 11303 1 1667 2 5651 1 10131 1 6820 1 7384 2 15771 1 ski 4943 3 8539 1 12718 1 5770 5 13668 2 12325 2 10535 2 927 1 16377 3 10247 1 11693 1 8728 1 9285 1 3874 1 9228 2 13063 1 3582 1 12989 1 8084 1 5704 24 10483 1 16047 3 10870 1 9705 3 1699 1 9397 1 6241 1 15933 1 5745 3 12998 1 12675 3 16245 1 1211 1 234 1 11307 2 10556 6 13795 1 12315 1 5173 1 12440 1 8032 1 970 2 9723 3 14626 1 11339 1 2317 1 15118 2 6230 1 2546 1 11110 3 10340 2 14634 1 10690 7 16494 3 12007 1 14106 3 11282 1 12543 1 9918 1 14189 1 2111 6 containerized 15486 1 1211 1 10834 3 5632 1 1478 1 2953 2 8800 1 2792 1 4112 1 1604 1 859 1 west_end 16494 1 2332 1 4287 1 6830 1 5228 1 6890 1 2921 1 12555 1 11101 1 4829 1 coldness 4659 1 906 1 1331 22 14816 1 3113 1 6318 1 10707 1 15470 2 8900 1 4588 2 2864 1 689 1 3017 1 13040 1 23 1 972 2 12465 1 7304 1 9822 2 12943 1 2945 1 11400 1 9021 1 5706 11 7412 2 7527 1 14127 3 866 1 16491 2 16172 2 4228 1 5439 1 2563 1 2214 2 1893 2 2669 1 11438 1 895 1 615 1 13735 1 15056 1 1595 1 1136 2 12383 1 10033 1 8996 1 11749 2 6850 2 8849 1 7393 1 13608 2 11631 2 12620 1 whirlwind 905 2 3329 2 16263 1 10332 1 7189 1 14469 1 213 1 8863 1 7009 1 7881 1 1180 1 4143 2 2598 1 10036 1 11863 2 2053 1 6596 1 839 1 14941 1 10745 1 6894 1 9754 1 1764 1 dimensional 9494 1 4284 4 1939 1 6462 1 4335 1 704 3 8156 1 8396 2 4777 1 12041 1 4950 1 7372 1 4526 1 3676 1 8571 1 10418 3 2260 1 7697 2 9564 2 2960 1 7304 2 13354 1 9153 1 14648 1 763 2 8754 1 5705 1 10421 1 13493 1 9021 2 2263 2 5707 54 5648 1 10226 1 11695 2 13169 1 1595 1 834 1 14584 2 13358 2 13 1 7195 1 13684 3 14269 1 1667 2 4189 3 5390 1 8523 4 2335 1 10093 1 12826 1 6349 1 7707 1 1211 2 2906 1 15839 1 12643 2 529 1 8214 2 1885 2 4903 1 5022 1 8582 1 15910 1 2282 1 3258 1 2724 1 9033 3 14211 1 9034 1 2912 2 10775 1 8361 1 4007 1 532 1 585 1 10097 1 1347 1 4907 2 15964 1 536 1 12578 1 8292 10 7526 1 6102 1 15715 1 6788 2 8223 1 3862 2 10442 1 4361 1 9971 1 5547 1 14738 1 4912 1 15423 1 13701 1 9728 1 9918 1 1108 1 15922 1 6161 2 9920 2 13581 1 11517 1 14226 1 7398 1 12718 1 12522 1 5120 1 12586 1 10310 1 9451 1 4487 2 3064 1 7210 1 2301 1 14026 1 7661 1 4985 1 12722 1 670 1 3950 2 611 1 10385 1 672 1 7976 2 5128 2 7729 1 9659 1 8059 1 12792 1 12995 2 1309 1 934 1 8246 2 1180 2 1428 1 2366 2 996 1 3816 1 15933 1 373 1 14619 1 9860 1 15572 1 10131 2 9197 1 13965 1 5945 1 7421 12 4933 1 3145 1 10812 1 6750 1 10741 2 14690 1 4499 3 9477 1 11675 1 5815 6 10816 1 4823 1 9203 2 62 1 14693 2 12534 7 7291 7 9413 2 13214 1 3223 1 8322 1 1713 1 15457 1 1130 1 9941 1 877 6 7995 1 15028 2 3086 2 15940 29 625 2 11002 2 14835 1 3601 1 10465 1 1134 1 16492 3 13277 1 16258 2 4323 1 13736 1 6760 1 1136 1 10406 1 4172 2 3158 5 6328 1 4451 1 7746 1 629 3 14969 1 16495 2 3534 1 12416 2 14044 7 13665 1 11488 1 10018 3 12353 1 7934 1 16144 1 pentecost 7774 1 8558 1 10363 1 573 1 7165 1 11152 1 7230 1 10264 1 4492 1 11141 1 5214 1 12341 1 8954 1 11327 1 2937 1 13080 1 13476 1 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13066 1 6389 1 1367 1 9860 5 1108 1 10018 1 14047 1 6971 3 aegean_sea 10692 2 505 10 15599 2 12123 2 13144 1 15241 1 884 1 2300 1 12181 1 8918 1 14174 1 12533 1 14055 1 15911 1 13821 1 1475 1 13777 1 4031 1 11728 1 9435 1 5347 2 5221 7 4906 1 11619 2 10335 1 9260 1 11924 1 7824 1 11796 3 8033 2 6121 13 293 1 4829 1 244 1 7178 2 12540 1 5228 2 6498 1 11946 1 14527 2 2950 1 7528 1 10325 1 159 1 10374 1 3789 1 7012 3 3816 1 6686 1 13330 1 15301 1 11055 2 1685 2 2594 1 5115 1 13312 2 greyish 6977 1 14077 1 7131 1 2335 1 16329 1 15378 1 8762 1 5033 1 1168 1 as_needed 7969 2 9305 1 909 1 136 1 1132 1 10101 1 3218 1 2136 1 15958 1 16174 1 15656 1 7607 1 4562 1 15833 1 2863 1 804 1 5608 2 14016 1 8881 1 15729 1 8385 1 12416 1 126 2 8202 1 9538 1 9860 1 6122 8 8408 1 5873 1 1805 1 14956 1 8198 1 complement_fixation_test 8226 3 15283 1 4938 1 14009 1 15430 1 10092 2 16026 1 2705 1 14616 1 11308 1 10120 2 fosterage 12336 1 12795 1 prorate 6473 1 3546 1 7238 1 7376 1 1460 1 585 1 16328 1 2135 1 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7697 2 1846 1 3010 2 tearfully 9739 1 9937 1 1839 1 3248 1 10355 1 13171 1 as_a_matter_of_fact 2952 1 10084 1 14941 1 8702 1 9457 1 11931 2 8255 1 9724 1 7195 1 16488 1 tucana 7885 3 957 1 12812 2 1855 1 3303 1 perplexed 542 2 9882 1 1939 2 10333 1 12011 1 6880 1 9886 1 5570 1 9150 1 16461 3 9775 2 14693 1 3065 1 7587 2 14884 1 14405 1 992 1 11002 2 13075 1 3741 1 2546 1 10712 1 9532 1 7649 1 16356 1 6127 12 7672 1 16494 1 3643 1 13648 2 1053 2 7480 1 12619 1 4086 2 8946 1 6520 1 7733 1 15521 2 10218 1 2986 1 11771 2 mathematical_statement 4523 1 6498 1 10333 1 14181 1 exploration 3634 1 10492 1 7451 1 2988 1 16182 2 663 1 10694 1 13819 1 9186 1 10760 2 6972 1 13674 1 685 1 16078 4 15759 1 7238 1 5153 1 7506 1 15899 1 6238 1 3400 3 9853 1 8903 1 7440 1 13576 1 2704 1 6419 1 7509 1 15117 1 223 1 3022 1 2646 1 1014 2 3741 1 2980 1 16491 1 1501 1 3483 1 12995 1 15833 1 13935 1 13749 1 12873 1 6128 12 757 1 2306 4 1773 1 2797 1 10771 1 9664 1 11917 1 15020 1 4689 1 14047 1 13264 2 12919 4 7285 1 7350 1 sledding 11777 1 11309 1 9532 1 3926 1 1976 1 2922 1 unreservedly 14762 1 2241 1 13415 1 chestnut_tree 1583 1 4734 1 2263 1 1667 1 4994 1 6727 4 12815 2 8581 1 12847 1 overall 12521 1 323 7 3436 1 9819 1 3242 1 12693 1 4335 1 2114 1 7209 1 5412 1 13674 2 11821 1 6925 1 222 1 10867 1 9705 2 14614 1 7917 1 3952 1 12567 1 15933 1 12795 1 154 1 1667 1 126 1 15572 1 16163 1 3712 1 1635 1 1920 2 4347 1 11773 1 16429 1 4847 2 1279 1 3525 1 11709 1 9203 1 8137 1 6473 1 2009 1 779 1 2399 1 9413 1 13575 1 3376 1 1130 2 13374 1 1787 1 15071 1 2577 1 8743 1 3049 1 5032 1 9418 1 11419 1 6129 8 12416 1 9918 1 16178 1 15123 1 2142 1 3269 1 9920 1 bacteriologic 2769 1 6980 1 12612 1 2627 1 5926 1 15765 1 anatomical 5985 1 8595 1 4552 1 4335 1 8156 1 2802 3 5412 2 12848 1 1836 1 15695 1 13423 1 4643 1 12266 1 1999 1 7977 1 8890 1 6278 1 14616 1 9400 1 10033 1 10127 1 10128 1 5849 1 1667 1 8550 1 7801 1 8802 1 5273 1 7421 1 2971 1 11366 1 4030 1 6780 1 11735 1 11709 3 1472 1 9477 1 5505 1 7387 1 2423 1 7740 1 689 1 4907 1 5756 1 9141 1 12943 1 6324 1 13177 1 9079 1 6257 1 11574 1 5278 1 13150 1 3830 1 1986 1 2735 1 13736 1 8421 1 15554 1 10978 1 6130 13 5766 1 10018 2 5180 1 12353 4 14047 1 11612 1 jeopardize 5498 1 16008 1 10131 1 9955 1 12852 1 11602 1 cyprinid 16314 1 11259 1 2089 1 4698 1 shotting 885 1 3396 1 7501 1 15294 1 2760 1 14010 1 14650 1 11084 1 1924 2 5439 1 2577 1 allah 2379 1 take_counsel 12424 1 6241 1 7580 1 bedrock 7473 1 3436 1 12584 1 5505 1 golden_aster 6442 2 1583 1 2436 1 ribbonfish 16078 1 2306 1 mustard_sauce 9162 1 16335 1 escarpment 1945 1 1410 1 884 1 2613 1 3134 1 3150 1 12705 1 6686 2 14305 1 11619 1 9108 1 2806 1 12359 1 3896 1 11799 1 tachometer 11633 1 15910 1 4933 1 1750 1 6279 1 9350 1 14271 1 dropped 6646 1 12229 2 7368 1 11691 1 6131 50 1392 1 6900 1 2698 1 4180 1 3097 1 7505 1 8881 2 5153 1 9696 1 3676 1 6527 1 9285 1 6400 1 16150 1 2440 1 9021 1 15403 1 5648 2 14461 1 767 1 10358 1 5271 1 11826 1 14583 1 5528 1 6145 1 11982 1 5272 1 13432 1 13434 1 1818 4 12368 1 12639 1 4659 1 1406 1 12123 1 12644 1 9833 1 7889 1 7142 1 8529 1 3258 1 3118 1 15277 1 9724 1 13695 1 11312 1 2577 1 13443 1 2732 1 2858 1 7898 1 15548 1 14220 1 10106 1 2583 1 6542 1 1290 1 1053 1 7260 1 13580 2 13451 1 540 2 14226 1 5554 1 3271 2 12139 1 927 1 8304 2 12524 1 14605 2 6796 2 13054 3 5187 2 1836 1 15170 1 12145 1 13189 1 12665 1 3703 1 12268 1 14483 1 6925 1 4925 1 15797 1 8663 1 7413 1 14352 1 10386 1 12792 1 12397 1 6439 1 9601 1 11327 1 13718 2 5423 1 12795 1 10129 1 9992 1 15301 1 8801 1 6178 1 5427 1 11733 1 7421 1 1576 1 9199 1 13209 1 6939 1 4933 1 12283 1 9477 1 10137 1 12031 1 12534 2 11742 1 14625 1 13214 1 4446 1 11339 2 16436 1 15940 1 12410 1 16065 4 14913 1 16438 1 565 1 1323 1 5063 1 1324 1 3158 2 7563 1 13982 1 4836 1 11612 1 11491 1 15818 1 11901 1 9882 2 14507 1 4332 1 9359 1 6337 1 6958 1 3165 1 12815 1 328 1 16079 2 9223 1 5075 1 11352 1 6830 2 3320 5 15201 1 14121 2 2490 1 10833 1 2263 3 10956 1 1461 1 11084 2 3483 1 2270 1 467 1 4957 1 7191 1 14266 1 13497 1 11764 1 3617 1 6596 1 7312 1 12569 1 14270 3 10701 1 964 1 2277 1 4464 1 4728 2 8213 1 1739 1 7585 1 12316 1 2912 1 15486 1 9775 1 9515 1 4852 1 12705 1 585 1 10846 1 13888 1 10848 1 1099 1 11780 1 3859 1 2404 2 2915 1 6230 1 484 1 16100 1 8110 1 5228 1 213 3 11928 1 10178 1 6849 1 14282 1 10977 1 2016 1 13897 1 16470 1 13383 1 10181 1 5865 1 1224 1 3350 1 4260 1 15230 1 2802 1 7210 1 8379 1 8728 1 3874 2 10727 1 3875 1 12853 1 10321 1 4159 1 1363 1 15235 1 10189 3 6241 1 6744 1 10195 1 14823 2 12596 1 13395 1 2306 2 9663 1 5745 1 1242 1 12727 1 13264 1 4029 1 7984 1 15611 1 1635 1 505 1 1370 1 1924 1 13915 1 15742 2 7218 2 15110 1 14555 2 5505 1 7992 1 10077 1 6629 1 13802 1 8739 1 3375 1 3377 1 3893 1 11677 1 15115 1 877 1 6123 2 2426 1 4627 1 1014 2 13921 1 8875 2 1136 1 9945 1 16494 3 5366 1 10755 1 15390 1 11686 1 12353 1 6894 1 3238 2 7878 1 loamy 9405 1 3436 2 11349 1 13674 1 8675 1 12968 1 4696 1 12534 1 12069 1 6692 1 953 1 americanize 16436 1 7465 1 12534 2 6801 2 10218 1 9665 2 dipteran 3644 1 8620 1 4920 1 9041 1 voluptuous 6198 1 543 1 11551 1 8565 1 6132 5 8237 1 14453 1 2598 1 8031 1 13823 1 8570 1 3424 1 13241 1 3501 1 1531 1 754 1 3032 1 6995 1 8517 2 2247 1 6574 1 10442 1 14528 1 9571 1 4901 1 15445 1 14933 1 4087 1 3059 1 palin 6241 1 10343 1 5403 1 2004 1 13064 1 signifier 10492 1 9679 2 11635 1 866 1 15950 2 10333 2 2083 1 11325 1 7155 5 14042 2 1053 1 10846 1 10992 1 5413 2 14665 2 1099 3 4087 1 2882 1 15877 1 1952 1 gastric_juice 12943 1 9807 1 10782 1 12150 1 13973 1 2012 3 4260 1 3744 1 15460 1 6318 1 4343 1 11327 1 833 1 1067 1 10195 1 11739 1 5681 1 10521 1 3223 1 6955 1 13665 1 7319 1 11080 1 12620 1 2025 1 pendulous 11431 1 3978 1 3095 1 10349 1 2699 1 7571 1 6133 28 2190 1 2436 1 15827 3 13985 1 14196 1 79 1 5776 1 4185 2 6708 1 4529 2 12822 1 8012 1 8702 3 5016 1 13681 1 13356 1 5779 1 15404 3 12171 1 1033 1 8343 1 4957 1 14065 1 1534 1 5271 1 3487 1 2904 1 13878 1 16087 1 15643 1 5849 1 1667 8 5783 1 7244 1 10094 2 15209 1 13567 1 2279 1 1599 1 906 1 3555 1 8894 5 5171 1 4293 1 16459 2 15840 1 10704 1 9635 2 16092 1 5173 1 12968 1 4143 1 10168 1 10169 3 12773 1 3501 1 6419 3 14076 1 4296 1 16400 1 7457 1 10176 1 16465 2 3629 1 8650 1 6727 4 7904 1 12518 1 11242 4 2921 2 3348 1 10983 1 5555 1 14149 1 1755 2 36 2 1756 1 10310 4 9115 1 9186 1 2300 1 12326 1 10055 1 4263 3 12847 2 12786 1 2598 1 988 1 11655 1 10727 1 5490 1 2662 1 15093 1 12666 1 293 1 2873 1 5295 1 3444 1 13531 1 14092 1 7057 1 7103 1 15297 1 13465 1 6442 2 9463 1 3447 1 3760 3 10326 1 9932 1 5680 1 14297 1 14954 1 447 1 6178 2 799 1 15181 1 6801 1 11474 1 7350 2 1576 1 4030 1 5135 2 3524 1 1314 1 872 1 13469 1 1709 1 11061 1 953 1 10746 1 14370 1 4829 1 10463 1 2321 1 7360 2 1445 3 15461 1 7687 1 14634 1 6635 1 6516 1 16439 1 10273 1 9216 1 14189 1 14972 1 intramuscular 10492 1 2581 1 14458 1 13297 1 531 1 9493 2 cryptically 5528 1 4680 1 6655 1 11973 1 goodeniaceae 14300 1 8529 1 married 10606 1 4640 7 7367 1 12229 1 252 1 5009 1 253 1 13552 1 1941 2 13674 2 4285 1 1395 1 14319 1 11819 1 2326 2 2190 1 7374 1 4527 1 15637 1 6134 77 13289 1 7239 1 8512 1 13930 2 4645 2 12361 1 8278 1 2331 1 2332 1 9955 1 9021 3 1807 1 13426 1 1148 2 3785 1 15136 1 16007 1 8517 5 3410 1 4649 1 897 2 8892 1 12634 1 5528 3 8758 1 267 1 14325 1 15 1 13171 5 6283 1 10625 1 9714 1 10492 1 905 1 13035 1 9833 1 8639 1 3116 1 4539 3 13176 1 14865 1 10639 1 7774 3 12246 1 12376 1 4200 1 11843 1 2734 1 16290 1 14082 1 2214 6 14738 1 12383 1 6916 1 15000 1 12257 3 1053 1 6545 1 6159 2 281 1 15784 1 8302 1 16182 1 12922 1 8184 1 12011 1 15790 1 7158 1 11728 1 13322 2 10795 1 8055 2 9856 1 3582 2 931 1 1176 1 16045 1 12270 1 7789 2 10800 1 674 1 14614 1 2083 2 1309 1 3952 1 1180 1 2231 1 16053 1 12795 2 15933 1 9603 1 446 1 9860 6 12796 1 7676 1 13965 1 7802 1 8805 1 7682 1 684 1 3295 4 4110 1 13971 1 16062 1 3153 1 14626 1 691 2 10005 1 2757 1 7807 1 9209 1 8325 1 10010 1 813 1 15311 2 2247 1 13604 1 8942 4 13736 1 2887 1 8811 1 10143 1 3605 1 5065 1 13342 1 11205 1 68 1 699 1 15034 1 13482 1 11612 1 4455 1 573 2 2385 1 4332 2 6200 2 6204 1 11212 2 12424 1 12818 1 8083 1 13089 1 15200 1 7695 1 5078 1 14650 1 2001 1 3483 2 5214 2 11626 1 4243 1 12563 1 3997 1 5458 2 5087 1 12569 1 5850 1 10701 1 4466 3 3329 3 15479 2 7081 3 15483 1 6351 4 4143 1 2009 1 5598 2 12707 1 14941 2 6720 1 7320 1 2509 3 14010 2 10564 3 4861 1 11109 3 12448 1 12068 2 13767 1 10978 4 15225 1 2797 1 12716 1 13516 1 2142 1 9920 1 14532 1 12586 1 9390 1 5123 1 1905 1 4616 1 8237 1 12848 1 5613 1 2658 1 15503 1 9116 2 3354 1 9394 1 3070 2 5491 1 10318 1 9926 1 1498 1 10868 1 7729 5 13260 2 7344 2 12995 2 13790 1 15236 1 11406 1 5498 1 13648 1 5745 1 5628 1 997 1 3520 3 5134 1 8128 1 5995 1 6376 1 1635 1 1246 1 11951 1 12341 1 13798 1 13538 2 11676 1 9937 6 1642 1 131 1 10745 1 3659 1 1014 1 879 1 7230 1 16491 1 10465 1 141 1 16494 4 2694 1 11282 1 12102 4 9552 1 13666 3 4175 1 1656 1 12228 1 accurately 6011 1 15818 1 9527 2 7366 1 7264 1 12229 1 356 1 3636 1 11587 1 10912 1 7403 1 4093 1 16117 1 6988 1 2301 2 7505 2 11147 1 7661 1 6081 1 6135 16 8571 1 3874 3 8857 1 12206 1 4129 1 12207 1 3951 1 9429 1 3138 1 1460 1 9021 2 4308 1 13785 1 5901 2 6498 1 2083 1 11662 1 12500 1 15104 1 8246 1 11405 1 2670 1 7135 1 996 1 4990 1 267 1 373 1 3036 1 4189 1 1735 1 1818 1 5427 1 10517 1 12828 1 906 1 9902 1 10265 1 4537 1 8529 1 5173 1 9719 1 14274 1 9478 1 14864 1 5100 1 10097 1 3223 1 4412 1 4502 1 134 1 11925 1 16436 1 4040 1 1828 1 6102 1 1014 1 6230 1 5279 1 1681 1 2214 1 5366 1 11111 2 3972 1 4707 1 14504 1 9552 1 2797 1 1389 1 7014 1 simeon 1072 1 leglike 7541 2 3918 1 10656 1 4335 1 11612 1 8802 2 12578 1 chemical_energy 14071 2 5729 1 344 2 14674 1 9219 1 14193 1 4323 1 9246 1 6792 1 9558 1 3609 1 drilling 4659 1 4933 1 10363 2 15254 1 4332 1 16018 1 13300 8 13367 1 7218 1 11859 1 7403 2 5505 1 13538 1 5950 1 3189 2 7145 1 2701 2 16253 2 10335 1 9500 2 6136 12 13583 1 6419 1 3084 1 1511 1 2855 1 16342 2 5739 2 10421 1 2263 2 8040 1 2266 1 6592 1 14716 2 1134 1 5228 1 9310 1 14811 1 15833 2 9838 1 13718 1 11381 1 14584 1 9380 1 11383 1 414 1 1773 1 2652 1 1328 1 7799 1 1357 3 10979 1 8801 1 13312 1 mortuary 8358 1 8975 1 10926 1 5481 1 1863 1 noah 9714 1 939 1 905 3 5516 1 3310 1 11901 1 8624 2 8463 1 3184 2 13417 1 8596 1 4155 1 3981 1 58 1 10997 2 14555 1 15023 1 12232 1 13801 2 14319 1 16268 1 2326 2 11743 2 6137 9 9457 1 8055 2 14525 1 13712 2 8106 1 9598 1 2426 1 9955 1 12726 1 13474 1 2646 1 13546 1 8991 1 8575 4 12254 1 13826 1 14838 1 8648 1 7362 1 15968 1 5528 1 16494 1 13648 1 9727 1 13877 1 15445 1 15034 5 3025 1 8128 1 10147 1 1656 2 11612 2 10361 1 7093 1 alex 4502 1 6330 1 2304 1 3952 1 fixate 10492 1 12410 1 6779 1 6713 2 8325 1 12097 1 6704 1 6423 1 4850 1 7353 1 7446 1 3862 2 8281 1 12383 1 14245 1 11907 1 3617 1 12102 1 2833 1 10952 1 10018 1 13412 1 2016 1 6138 5 11572 2 sign_in 13510 1 3136 1 15403 1 12561 1 8529 1 16355 1 1145 1 3393 1 11308 1 11406 1 11812 1 6080 1 2680 1 15444 1 11896 1 3152 1 10131 2 11925 1 bifocal 8059 1 15120 1 13913 1 irredenta 13228 1 10325 1 16174 1 9935 1 10144 1 boiling_point 15228 1 10946 1 3749 1 4944 1 7625 1 5443 1 9219 4 4155 1 12623 1 12553 5 15089 1 11861 1 15470 4 11461 1 12145 2 15636 1 1908 1 2873 1 6139 16 4158 1 10421 2 11189 1 15403 1 6215 1 13064 1 9989 1 13495 1 10926 1 14892 1 8609 2 13649 1 13263 1 2564 1 1916 1 6221 1 13434 1 5132 1 8997 2 6536 1 14956 2 4164 1 1074 1 3820 3 9477 4 6974 1 15880 1 13273 1 2881 1 3859 1 536 1 2041 2 11716 1 2945 13 11781 1 15419 1 14010 1 16203 1 5978 1 10340 2 5729 1 4083 2 3534 1 2824 1 163 2 7393 2 16259 1 5282 1 10018 1 6520 1 16143 1 4364 4 13814 1 13706 1 9920 1 decorousness nationalize 16151 1 12534 1 1279 1 187 1 9092 1 14199 1 11877 1 139 1 3064 1 10909 1 6835 1 1169 1 790 1 headphone 6710 1 12342 1 puck 4659 1 7582 1 12718 1 15500 2 10630 1 252 2 2802 4 7210 1 11268 1 8546 1 11309 2 8104 1 4446 1 13614 2 536 1 6123 2 15028 1 8391 1 14994 1 4998 1 13196 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4443 3 3701 1 5927 1 2570 1 7236 1 4143 1 2422 1 7158 1 3375 1 5029 1 6866 1 3873 1 5964 1 7440 1 13091 1 2560 1 14731 1 9747 1 6053 1 6604 8 3531 1 673 1 5648 1 9532 1 9705 1 2613 1 13736 1 5271 1 16494 1 6359 1 13048 1 9787 2 13381 1 4189 1 2846 1 10092 1 5426 1 12568 1 7314 1 14984 1 4087 1 11939 1 2006 1 11474 1 2110 1 lacy 3685 1 14957 2 4030 1 11008 1 3145 1 16459 1 6350 1 4961 1 10310 1 10136 1 1567 1 5556 1 12326 1 14212 2 4263 1 7210 1 1709 1 2436 1 14575 1 16225 1 16148 1 13583 1 6965 1 3875 1 6605 7 13681 1 1014 1 5455 1 12171 1 6480 1 3992 1 8472 1 7360 1 8147 2 3760 1 9945 1 10079 1 5745 1 15518 1 9092 1 12862 1 10094 1 11852 1 salmonella_enteritidis 15736 1 1736 1 2012 1 sprig 8796 1 777 1 autoecious 1047 1 3200 2 14974 2 9976 1 9308 1 9860 1 2300 1 2554 1 himalayish 6289 1 13648 2 15469 1 12415 1 10070 2 transverse_process 1351 1 14585 1 8822 1 13756 1 989 1 7872 1 gesticulating 4728 1 1370 1 13569 1 15459 1 11492 1 7762 1 12439 1 exceed 14051 1 252 2 3982 1 13525 1 1145 2 12300 2 8157 1 13674 1 2699 1 6168 1 15436 1 13745 1 10664 1 3067 1 14420 1 8202 1 7539 1 5964 1 826 1 16237 1 1873 2 10318 1 4529 1 6084 1 2048 1 10867 2 8058 1 11495 1 14462 1 6241 1 13065 1 16158 1 9400 1 16051 2 4102 1 10325 1 16483 1 4437 1 15058 2 14428 1 12569 1 15572 4 5531 3 15301 1 9665 2 14956 1 11701 1 1635 1 4164 1 2638 4 1279 1 11447 2 9716 1 4314 1 10558 2 1372 1 15486 1 6473 2 585 3 7894 2 14665 1 7006 1 11339 1 877 1 3531 1 5543 1 2945 2 8076 1 11999 1 6230 1 10304 1 5914 1 4169 1 7649 1 11784 1 16206 1 6606 18 15077 1 6760 1 5608 1 14015 1 4451 1 1448 1 12543 1 11849 1 7045 1 6263 1 14504 1 8779 2 16318 1 5957 2 noncompetitively 1615 1 8778 1 common_moonseed 5906 1 1583 1 7471 1 7904 1 3258 1 stopper 2638 1 12139 1 16182 1 7368 1 758 1 8132 1 9030 1 3524 1 2127 1 5246 1 15112 1 585 1 6238 1 3959 1 7 1 12590 1 7129 1 8513 1 11886 1 10318 1 5618 1 7716 1 9056 4 9078 1 2196 1 6770 1 3461 1 16438 2 4215 1 4169 1 13893 2 13718 1 15056 1 6607 8 2953 2 12102 1 7045 1 15875 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9107 1 1055 1 9976 1 2797 1 16345 2 1611 1 6160 1 1834 1 5181 1 13248 1 15557 1 1753 2 13581 1 9648 1 3201 1 6732 1 8596 1 13525 1 3204 2 1229 1 9393 1 1965 1 5041 1 11796 1 6238 1 13192 1 6737 1 432 1 10989 1 293 1 12790 1 7161 1 13712 1 2873 1 10253 2 5933 1 5295 1 6678 42 6372 1 1364 4 7789 1 8663 1 3139 1 5244 3 11327 1 15868 1 9061 1 6311 1 935 1 5047 2 10325 1 2365 2 13648 3 1068 1 3816 1 13791 2 14892 1 12020 1 12023 1 4495 1 2036 1 9665 1 1635 1 8438 1 8998 1 1246 1 12218 1 9609 1 3820 2 15023 1 11675 1 2419 1 7989 1 7061 1 2940 1 3376 1 5888 1 7226 1 14911 2 6123 1 15310 2 13148 1 15886 1 10339 1 4041 1 8325 1 13077 1 4700 1 13011 1 15312 4 14251 1 14105 1 7120 1 7012 2 14969 1 7296 1 8081 1 4707 1 8817 1 15034 2 14188 1 6192 1 7434 1 1656 7 14047 1 13482 1 11612 1 16144 1 petrograd 12302 1 10825 1 11011 1 5561 1 12781 1 13079 1 6801 1 hipbone 15940 3 9812 1 3136 1 14077 1 10464 3 9550 2 14351 2 6679 8 12733 1 12538 1 7344 1 2802 1 13736 1 12815 1 9924 1 4143 1 7505 1 11413 1 13836 1 12308 1 10268 1 6 1 13272 1 10131 2 7474 1 11474 1 1131 1 chastity 9279 2 10492 1 4256 2 4558 1 9702 1 4640 1 7230 1 11084 2 6680 7 8517 3 9601 1 8156 1 10441 1 4156 1 15845 1 9903 1 9886 1 11147 1 4222 1 8100 1 9861 1 7537 1 9270 1 3582 1 14540 1 5995 1 9629 1 1176 1 flowering 11008 1 1659 1 8879 1 9818 1 13419 2 15757 3 254 2 4179 2 2554 1 3392 1 13821 2 5 1 12884 1 11292 1 2436 5 14712 1 9284 1 1943 1 14196 1 5379 1 8012 4 3404 2 8886 1 5779 1 15404 3 8013 1 15267 7 9704 1 10226 1 6278 1 12499 1 12502 1 8021 2 1667 2 11017 1 6146 1 13566 1 10094 1 2970 2 5906 5 12643 2 8171 2 3416 1 11709 1 6032 1 5276 1 5173 3 3258 5 16394 1 14212 2 5396 1 4906 1 9969 1 409 1 9172 1 2578 1 1047 2 11574 3 10103 2 7900 1 10107 3 5033 1 12383 1 1560 4 159 1 9587 1 7904 2 15920 1 6430 1 13581 1 3436 1 5555 2 8047 1 9186 2 14606 2 11859 1 5556 2 14607 1 12011 1 14087 1 12786 2 7158 1 2865 3 11728 1 4678 1 293 4 13712 6 6306 1 10253 1 10668 1 14351 1 2999 1 16417 1 13462 1 2746 1 13714 2 12931 1 6681 83 6442 2 10128 3 5426 1 7798 1 9860 2 798 1 8802 1 6801 3 11474 2 15675 1 4933 5 13726 1 6941 2 13967 1 11331 1 13968 2 13469 1 62 1 14365 1 2757 1 14370 1 1982 1 4829 1 13216 2 15940 1 3301 1 9210 1 3830 1 12414 1 1583 1 10941 3 4232 1 7435 1 9216 1 12546 1 9880 1 5442 1 11614 4 4711 1 4332 1 1457 1 15037 1 4238 1 16321 1 6586 2 13086 1 7694 1 4951 1 15323 2 11619 1 7574 2 7824 1 10418 2 6965 3 1999 1 5452 1 12822 3 8458 3 5455 6 5083 1 3324 1 834 1 10286 1 13878 1 14932 2 8973 1 1599 1 5329 1 10965 1 8977 1 10704 1 11636 1 9635 4 4730 2 12181 1 3189 1 2131 1 717 1 14137 1 6600 2 7711 1 6601 1 4143 1 10168 1 5471 1 14002 3 208 1 7321 1 7085 2 481 1 6977 4 4971 1 10566 3 3744 3 1893 3 7091 1 6728 4 2797 1 15082 1 13768 1 2921 2 12717 1 3348 1 3200 1 1487 1 10983 1 1755 3 10310 1 6985 1 1613 1 4155 1 4263 3 10185 1 4490 1 7471 3 5347 2 5126 1 3354 4 1907 1 7473 1 5490 2 3875 2 4870 4 611 1 6369 2 5618 1 11944 1 4491 4 14159 3 6498 2 1624 1 11661 3 227 3 15872 1 3760 11 12087 1 999 1 7485 1 7486 1 9665 1 7350 3 4623 1 15241 2 14173 6 2819 1 12738 1 11541 1 11133 1 3220 1 4031 1 2423 1 14175 1 3223 1 9673 2 13806 1 8259 1 1510 1 11414 4 5361 1 510 1 1932 1 11956 2 9680 1 14563 1 9681 1 16492 1 7360 2 8875 1 7744 2 6890 3 2951 1 14042 1 6516 3 9945 1 15753 1 11964 1 14047 3 kingbird 1576 2 9079 1 5171 1 5733 1 5555 2 15329 1 9922 1 12981 1 9635 2 5718 1 4661 2 4179 1 2563 2 4352 2 4263 1 12968 1 16314 1 4143 1 1583 1 4678 1 7922 1 13583 1 4185 2 12577 1 kyphosis 1662 1 8369 1 8739 1 12848 1 grudge 4158 1 7256 1 1635 1 9509 3 9830 2 10657 1 15403 2 2136 1 278 1 13968 3 9525 1 6682 8 9035 1 8161 1 5614 1 9587 2 2598 1 6081 1 14776 1 2749 1 5813 1 7545 2 598 2 4502 2 8645 1 1604 3 6793 1 4865 1 character_printer 5841 1 15630 1 8287 1 14015 2 11895 1 519 1 12534 1 9698 1 7614 1 13888 1 9107 1 5291 1 5245 1 16318 1 overlap 15228 1 7068 1 2385 1 2989 1 15665 1 7658 1 5124 1 15230 1 7692 1 15759 1 5188 1 1301 1 3320 1 13930 1 2960 1 2027 2 3136 1 5494 1 13531 1 16008 1 11558 1 6683 15 8246 1 11406 1 10033 1 9860 2 13994 1 10628 1 15707 2 10556 2 16019 2 3926 1 15614 1 10774 1 718 1 11166 1 4272 1 4734 2 970 1 13273 1 5361 1 877 1 3086 1 4040 1 5602 1 6230 1 1014 1 14009 1 10751 3 5228 1 15423 1 1137 1 6849 1 10108 1 1860 1 2408 1 13814 2 forthwith 5739 1 12529 1 1201 1 13765 2 9021 1 573 1 3992 1 15301 1 9094 1 66 1 8381 1 3766 1 metamorphic_rock 5442 1 16180 1 7985 2 7015 2 16278 2 10083 1 6704 1 5718 1 6598 1 11349 2 5505 2 12326 1 3795 1 8067 1 7370 1 2131 1 10561 2 7473 4 13583 1 14911 1 6419 1 2873 2 12270 1 7117 1 12192 1 14010 1 3992 1 4169 2 2778 1 10442 1 10513 1 11946 1 3486 1 4830 1 1934 1 6684 12 7120 4 7393 1 6535 1 13951 1 12620 1 feudal_system 7287 1 steam_room 4529 1 13952 1 1331 1 2903 1 7739 2 13767 1 2270 1 16074 1 5608 1 alligatorfish hazel 9634 1 10321 1 3937 1 12326 1 5173 1 1104 1 10358 1 13086 1 4678 1 4994 3 4295 1 2873 1 sheet_metal 8726 1 11388 1 1722 1 14086 1 15692 1 6704 1 4877 1 1437 1 4350 1 10559 1 12815 1 4143 1 13273 1 8469 1 1604 1 11493 1 15342 1 15028 1 7256 1 16286 1 14855 1 3404 1 9521 1 5295 1 6713 2 2646 1 11002 1 7257 1 6592 1 2857 2 10751 1 4531 1 8980 1 7344 1 8281 1 13736 1 9062 1 7134 1 1957 1 2902 1 7120 2 5035 1 11259 1 15933 2 11685 1 6687 9 5634 1 8997 1 16318 1 11474 1 tonal_system 12522 2 3084 2 14018 2 garden_centipede 5988 1 13515 1 510 1 9982 1 parcelling 2308 1 3617 1 4203 1 2214 1 5240 1 12383 1 alkalinity 15228 1 9755 1 10133 1 9029 1 4714 1 10816 1 3258 1 7017 1 1836 1 5346 1 2598 1 97 1 4951 1 2881 1 5079 1 9415 1 13859 1 1203 1 16438 2 438 1 4169 2 11403 1 1893 1 6848 1 14251 1 15251 1 12416 1 5694 1 3435 1 12519 1 8408 1 9606 1 1389 1 3347 1 12964 1 henbit 3760 1 11956 1 5439 1 423 1 scarf 7598 1 2618 1 11119 1 5184 1 9219 1 1026 1 15860 1 1058 1 6339 1 12815 2 5318 1 8119 1 7237 1 13461 1 9890 1 3875 1 10617 1 5989 1 2466 1 5158 1 9155 1 15328 1 10421 1 8827 1 4957 1 14160 1 5384 1 16329 2 8517 2 16008 1 933 2 14755 4 7344 1 13201 2 4188 1 11630 1 14201 1 15875 1 6375 4 6688 20 3732 1 4659 1 14957 1 8214 1 1344 1 6502 2 4961 1 3333 1 15023 1 13799 1 3958 1 4443 1 7769 1 4143 3 806 1 9168 1 3375 2 15307 1 12131 1 11572 1 8390 1 3626 1 7774 1 15686 1 7257 1 6887 1 11646 1 11004 1 7009 1 6727 1 8147 1 6543 1 15968 1 5366 1 11684 1 7120 1 5232 1 5694 1 5180 1 3536 1 6580 1 15084 1 5112 1 6894 2 6644 1 organic_phenomenon 8460 1 1180 1 10247 1 16028 1 8512 2 4361 1 14616 2 1260 1 13674 1 gaius 15759 1 13801 1 7012 1 15919 1 9243 1 11924 1 884 1 3638 1 14527 1 2826 1 qe2 15968 2 4199 1 15579 1 9704 1 2682 1 2581 1 14163 1 max 1635 1 12726 1 8529 1 junction 5442 1 10757 2 14639 1 3473 1 8822 1 6267 1 13821 1 12956 1 15470 3 2436 1 7186 1 5153 1 6 1 6527 1 3676 1 1943 3 10418 1 5584 1 5965 1 14121 1 3322 1 7946 1 14980 1 6713 1 15404 1 4897 1 1877 1 16329 1 4720 1 15056 1 10840 1 6594 1 11092 1 16162 1 10094 1 8408 1 1736 1 4464 2 473 2 6226 1 16225 2 2452 1 8220 1 4907 2 12245 1 11312 4 8292 1 10968 1 2012 1 2857 1 9912 1 2582 1 1894 2 11380 2 8180 1 2584 2 15079 1 16175 1 9728 1 7260 1 13895 1 15920 1 7393 1 13516 1 13453 5 7720 1 12718 1 9849 1 2739 1 15788 1 7337 1 9451 1 7535 1 12848 2 7210 2 8787 1 1568 4 8657 1 6924 1 1964 1 5347 2 10119 1 10664 1 8309 1 6736 1 9656 1 3874 1 13461 1 3580 1 5616 4 13063 1 14885 1 3581 1 14157 1 7856 1 2928 1 4680 2 3138 1 15362 1 7729 1 12668 1 9532 1 6497 2 15736 1 6310 1 12995 1 3760 1 16422 1 4990 1 2811 1 14954 1 6244 1 1367 1 8997 1 2815 1 7058 1 7169 2 12600 1 14956 1 9996 1 4623 2 505 1 6059 1 6502 1 3145 2 3218 1 6689 34 10265 1 15576 2 15378 2 15744 1 62 1 2422 1 10520 2 807 1 6509 1 2822 1 4382 1 15244 1 10464 2 8076 1 13973 1 7230 1 9209 2 11275 1 4227 1 8559 1 7009 2 13736 4 7872 1 1136 1 7500 1 16494 1 5257 1 10408 1 10755 1 7120 1 1935 4 5367 2 14505 1 14047 1 12620 1 883 1 2826 2 suborn 4889 1 8826 1 1464 1 11812 1 9937 1 15479 2 12249 1 11137 1 10153 2 8026 1 13280 1 8134 1 13307 1 833 1 16117 1 5365 1 lamentation 5442 1 8091 1 10342 1 1014 1 15419 1 14507 1 16025 1 15034 1 468 1 11917 1 4384 1 1072 1 11668 1 5913 1 14527 1 8805 1 genoese 8272 1 9420 1 7456 1 13606 1 12848 1 nearsighted demoralized 5323 1 3741 1 6844 1 9819 1 2360 1 13150 1 8026 1 16312 1 7649 1 13428 1 12382 1 7730 1 13490 1 9189 1 15933 1 4707 1 950 1 15225 1 2445 1 zirconium 10253 1 2253 2 14774 2 1088 3 3992 2 1334 1 5742 2 9309 1 7559 2 11811 1 10442 1 11327 1 11069 2 768 1 15198 1 8021 1 4185 2 3859 2 loch_ness 15200 1 6032 1 3555 1 6952 1 11612 1 6946 1 undulate 7421 2 11670 1 3555 1 5896 1 7657 1 1457 2 13967 1 11878 1 12326 1 4352 1 15911 1 7354 1 11541 1 4263 1 6690 8 718 1 4143 1 4696 1 6020 1 2045 1 3320 1 11778 1 13582 2 8400 1 2836 1 3501 1 2466 1 7743 1 7410 1 1788 1 10465 1 16329 1 16258 1 8500 1 7010 1 1957 1 6328 1 3916 1 5500 1 8449 1 2306 1 6596 1 6070 1 6535 1 3366 2 13518 1 15836 1 3571 1 11427 1 11612 1 7436 1 8581 1 muggy 12761 1 12605 1 14323 2 15994 1 7657 1 179 1 strewing 16008 1 1014 1 4584 1 2583 1 rio_grande 12284 3 3980 1 9186 1 3164 1 7288 4 13672 2 13821 1 6289 1 14990 1 5347 1 6691 12 1006 1 11619 2 6754 1 6830 1 14155 1 13063 1 293 4 4829 2 13784 1 13700 1 7457 2 5228 7 13718 2 7091 5 14584 2 159 1 6686 1 1773 1 15920 1 13312 1 cathay 4529 1 2426 1 10286 1 7012 1 7605 1 8040 1 15574 1 7528 1 brig 15108 1 15119 1 10846 1 4412 1 10577 1 steamer 9639 1 152 1 14106 1 719 1 3118 1 6735 1 306 1 6279 1 2879 1 233 1 feodor_dostoevski 3816 1 coral 7015 1 4393 1 12109 2 1392 1 13821 1 16146 3 13674 2 2436 1 14712 2 3676 1 14196 2 3400 1 11437 1 5644 2 6656 1 4529 1 5016 1 15404 1 15267 2 12633 1 10900 1 8890 1 4648 1 13935 4 2052 1 5651 1 1667 1 10094 2 11021 1 14985 2 6778 1 12123 1 6661 1 7142 1 5173 1 3258 16 7032 2 10632 1 14212 1 1042 5 13836 1 7251 1 11996 1 409 1 13302 1 4542 2 14997 1 1560 2 3130 1 7904 2 9841 1 1685 2 13706 1 8425 1 421 1 3436 1 12138 1 12263 1 11859 1 1691 4 5927 1 12786 1 2224 1 6050 1 4812 11 5041 1 13583 1 293 1 2873 2 8057 1 6927 1 5295 1 5418 1 1698 1 14616 1 14235 1 13718 1 5422 1 15297 1 2089 3 13206 3 11474 3 10678 1 7421 3 13967 1 10399 1 5950 1 8675 2 6449 1 12287 1 4696 1 11882 1 10681 1 16061 1 1441 1 6692 68 2757 1 2610 1 14911 1 953 1 9206 2 4698 1 4829 2 13867 1 9210 1 16314 2 9876 2 2106 2 16440 1 698 1 12546 2 11612 2 3310 1 4457 1 11349 1 7938 1 5837 1 14510 1 13086 2 2628 1 7442 1 12822 1 10698 1 15830 1 6342 3 8343 1 3992 1 11496 2 16329 2 3487 1 10286 2 13878 1 12826 1 10701 2 4468 1 15211 1 5331 1 10965 2 10704 3 15840 1 9635 3 7452 1 14936 1 12968 1 12705 2 2641 1 11228 1 14525 1 16228 1 6977 3 11233 1 15219 2 5228 1 16465 1 1893 1 6727 1 7846 1 3866 2 7333 1 12841 1 13769 1 9255 1 3200 4 4017 1 5733 3 16350 2 3750 1 13900 1 12586 1 9115 2 11390 1 16114 1 4983 2 4487 1 12326 1 4263 1 12847 1 7471 2 3065 1 4742 1 11941 4 6238 1 11525 1 14421 1 4986 1 9119 1 10321 1 225 1 14424 1 11660 3 6498 7 1770 2 7344 1 15872 3 1238 1 2306 4 11530 1 11949 1 2671 1 7485 1 5134 1 12862 1 7350 2 14956 1 9540 1 12731 1 505 15 9405 9 4030 1 14173 1 10742 1 7862 1 1925 2 5505 2 11413 2 2423 2 1006 1 7224 1 10335 1 10743 1 5754 1 7868 3 3764 1 8259 1 10746 1 877 1 11956 1 10597 1 12744 1 7360 1 15249 1 9945 2 9950 1 3235 3 145 1 12353 3 11964 1 14189 1 13667 1 prime_mover 16494 1 13188 1 45 1 2159 1 14527 1 14650 1 1328 1 fallow_deer thalidomide 5442 1 5443 1 16182 1 9097 1 758 1 1370 1 9883 1 11691 1 4335 2 7031 1 4588 1 1300 1 6020 1 7239 1 10543 1 1662 1 8645 1 3173 1 14405 1 13576 2 877 1 5240 1 7541 1 15439 1 3913 1 15119 1 2857 1 5000 1 12895 1 1534 1 13283 1 6728 1 15739 1 8330 1 16331 1 2159 1 9920 1 orgiastic 16035 1 3396 1 11068 2 7712 1 14319 1 3329 1 9079 1 7012 3 7570 1 5221 1 10499 1 9243 1 14186 2 8517 1 4309 1 5105 1 14219 2 3101 1 12004 2 6693 5 dormouse 6442 1 11956 1 7574 1 2921 1 6287 1 293 1 4829 1 taint 6973 1 1291 1 10834 1 13070 1 7275 1 6830 1 10411 1 11987 1 14270 1 7441 1 coelenterate_genus 6692 1 maze 12718 1 9882 2 15459 1 5187 2 9886 1 8180 1 1642 1 8983 1 607 1 9478 2 12795 1 1779 1 284 1 auvergne 2613 1 6686 1 5347 1 13035 1 13821 1 workshop 800 1 8292 1 14438 1 5883 1 5785 1 6102 1 6694 8 8980 1 11674 1 1616 2 9784 1 15477 1 11147 1 126 1 7537 1 8408 1 15210 1 1656 1 11474 1 4464 1 5182 1 columba 7885 3 86 1 12812 3 harden 14226 2 15499 2 14742 1 13484 2 5984 1 5209 1 4016 1 13669 1 15692 3 11586 1 6704 1 15431 1 9219 1 6017 1 5836 1 5412 1 6552 1 1836 1 15636 2 4951 1 4128 1 14920 1 7824 1 1397 1 11187 1 6109 1 2836 1 15954 1 6655 1 15093 1 5240 1 5295 1 14980 2 15865 2 4531 1 15267 2 16008 1 2229 1 5742 1 1621 2 11946 1 1534 1 15056 1 1180 1 13497 1 1428 1 768 1 12566 2 11017 1 4220 1 2335 1 7025 2 10962 2 11771 1 14956 3 1635 1 7028 1 4933 1 3793 1 16487 1 12866 1 9833 1 7833 1 15453 1 2419 1 16092 1 13834 1 8675 1 8613 1 13112 1 6629 1 533 1 7740 1 14002 1 3223 1 4035 1 877 1 2067 1 14909 1 536 1 10172 1 9305 1 8716 1 9680 1 10597 1 15918 1 918 1 13118 1 6357 1 565 1 6695 21 1892 1 14838 1 5033 1 12809 1 16344 1 9008 1 13379 1 13013 1 7904 2 3508 1 2953 1 10273 1 7393 1 1357 2 9448 1 16143 1 3699 1 6983 1 6894 1 logical_relation 1949 1 15479 1 12643 1 5901 1 13965 2 15795 1 secured 15581 1 820 2 573 1 7368 2 14052 1 5444 2 6336 1 148 1 12815 1 9624 1 13422 1 7505 1 14319 1 3477 4 6828 1 15898 1 3986 4 13745 1 5700 1 6830 1 7695 2 8340 1 4129 3 13091 1 7441 1 9629 1 6656 1 5158 2 10281 1 14855 1 5586 1 12822 1 14263 1 2263 1 5648 1 3992 2 3483 2 16008 2 8631 1 1090 2 7134 2 14267 2 2902 1 6716 2 7764 1 14658 1 2201 1 12569 2 4464 1 6835 2 4659 2 7582 1 4466 1 1737 1 8975 1 15273 1 8214 1 1344 1 2127 1 12439 1 7893 1 10561 1 4471 1 7034 1 585 1 8288 1 646 1 7587 1 4540 1 9377 1 14665 1 970 7 14940 2 14005 1 11230 1 481 1 1552 1 9440 2 10101 1 10044 1 3196 1 11928 1 15077 1 15423 5 5860 1 2406 1 12978 1 11321 1 4425 1 284 2 15924 1 3348 1 8726 1 5184 4 3350 1 14675 2 16112 2 12325 1 12658 2 5924 1 6298 2 2655 1 15561 1 1114 1 4616 1 14609 1 1300 1 7537 4 8730 1 7787 1 10989 1 931 1 611 1 7541 1 5295 1 4213 1 9121 1 16479 1 2361 1 7413 1 16047 2 11662 1 11406 2 14892 1 15017 1 8800 1 7799 1 10131 2 12215 1 13002 1 12027 2 6059 1 557 1 3592 1 6502 1 11330 1 1925 1 15025 2 9937 1 623 1 10998 1 877 1 3531 1 12537 1 4040 2 3156 1 16491 1 11680 1 5062 3 4939 1 2613 1 6697 40 5314 1 1192 2 16494 2 6328 2 5512 1 15579 1 16441 4 456 1 2487 1 12686 1 1656 1 senior 602 1 6011 1 7069 1 7367 1 6200 3 12524 4 7879 2 15926 1 7851 1 13552 1 4093 1 10581 1 4524 1 8881 5 11147 6 14644 1 6830 1 8203 1 6652 1 15564 1 2927 2 576 1 223 1 10922 1 12427 1 15797 1 10619 1 5621 1 13093 1 14614 1 8517 1 6498 3 3997 1 13935 1 13749 1 5271 1 832 1 4928 1 10325 1 11830 1 15933 1 267 1 231 1 1667 1 16484 1 126 1 7168 1 5356 1 13171 1 2815 1 4029 7 15674 1 9665 1 15450 1 13883 1 2447 1 2906 1 10363 1 14591 1 5534 1 15023 1 1282 1 5570 1 9203 1 13269 1 9937 1 1642 1 8031 1 4110 1 14905 1 8615 1 11742 3 8323 1 9546 1 3153 1 408 1 723 2 13241 1 3766 1 4860 1 10497 1 3564 1 6230 1 7527 1 13698 1 657 1 6696 26 14104 1 13606 1 16177 1 6105 1 7563 2 12543 1 12488 3 2797 1 3025 1 11584 4 7067 1 filmmaker 4391 1 11517 1 7985 2 351 1 5671 1 7398 2 4004 1 11709 1 9186 1 4499 2 10433 1 6204 1 5536 1 10539 1 4950 1 717 1 12816 1 4008 1 6035 1 8070 1 16283 1 13583 1 2661 1 13229 1 10337 1 13041 1 14993 1 12726 1 6102 3 8108 3 7729 18 2535 1 13260 2 13079 2 10972 1 12448 1 12995 5 6698 33 15077 1 5271 1 14738 1 4912 1 1169 1 16494 2 8845 2 9728 1 3816 2 14018 1 1735 3 2394 1 11666 2 4844 1 6801 2 5273 1 15371 1 bibliotics 314 1 axil 7568 1 10628 1 16459 1 3416 1 7452 1 9186 1 9115 2 13968 1 5556 1 2144 1 12326 1 10023 2 14212 4 12786 1 7992 1 12968 1 4143 1 3578 1 5910 1 16225 1 988 1 14196 1 64 1 15472 1 7007 1 14911 1 4829 1 7410 1 8012 3 8771 1 3444 1 1014 1 15404 1 5455 1 9291 1 5104 1 67 1 6699 6 15753 2 11699 1 6820 2 15573 1 13768 1 11474 1 7350 1 8373 1 disconcertingly 1580 1 7191 1 3281 1 2697 1 2849 1 11572 1 5307 1 woody_plant 5442 1 3012 1 10704 1 14606 1 4487 1 6287 1 13821 1 4031 1 1799 1 12395 1 2436 1 15846 1 3676 1 3653 1 3376 1 11777 1 4318 1 14196 1 9206 1 6419 1 9811 1 13443 1 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5616 1 4986 1 3875 1 11798 1 15093 3 13645 1 11400 1 3072 5 13908 1 2152 4 6498 1 7344 1 1624 3 3760 2 1242 1 9536 1 13649 1 14168 1 9403 1 15877 1 5502 1 12601 1 15986 1 7350 1 869 2 6501 2 8252 1 15987 1 12732 1 2164 1 13652 1 8612 1 10742 1 5999 1 7863 1 14557 1 9542 1 15113 2 10077 1 14693 1 3890 1 15880 1 12477 1 9413 1 3223 1 9138 3 7868 1 8140 1 5888 1 6510 1 13545 1 2945 1 1513 1 2042 2 16491 1 1257 1 1136 1 9945 1 13013 1 11684 1 11424 1 5140 1 14189 1 11427 1 3238 1 6894 1 3671 1 locomotor 9755 1 16056 1 13682 1 3556 1 12342 1 15230 1 6862 1 1708 1 469 1 14616 1 16405 1 14147 1 2598 1 14954 1 13099 1 9226 1 6428 1 1943 1 11061 1 6367 1 4446 1 8802 1 9207 1 jerking 7541 1 4529 1 3085 1 12718 1 5778 1 8242 1 9297 1 4238 1 13024 1 9989 1 10503 1 14293 1 6499 2 12416 1 11872 1 12338 1 13937 1 5669 1 choose 5261 1 6011 2 573 1 10895 1 4774 1 8624 3 1660 1 2114 1 1145 1 6825 1 12556 1 328 1 14318 1 10086 1 12041 2 7505 1 11147 2 2326 5 7374 1 3317 2 15637 2 2624 1 11619 2 10543 1 12043 2 1663 4 6274 1 15953 1 4129 1 5380 1 13168 1 4529 3 15764 1 2332 1 9021 1 10619 2 4840 1 1807 1 3992 1 13293 1 12563 2 14928 1 15590 2 7132 1 11826 1 15959 1 9506 1 4064 1 13430 1 14201 1 4001 4 14658 1 13171 1 12569 1 3852 1 10492 4 905 2 12643 1 12123 2 12241 1 3113 1 5172 1 4140 1 5718 1 8834 1 16019 1 13438 1 8897 1 9719 1 4193 1 7712 1 9036 1 15216 4 9518 1 10845 1 13371 1 13575 1 11309 1 15339 2 1549 1 14666 1 14141 1 10043 2 15916 1 11312 1 8769 1 12061 1 8038 2 7390 1 8840 1 3049 1 2509 2 8716 2 6230 2 211 1 7648 1 6788 1 16290 1 7899 1 10440 1 2581 1 11846 1 12581 1 8844 2 1749 7 1053 2 10977 2 4422 2 2589 1 14018 1 8476 1 1109 1 7464 3 4365 2 11853 1 9920 1 7969 2 9527 1 13053 1 8916 1 12586 1 6732 47 12393 1 10247 2 5412 1 2526 2 12848 1 16410 3 15170 2 14153 2 6168 1 12721 1 16234 1 13063 1 14538 4 6991 2 8055 1 3280 1 13712 1 5240 1 10866 1 1498 1 741 1 10867 1 4925 5 15294 1 6678 1 8991 2 2081 1 1501 1 6241 1 5244 1 1309 1 9398 1 3815 1 13648 1 2366 1 9663 1 10392 1 16484 1 126 3 2089 2 3646 1 5245 1 15301 1 12727 1 8128 1 15877 1 748 1 1635 1 939 2 2371 2 14096 3 12868 1 12342 2 14555 2 10934 1 11059 1 7002 1 15935 1 3150 1 62 2 4696 1 2241 2 3082 1 7061 2 12740 1 3375 1 15114 1 14626 1 8192 1 3019 1 7006 1 4114 1 5573 1 5888 1 510 1 12033 2 10269 1 3531 2 13546 4 3897 1 6886 1 879 1 15459 1 13076 2 15248 1 12540 1 2483 1 2247 1 6890 1 13660 3 1789 1 13280 1 1719 1 1136 1 1519 1 16439 1 8814 1 14570 1 15191 1 5830 1 14044 1 14504 1 15034 3 144 1 12352 1 6071 1 6331 2 9421 2 189 1 4891 1 11347 2 stochastic_process 10698 2 585 1 4990 1 14010 1 9377 1 6962 2 14770 1 3504 1 6367 1 9717 1 12228 2 wren 11008 1 16350 1 3636 5 6733 18 2830 1 14533 1 12326 1 6302 1 5347 1 4678 1 1909 3 12666 1 13712 1 1588 2 8702 1 3444 1 10956 1 7166 1 11217 1 3487 1 3814 1 10325 1 11406 1 13878 2 11597 1 14297 2 8021 1 5849 1 8435 1 15181 2 6801 4 12731 1 12508 1 12676 1 8529 1 3189 1 9940 1 1006 1 1509 1 5054 1 1889 1 12186 1 5361 1 12578 1 9639 1 14628 1 625 1 7457 2 13660 2 13606 1 12159 1 12617 1 14251 1 5513 1 284 1 13312 1 shimmering 7421 1 2151 1 11123 1 15862 1 7769 1 7667 1 head_cold 12795 1 349 1 8309 1 9707 1 southerner 8458 1 10363 1 6230 1 4840 1 5648 1 10157 1 13259 1 8954 1 15023 1 15305 1 6981 1 6734 5 12848 1 13440 1 14728 1 11785 1 4706 2 13490 1 13583 1 9420 1 10491 1 4829 1 yangtze 2227 1 6289 1 14550 1 14059 1 9032 1 14797 1 15574 1 10070 1 perfect_tense 14649 1 9679 1 3776 1 14929 1 auspiciously 7021 1 440 1 126 1 2490 1 now_and_again 7420 1 10363 1 15797 1 2263 1 10509 1 7452 1 7990 1 3954 1 3617 1 2623 1 4511 1 6019 1 4546 1 10087 1 3320 1 7958 1 7175 2 1656 1 10233 1 repress 13492 1 9154 1 8831 1 11330 1 3367 1 13797 1 13011 1 15364 1 14557 1 531 1 1136 1 1169 1 5686 1 12932 1 12818 1 2865 1 6989 1 5766 1 1703 1 12206 1 13965 1 2159 1 diaper 2478 1 2646 1 12732 1 4497 1 758 1 6226 1 5446 1 2393 1 2902 1 13029 1 14117 1 12795 1 4412 1 4220 1 4446 1 schedule 6011 1 5443 1 9819 1 758 1 12231 5 14114 2 15318 1 3242 1 1084 1 5581 1 12556 1 3393 1 12041 1 13674 1 12884 1 6080 1 5583 2 14057 1 15528 1 2045 2 6830 1 8203 1 8512 1 7071 1 7304 1 3174 1 1201 1 2331 1 14514 1 9021 2 6713 1 15831 1 14650 1 7189 1 3997 1 15833 1 1812 1 14720 1 152 5 2902 1 12899 1 13684 1 11559 1 10771 1 14658 1 13938 1 9830 1 8093 1 13034 1 7451 1 3685 1 7450 1 3734 1 13626 1 8582 2 7585 1 5654 1 6601 1 12575 1 719 2 7587 1 4907 1 16227 1 12971 1 4200 1 11033 1 5278 1 2135 1 5978 1 7392 1 9583 1 5608 1 1957 2 15968 1 14015 1 9587 1 12260 2 15720 1 16178 1 7530 1 7776 1 11788 2 6853 1 14747 1 10453 1 16472 1 10860 1 1568 1 6167 1 11461 1 12145 2 9116 2 13780 1 15729 2 12787 1 8309 1 6735 28 608 1 739 1 15564 1 12724 1 5873 1 4211 1 2927 1 11188 1 11462 1 15864 1 12466 1 7476 1 866 1 7607 1 12856 2 9662 1 8246 1 6685 1 15570 2 7345 1 15933 1 14892 1 4930 1 14429 1 126 1 8436 1 12998 2 12214 1 15875 1 5427 1 2036 3 14033 1 3145 1 1637 1 13968 1 2419 1 7864 1 9203 1 450 1 12534 1 6248 1 1642 2 13214 3 689 1 3718 1 11336 1 306 1 5688 1 5888 1 2686 1 7496 1 8682 1 1014 1 2547 1 9872 1 2483 1 14838 1 6891 1 16141 1 15888 1 3158 1 1137 2 10755 1 5828 1 698 1 12102 1 12416 1 15519 1 11963 1 4118 2 13608 1 6520 1 15946 1 11612 1 7014 1 voltage 13668 1 1660 2 6266 1 15629 1 13552 4 8157 1 8628 1 3097 1 13021 1 7374 2 3676 1 6527 1 1273 1 10618 1 14578 1 12889 1 2960 1 1805 1 2705 1 14197 1 9429 2 8753 1 3404 1 9021 1 6770 1 263 2 4898 1 4530 2 10226 1 9705 1 7378 1 16158 4 14464 1 14857 3 5271 1 7133 1 2845 1 4790 2 14585 1 4064 1 641 1 2334 1 4791 1 6281 1 14587 1 4534 1 8408 1 11833 2 3685 2 10363 1 16391 1 9031 4 3556 3 8637 1 8027 2 15910 1 8529 1 10495 5 16025 1 6416 1 4907 1 650 1 12245 1 14868 1 10367 1 7900 2 6294 1 5033 1 1168 1 14225 1 9587 2 13701 2 9728 1 11040 2 4424 1 6792 1 2074 1 5921 1 14341 1 12522 1 2989 2 15430 1 8541 1 2739 3 10660 1 8653 1 8050 1 12145 2 11729 1 10118 1 10664 3 7272 1 14348 1 7665 1 2466 3 2467 1 10253 8 16416 1 4925 1 3807 1 10255 1 13064 3 9601 1 11327 3 11191 1 16052 2 11596 1 9195 1 7796 2 11051 2 13467 1 3710 1 679 1 9860 1 15572 2 7800 2 556 1 938 1 9337 1 179 1 12026 1 12935 1 4933 9 6689 1 5048 3 11739 1 8072 1 12156 1 15940 1 15812 1 9209 1 15187 1 8939 1 7559 1 3971 1 5955 1 11895 2 11896 1 8449 4 12416 1 1082 1 10018 1 1453 2 15035 1 7934 1 9493 1 5443 2 573 1 3473 1 16212 3 6958 3 16321 1 12556 1 4950 1 15695 1 16078 1 8202 2 6962 1 10418 1 13228 1 5964 1 5584 2 6964 1 1999 1 16325 1 5702 1 10544 1 3322 1 10698 1 10030 1 15053 1 12172 1 7189 1 14792 1 5458 1 3729 1 3490 1 6347 1 2495 1 15701 1 14658 2 5089 3 471 1 12178 1 6223 1 4464 1 7080 3 7583 1 2499 2 473 2 13507 1 2131 1 95 1 12059 2 585 1 15710 1 11105 1 14940 2 15964 1 589 1 14525 1 16227 1 5336 1 9640 3 6230 1 7326 1 14010 1 2013 1 9912 2 1893 6 5729 2 16467 1 4362 1 3865 1 14286 2 10309 1 11119 2 351 1 4740 1 1224 1 8234 1 13899 1 12459 1 10859 1 1490 2 7210 1 3065 1 3066 2 13903 2 6865 1 4745 1 6736 79 221 6 15093 1 13255 1 2148 2 7976 1 11657 5 8382 2 4160 1 6499 1 8246 2 6747 1 6244 1 13263 2 14828 1 1367 3 12997 1 9539 1 2417 1 11261 1 7733 1 8128 1 6876 1 7486 1 8490 4 1004 1 8131 1 1782 1 7218 1 13795 1 7738 1 2678 1 2422 1 3082 1 12740 1 3376 1 3223 1 14436 1 13273 2 13008 1 2425 2 5254 1 12095 3 1134 1 1788 2 4169 24 5362 1 7009 16 3231 3 14181 1 11139 1 14969 2 9147 1 10343 1 1389 3 1140 1 14047 1 7623 1 6894 1 5893 3 6264 1 bovine 5301 1 12240 1 13571 1 1992 1 9718 1 12848 1 12059 1 1963 1 62 1 4143 1 9393 1 13228 1 8645 1 6737 9 6123 1 7410 1 14855 1 12537 1 6678 1 2012 1 15473 1 8325 1 8663 1 9444 1 9971 1 1893 1 728 1 3881 1 7120 1 7296 2 5681 1 12068 1 10978 1 6146 1 13880 1 1596 1 16347 1 12368 1 13362 1 4365 1 12295 1 15557 1 11612 2 1296 1 11287 1 triceps_brachii 15940 1 3595 1 2423 1 terra_cotta 7799 1 4588 1 3015 1 11474 1 11782 1 3983 1 jaundice 12585 1 4933 2 13106 1 3145 1 5853 1 14259 1 4156 1 4263 2 15791 1 1301 1 12374 1 8309 1 16026 1 10418 1 13040 1 11080 1 12889 1 6738 10 8292 1 15244 1 5103 1 7413 1 16492 1 85 1 13220 1 11327 2 14066 1 9913 1 7636 1 3288 1 4771 1 8180 1 11870 1 12308 1 12068 1 4512 1 7922 1 5694 1 5766 1 15572 1 5088 1 11631 1 10199 1 7314 1 6792 1 11612 1 730 1 12197 1 genus_canna 10185 1 9945 1 1261 1 9216 1 looper 13609 2 10310 1 15047 1 12326 1 9206 1 body_part 5959 1 4178 2 12297 2 13818 1 71 1 11753 1 14259 1 16321 1 13821 2 13674 1 13422 1 4588 1 15759 1 14919 1 16268 1 6828 1 3839 1 14979 2 9224 1 3030 1 4951 4 9285 1 8202 1 7239 1 5644 1 1999 1 15588 2 14458 1 14121 2 5702 2 2329 1 5645 1 13680 1 4531 1 14718 1 7189 2 6278 1 14199 1 7882 1 1534 1 7133 1 7636 1 11827 1 10033 1 3617 1 13099 10 2335 1 12569 1 8408 1 1736 1 9093 1 6147 1 201 1 5273 1 4659 1 9901 1 15705 1 16391 1 3113 2 1738 1 2720 1 15541 1 14469 3 8639 1 4352 2 531 1 10906 1 4143 1 3420 1 13837 1 16026 1 971 1 15277 1 9437 1 536 1 14992 1 7256 1 5103 1 15918 1 6230 1 2789 1 8110 1 13377 1 9644 1 14669 1 658 1 9971 2 7042 1 4610 1 5033 3 12916 1 13767 1 6728 1 3056 2 5282 2 349 1 3348 2 12718 2 15353 1 15559 1 13772 1 14746 2 1225 2 8852 1 10453 1 2299 1 4303 1 4156 1 357 1 13251 1 8787 1 9259 1 3639 1 1300 1 12145 1 3276 1 8309 1 10252 1 8310 1 5616 3 13711 3 3582 1 5240 3 6110 3 14291 1 6173 1 12270 1 10457 1 6739 50 10867 2 3282 1 13531 1 1840 1 10255 2 14352 2 11403 2 14160 1 7477 1 2229 1 6497 2 6241 1 6499 1 1309 1 3812 2 6873 1 13718 1 14616 1 15672 1 935 1 1067 1 5500 1 13395 1 5942 1 9932 1 10066 1 14428 1 2749 1 1430 1 2815 1 5299 1 14759 1 7420 1 7421 2 1635 1 9405 1 1370 1 10812 2 12733 1 3454 1 12735 1 1005 2 1313 1 3595 1 13797 1 10333 1 9477 1 11739 1 14905 1 8322 1 7358 2 8192 1 4446 1 2881 1 15028 1 9942 1 15245 1 12744 1 14103 1 16491 1 8559 1 13278 1 16258 1 6760 1 11749 1 14106 2 6261 1 12226 1 15687 1 11686 1 5694 3 13666 1 4636 1 12353 1 16318 2 13482 1 11612 1 14972 1 14189 1 15317 1 7878 2 connective_tissue 13484 1 6523 1 10757 1 13018 1 16213 1 8822 1 8697 1 2699 1 15636 1 2117 1 4951 2 15828 1 7239 1 2045 1 15045 1 6398 1 11080 1 15472 1 2836 1 10765 1 16082 1 8403 1 10698 1 7946 4 2263 1 14980 1 4591 5 15329 1 3992 3 85 1 16008 1 8890 6 7882 1 7636 6 13752 1 12567 1 13099 2 9235 1 10288 2 2335 6 5088 1 6535 2 3685 2 11504 1 15840 1 11636 1 13626 1 11709 1 12905 1 11223 1 10706 2 14469 1 12439 1 10561 1 1042 1 779 1 5175 1 2284 1 6414 1 11104 1 13576 1 11312 1 2067 1 6419 1 12578 1 11997 1 1953 1 918 1 10782 1 8110 1 1479 1 10369 1 10970 1 659 1 5033 2 8180 3 15079 1 14147 1 10572 1 6728 1 6850 1 8722 1 1562 1 5180 1 10656 1 13897 1 12325 1 15430 1 12586 1 12075 2 4156 1 14025 1 167 1 2658 1 1301 2 1420 1 1907 1 8309 2 3356 2 7473 1 9394 1 5616 6 13063 1 13711 1 5240 2 2928 1 5878 1 6740 42 9800 1 8662 1 13257 1 10255 1 9532 1 6497 3 15736 3 6310 1 14293 1 7344 3 11327 1 11131 1 9127 1 998 1 12862 1 13264 5 16131 1 1634 1 14956 1 7421 1 9996 1 14172 2 6502 1 9667 1 11673 1 14241 1 1313 1 3219 1 13654 1 7002 2 1784 1 11883 1 3223 1 5756 1 14911 1 3084 1 11887 1 15244 1 9079 1 9550 2 11889 1 14500 1 8559 2 8687 3 1856 1 565 1 3969 1 13736 1 1651 1 9618 1 11139 1 11684 1 8449 1 12226 2 14044 1 4512 1 5766 1 5694 7 12353 1 1655 1 11612 1 stretcher 6080 2 10628 1 12645 1 16484 1 13451 1 13408 1 15588 1 4464 1 853 1 looped 5883 1 3750 1 13157 1 13314 1 1076 1 10774 1 4588 1 6080 1 16120 1 3764 1 9228 1 13461 1 13584 1 4998 1 6741 7 9175 1 10784 1 14160 1 14092 1 16355 1 2778 1 4171 3 7010 1 3971 1 7481 2 2605 1 173 1 6819 1 16178 1 14270 1 14956 1 siskin 1457 1 1970 1 6742 5 6491 1 3258 2 12395 2 12653 1 1509 3 2869 1 1510 2 5361 2 spaniel 1576 2 4623 1 12643 1 5555 1 12231 1 8171 2 2655 1 13915 1 9219 2 253 1 14259 3 14606 1 1058 2 14242 1 12326 1 4352 1 8709 1 14319 1 1907 1 16148 1 13583 2 2834 1 9287 2 9206 2 11572 1 15028 1 626 1 3483 1 4957 5 14012 1 6743 9 14065 1 5457 1 16258 1 14279 1 7103 1 227 1 12811 1 5680 1 1328 1 901 1 6178 1 7396 1 6581 1 3921 1 pinaceae 8702 1 14856 1 13356 1 10983 1 10321 1 5901 1 1457 1 3416 1 12711 1 11661 1 6345 1 13086 1 6516 1 11147 1 15827 1 13878 1 5849 1 14196 1 15082 1 8512 1 5361 1 14189 1 9103 1 15574 1 13712 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12206 2 11821 2 3582 1 7226 1 6323 1 8292 2 9521 1 4680 2 5914 1 14791 1 2247 1 5439 1 6872 11 9176 1 227 1 4688 1 11686 2 3790 1 14047 1 12620 1 1669 1 humiliation 869 2 15509 1 8414 1 219 1 12904 1 10453 1 4369 1 9400 1 11812 1 12802 1 1997 1 13040 1 13000 1 14923 1 14636 1 5995 1 embolism 1635 1 1067 1 6739 1 10255 1 7189 1 3641 1 fascination 7420 1 56 1 483 1 3461 1 8542 5 9400 1 11246 1 15635 1 13490 2 12795 1 3234 1 12161 1 4688 1 13040 1 13442 1 11250 1 2277 1 13712 1 spasmodic 12585 1 7969 1 10578 1 4018 1 12693 1 13321 1 15170 1 8309 1 9953 1 14121 1 3677 1 2329 1 9020 1 9429 1 6739 1 5878 1 5778 1 3995 1 9989 1 6310 1 6499 1 11327 1 1534 1 10062 1 6873 11 1595 1 14066 1 5527 1 15672 1 14583 1 5500 4 298 1 12121 1 12338 1 12572 1 869 2 7421 1 6779 1 3145 1 13797 1 1980 1 5975 1 8556 1 5053 1 13040 1 7358 2 9074 2 12186 1 7084 1 9942 1 12912 1 13150 1 8392 1 213 1 12003 1 15422 1 11236 1 8180 2 15687 1 rise_up 11108 1 9209 1 4238 1 12995 1 227 2 8119 2 4279 1 7120 1 7260 1 5281 1 7314 1 14525 1 11612 1 2826 2 12045 1 at_odds 2608 1 763 1 1617 1 11033 1 203 1 12969 1 15194 1 9390 1 8834 1 8314 1 9215 1 8328 1 15521 1 7839 1 sneezeweed 12968 1 3760 1 3444 1 6020 1 6801 1 293 1 4263 1 endocrine_gland 4343 1 3799 1 3065 1 12733 1 on_tap 1331 2 4200 3 2179 3 2858 3 94 2 14718 2 9219 2 7438 3 5608 5 6874 13 8435 2 3619 2 13374 3 2277 3 4698 3 16074 3 179 2 coaxial_cable 3685 2 2263 1 14053 1 10255 1 12393 1 6080 1 11728 1 3907 1 1907 1 7800 1 2438 1 11312 2 2960 1 chivalric 16035 1 9021 1 8325 1 5805 1 4155 1 12848 1 9486 1 hypophysectomized 15694 1 15875 1 5278 1 9431 1 deterge 9693 1 1430 1 6535 1 12735 1 10247 1 13096 1 531 1 beam 14314 1 12621 1 5005 1 11008 1 7365 2 5770 7 4393 1 12229 2 9819 1 15629 1 8628 1 15759 2 1395 1 14319 1 11147 1 6020 3 3676 6 10478 1 2045 1 15529 1 6767 1 14578 1 2194 1 9429 1 8753 2 14855 1 1664 1 9021 1 3406 2 893 1 3678 2 9705 1 3105 1 8519 2 14323 1 6279 2 11154 4 4401 2 13684 1 6281 1 1667 1 8523 1 2335 1 1817 1 13829 1 7025 1 13566 2 8408 8 10491 2 2204 1 10096 1 2716 1 6779 1 11705 1 14208 2 3556 4 5023 1 5173 2 14212 1 15147 2 13836 1 9169 1 11568 1 13040 1 9723 5 9172 1 536 1 7256 8 16399 1 8292 1 7257 1 8418 1 2581 3 3938 1 12916 3 7528 1 1290 1 9728 1 7260 3 13451 3 14083 1 11040 2 4424 1 9731 1 13454 1 7657 1 12140 1 13958 1 12011 1 15009 2 10505 1 2598 1 13461 1 13063 1 2601 2 13324 1 931 1 6053 1 11188 1 2467 2 14610 1 10253 3 5295 1 9459 1 3138 1 10126 1 12020 1 2089 1 7799 1 10131 2 6445 1 556 1 2751 3 179 1 7421 1 3716 1 4933 3 11057 4 3145 1 10812 1 3717 1 10265 1 10136 1 1437 1 8930 1 2755 1 4112 1 14626 1 7806 1 9616 1 8558 1 13218 1 14500 1 10936 1 3601 1 7559 7 13736 1 4830 1 11069 1 11895 2 12543 1 7565 1 8817 1 699 1 16318 2 7436 1 7934 1 5442 1 9494 1 15692 1 3473 2 702 1 9087 1 14779 1 6339 1 12815 1 7692 9 4948 1 6080 2 14118 2 2625 1 5319 1 6830 3 2260 1 7697 4 4129 1 1999 2 7442 1 10280 2 12822 1 6713 2 15053 1 1089 1 87 2 3615 1 3490 1 2902 1 13360 1 4001 1 471 1 12570 2 6349 1 4464 2 3732 1 4728 2 5331 2 5718 1 12703 2 7585 2 7711 2 4143 1 11373 2 11105 1 14941 1 1604 2 14525 1 3626 1 14408 1 9521 1 8716 2 2646 1 14010 2 2136 2 8223 1 3195 1 213 1 14014 1 11928 1 6727 1 6849 1 16467 1 9527 2 8592 1 5610 1 3510 4 13129 1 7210 1 1117 2 8122 1 4371 1 1761 2 6736 1 3875 1 11251 3 13256 1 10321 1 14424 1 11401 3 2931 1 9532 2 11406 1 14294 1 996 1 13791 1 6244 2 3215 1 12997 1 7731 1 4992 1 8128 1 13264 3 6876 73 13002 1 4164 2 3077 1 8868 1 15110 1 15744 1 11542 1 13801 1 9672 1 6629 1 2544 1 3376 2 13273 1 7006 1 5756 1 877 1 3896 1 11544 1 7496 1 1512 1 4040 1 15118 1 4041 1 2042 1 10751 6 15120 6 1381 1 4169 3 8875 1 9416 1 6760 1 1136 2 5633 1 7749 1 7233 1 12104 1 12353 6 14189 2 7623 1 6644 1 underpart 9405 1 14623 1 6941 1 9922 1 8822 1 9186 1 1690 1 4335 1 10167 3 12326 1 3258 1 6287 1 13821 1 15471 1 15434 1 9074 3 6398 1 13063 2 4185 2 9103 1 15763 1 6419 2 14076 1 7410 1 4829 1 5705 1 4590 2 9079 1 12223 1 7166 2 13392 1 15057 1 10239 1 8021 1 2089 1 10131 2 10656 1 6875 14 6801 1 11612 2 5342 1 4622 1 15426 1 7350 2 9095 1 2970 3 infuriating 4695 1 2341 1 5084 1 13699 1 8528 1 4099 1 12011 1 report_card 8975 1 14328 1 14532 1 13315 1 4499 1 15110 1 10380 1 1615 1 1418 1 8869 1 14904 1 3676 1 838 1 4035 1 14405 1 14941 1 5964 1 877 1 3280 1 2705 1 7667 1 13492 1 5841 1 6592 1 4886 1 16492 1 14995 1 8980 1 8346 1 8314 10 7148 1 6241 1 4701 1 312 2 8844 1 10875 1 13683 1 13949 1 15225 1 6444 1 14394 1 14271 1 6853 2 2016 1 chiaroscuro 3951 1 7398 1 8285 1 996 1 8108 1 7195 1 3080 1 12164 1 4185 1 3862 1 celt 2404 1 6877 6 244 1 13569 1 253 3 12555 1 8892 1 12848 1 1216 1 13648 1 7012 2 10196 1 15919 2 15200 2 13228 1 11651 1 diphthongize 12399 2 12145 1 6367 1 1039 1 3711 1 b_vitamin 10182 1 10867 1 5443 1 15781 1 15918 1 15403 1 2962 1 8588 1 10828 1 16438 1 6878 8 11223 3 5104 3 4156 1 7585 1 2900 1 5033 1 7636 1 2902 2 11870 1 6456 1 1420 1 11017 1 8456 1 11424 1 depressing 9846 1 387 1 13314 1 9819 1 11653 1 13900 1 6336 2 10452 1 3475 1 4335 1 6204 1 10537 1 12011 2 1145 1 1905 1 12556 1 10541 1 13674 1 4950 1 6019 1 5188 1 6081 1 7239 2 10087 1 8428 1 7914 1 11081 1 2467 1 6769 1 4529 2 10319 1 3678 1 10485 1 9327 1 5621 1 8796 1 15533 1 2563 1 14323 2 5328 1 13912 1 11869 1 996 1 8385 1 9663 1 154 1 8062 1 15445 2 14658 1 9403 1 16015 1 9714 1 9830 1 4933 1 6059 1 12643 1 7422 1 12284 1 11951 1 3113 1 12315 1 15146 1 6879 16 15743 1 404 1 12534 2 7223 1 11450 1 10298 2 240 1 14769 1 13214 1 689 1 838 1 12094 1 8445 2 1099 1 2943 1 14525 1 7006 1 134 1 6419 2 2945 2 10852 2 4998 1 3022 1 1014 1 10564 1 14009 1 16491 1 7178 1 8325 1 16492 1 11512 1 1986 1 7360 1 16258 1 310 1 657 1 12383 1 4172 2 16494 1 14147 1 5065 1 9787 1 6389 1 9552 1 14108 1 8332 1 13814 2 frustration 869 1 7566 1 6549 1 9882 1 15430 1 11753 1 10265 1 16360 1 14412 1 14555 1 13799 2 6880 8 3205 2 12753 1 9937 1 1964 1 11589 1 3248 1 3376 1 14231 1 1318 1 12939 1 10797 1 15028 1 4606 1 4529 1 10253 1 11462 1 992 1 11400 2 9956 1 1148 1 5648 1 2546 1 9532 1 8517 1 13912 1 6127 1 15297 1 12383 2 13497 1 4401 1 6685 1 11090 2 11749 1 15571 1 5034 1 13624 1 6747 1 9827 1 12619 1 1818 1 8651 1 6833 1 5180 1 7733 1 15946 1 6853 1 5667 1 subclass_dilleniidae 4870 1 3230 1 diversion 2431 1 15466 1 8153 1 8006 1 10380 1 2863 1 16117 1 13674 1 8238 1 15170 1 11755 1 2865 1 4021 1 3676 1 13490 1 5319 1 10543 1 13091 2 15507 2 1495 1 6925 1 4556 2 16300 1 3705 1 14126 1 5492 1 9021 2 2049 1 10483 1 6212 1 1807 1 9956 1 12051 1 14718 1 6241 1 12895 1 3617 2 6624 1 553 1 15933 1 5781 1 11051 1 4688 1 15445 1 2089 1 12569 1 7611 1 8408 1 14271 1 7198 1 9830 1 13034 1 5883 1 8093 1 3043 1 3113 2 5717 1 6781 1 13438 2 11477 2 4443 1 13110 1 14243 1 6881 13 15846 1 14002 1 2452 1 3376 3 11309 1 14626 1 2506 1 7320 1 10233 1 14276 1 4626 1 11374 1 2646 1 1985 1 10465 1 14373 1 15279 1 7649 1 14082 1 15077 1 7932 1 2950 1 12383 1 4912 1 11278 1 314 1 1413 1 11282 2 5205 1 7066 1 7749 1 13608 1 1141 1 abortive 16332 1 622 1 2646 1 11769 1 2673 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12383 1 12795 1 12715 1 7066 1 9009 2 5694 1 2591 1 15625 1 1562 1 14271 1 4758 1 final_solution 13687 1 15570 2 4680 1 199 1 5203 1 7076 1 13606 2 4860 1 11826 1 fabricate 14798 1 8628 1 6321 1 8752 1 correctional_institution 12529 1 16164 1 3784 1 13668 1 7230 1 1566 1 12249 1 5978 1 5534 1 2511 1 9773 1 13763 1 9203 1 945 1 6817 1 16494 1 12715 2 6238 1 4136 1 6885 5 8408 1 14285 1 3084 1 4226 1 coefficient_of_expansion 15228 1 7393 1 1893 1 13649 1 10974 1 16236 1 1205 1 13002 1 bob_hope 10325 1 15733 1 3996 2 5612 1 14047 1 11612 1 3322 1 edible_snail 13434 1 1613 1 8021 1 6201 1 robotic 9784 1 12229 1 11020 1 15833 1 5216 1 7665 2 11188 1 1169 1 sputtering 2499 1 12939 1 13008 1 4145 1 16097 1 5844 1 10231 1 15393 1 4950 1 11287 1 roam 4466 1 3077 2 12718 2 3143 2 6059 2 12284 3 1436 1 6732 1 424 2 10364 1 1145 1 9220 2 10934 1 7682 2 13821 1 12786 2 3354 1 5029 1 13192 1 2424 1 13007 2 13063 1 12987 1 8260 1 8367 1 13646 2 9079 1 11002 1 6886 13 14733 1 1134 1 11545 1 3768 1 12540 2 9444 2 4888 1 13762 1 6312 2 13080 1 14584 1 8091 1 2055 1 12795 1 6146 3 13434 1 1596 2 7464 1 8248 1 11898 2 3699 1 8372 1 5593 1 9845 1 3252 1 13104 1 8735 2 slag 11176 1 15572 1 15996 1 9728 1 1893 1 14544 1 3336 1 10999 1 15692 1 9067 1 sterile 8879 1 14639 1 13609 1 1024 1 9219 1 4179 1 6204 1 1271 1 8953 2 13552 1 13822 1 13674 1 11212 1 15759 2 9014 1 6018 1 2190 2 11147 1 14712 2 4951 2 5449 2 8512 1 4338 1 5776 2 2836 1 10544 1 14924 1 12302 1 8012 1 10698 1 2263 1 11760 1 13987 1 15403 1 7377 1 3992 1 2120 1 8516 1 5163 1 3324 1 10226 1 8517 1 13096 1 16158 1 6467 1 10286 1 16087 1 10901 1 5849 1 11017 1 1818 1 1596 2 8408 1 1403 1 10772 1 14798 2 13034 1 12643 2 9576 1 6661 1 9635 1 10364 1 12509 3 5718 2 7523 1 10293 1 2911 1 15911 4 531 1 10906 1 6351 1 4143 1 779 1 14805 1 11451 1 13372 1 208 1 13576 1 6419 1 7391 1 11233 1 11999 1 2012 2 13698 1 14472 1 6787 1 3744 1 16465 1 1106 1 15719 1 5282 2 6852 2 9920 2 11854 1 8300 1 3200 1 14675 1 8047 1 7658 1 666 1 6862 1 12326 2 8786 1 4263 1 5927 2 3135 1 5188 1 12723 1 120 1 2024 1 6367 1 4920 2 2662 1 6369 1 15293 1 6557 1 9056 1 6371 1 10321 2 7413 1 14425 1 2152 3 11403 2 14092 1 614 2 12856 1 14616 1 227 1 6746 1 3362 1 5811 1 14548 1 3760 1 4563 1 5745 1 14892 1 8434 2 12020 1 13722 1 1917 1 10993 1 5747 1 12862 1 7421 3 4030 1 10397 1 13968 1 9477 1 13970 1 10267 1 8930 2 4110 2 9268 3 14175 1 15747 1 8259 1 14911 1 7007 1 14370 1 242 1 14774 1 5311 1 4886 1 13076 1 6888 25 13658 1 6890 1 5062 1 7744 2 10142 1 5889 1 12485 1 11684 1 6261 1 4511 1 2953 1 10273 1 13608 1 13665 1 5694 1 145 1 16444 1 11612 2 streaming 13952 1 6059 1 1344 1 4073 1 12815 1 16225 1 6020 1 14537 2 7697 1 11187 3 11572 2 12131 1 14348 2 12329 1 4542 1 9155 1 9021 1 9641 1 15686 1 6887 9 10103 1 9532 2 4957 1 524 1 11646 1 3585 1 7009 1 9971 1 6727 1 14376 1 13201 1 595 1 5232 1 5694 1 15572 2 6580 1 6688 1 3536 1 6374 1 7436 1 6894 1 15084 1 autosomal 4283 1 1903 1 9298 2 14805 1 13089 1 3894 1 13576 1 6494 1 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1 1014 1 6230 1 6102 1 5978 1 8223 1 15279 1 5062 1 4323 1 1650 1 6891 19 659 1 10374 2 13380 1 7260 1 12102 1 12416 2 4707 2 12161 2 13182 1 14018 1 13608 1 15225 1 16178 1 3026 3 5317 1 1656 1 3699 1 6264 1 ketch 15940 1 14101 1 6959 1 13264 1 12522 1 7143 3 caribbean_sea 4071 1 15759 1 505 2 1773 1 12524 1 9764 1 11859 1 3738 1 311 1 9278 1 exponential_expression 8503 1 11017 1 12669 1 scholarly 14897 1 13754 1 6781 3 1615 3 928 1 13610 1 4761 1 10137 1 5412 1 12848 3 11133 1 8379 1 6168 1 15637 1 2504 1 10418 1 15953 1 334 1 11974 1 5240 1 11172 3 5841 1 10483 1 16492 1 10970 1 846 1 7812 1 2002 1 1650 1 8775 1 1989 1 1180 1 6892 19 10162 3 4706 3 6262 1 14657 3 15805 1 1777 3 13171 1 1656 2 14895 1 brisbane_quandong 7904 1 commercialized 719 1 2315 1 5255 1 15487 1 8570 1 13722 1 productivek 12579 1 10594 1 10500 1 7067 1 5978 1 first_baseman 16250 1 4401 1 2802 1 2349 1 amended 14585 1 9305 1 5297 1 13626 1 12266 1 3596 1 2114 1 15326 1 3298 1 9375 1 3038 1 14956 1 calligraphy 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10144 1 11282 1 1263 1 12416 2 4707 1 1328 1 11963 1 15033 2 14187 1 7813 1 6070 1 5317 1 12620 1 14047 1 12688 1 4120 1 11611 1 2826 1 9278 2 opportunely 9021 1 14913 1 9377 1 11137 1 14626 1 6520 1 13871 1 effortless 10492 1 6530 1 8093 2 12718 1 1023 1 9297 1 16491 1 15996 1 9176 1 6499 1 1089 1 10875 1 10840 1 7835 1 1290 1 15512 1 2375 2 15579 1 5053 2 9264 1 10037 1 12959 1 15883 1 12186 1 12025 1 2686 1 6894 2 receipt 15581 1 1793 1 472 1 448 1 12198 1 5430 1 3242 1 12473 1 8156 1 2223 1 4616 1 6340 1 13745 2 6830 1 508 1 10919 1 3480 1 15594 1 6904 9 5689 1 8732 1 4970 1 3915 1 10485 1 994 2 7607 1 1650 1 9352 1 10840 1 14672 1 1242 1 4707 1 14504 1 1916 1 7014 1 bibliomania 8180 1 8108 1 12848 1 homograph 4198 1 10492 1 6779 1 14010 1 866 1 15459 1 12826 1 cupid 905 1 3329 2 15797 1 10202 1 12097 1 9650 1 15064 1 4309 1 8421 1 718 1 3760 1 10286 1 14319 1 2326 1 4511 1 11924 1 5682 1 14101 1 1510 1 8055 1 10068 1 3582 1 3348 1 13104 1 cultural_movement 5046 1 15407 1 5412 1 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1 14956 1 4164 1 4933 1 16428 2 3078 1 3820 4 10816 1 15878 1 9477 2 16433 1 3764 1 2823 1 2041 1 2945 1 1382 1 5439 6 1136 1 5204 1 7296 1 8449 1 1520 1 12161 1 11686 1 5694 1 3025 1 10018 2 13482 1 12038 1 13814 1 16210 1 living_space 5332 1 7110 1 621 1 dissenting 141 1 12586 1 3549 1 12569 1 8834 1 1604 1 3766 1 radix 10265 1 12868 1 1940 1 4272 2 2680 3 2133 3 11396 1 6975 10 13273 3 2705 1 2227 7 4925 2 6713 1 1014 1 14009 2 879 1 14913 1 12543 1 13182 1 1845 2 10018 2 11831 1 7733 1 11264 1 4176 4 10330 1 family_centrarchidae 2853 1 6801 1 13728 1 2089 1 4698 1 5179 1 line_of_credit 575 1 12448 1 6261 1 7304 1 pensively 7446 1 3862 1 knitting_stitch 9415 2 5442 1 4933 1 14424 1 15230 1 4171 2 1236 1 7692 1 5318 1 14488 1 4001 1 16178 1 10131 2 2662 1 8390 1 5240 1 tyche 12693 1 2799 1 13176 1 11796 1 10514 1 arsine 6498 1 12111 1 11017 1 behavor 12643 1 2882 1 10294 1 7137 1 fund 820 1 15581 2 7367 1 8334 1 9819 1 7626 1 3242 1 15995 1 6204 1 12556 3 5148 1 330 1 15759 1 15635 1 10615 1 1272 2 9625 1 11012 1 11147 1 1526 1 13490 3 4338 1 7441 1 10544 1 9288 1 5840 1 9429 1 4529 1 3404 1 2049 1 15403 1 468 3 13427 2 6714 1 2051 1 5271 1 11830 1 267 1 12309 1 8021 3 2846 1 4248 1 7519 1 8408 1 11917 1 15208 1 2204 1 10361 1 4250 1 1211 1 8975 1 11635 1 1040 1 12645 1 2720 1 4850 1 15910 1 11566 1 15844 1 5654 1 8097 2 1475 1 7893 2 1602 1 4966 1 779 1 8467 2 14213 1 15487 1 12909 1 10639 1 4860 1 16398 1 6976 31 2646 1 9441 1 6293 2 484 3 11109 1 11512 1 4203 2 12003 1 2214 1 3507 2 5403 1 15077 2 8775 1 12067 1 2407 1 729 1 1608 1 15225 1 11789 2 6162 1 13249 1 11182 1 3059 1 3749 1 5183 1 2989 1 8541 2 6612 1 6794 1 9793 1 16117 1 5412 1 5559 1 10456 2 14535 2 10795 1 3277 1 14155 2 10727 1 4680 1 6495 1 673 1 3807 2 6498 2 6871 2 6241 1 7607 1 11327 1 790 1 10325 1 14823 2 2366 1 13395 1 8609 1 9860 1 8436 1 7859 1 1181 1 6801 1 1635 1 8069 1 6249 1 13214 1 11742 1 3377 1 14626 1 5756 1 3720 1 3766 1 13338 1 3531 1 7555 1 3658 1 13275 1 1649 1 4041 2 1014 1 15386 1 14374 1 5362 1 13408 1 7563 1 14570 1 12226 1 12875 1 6519 1 15519 1 2551 1 10414 1 4121 1 11490 1 15688 1 gastrocnemius_muscle 7541 1 6646 1 5616 1 14189 1 15028 1 artistically 8231 1 7398 1 11084 1 2931 1 777 1 1639 2 6090 1 14720 1 8163 1 14044 1 9546 1 12796 1 7859 1 7667 1 automated 14015 1 1360 1 10456 2 1279 1 5291 2 9906 1 8408 1 16192 1 5216 1 3997 1 11339 1 10200 1 7197 1 richard_i 4455 1 905 1 12726 1 1737 1 3436 1 12198 1 7230 1 4426 2 2081 1 7789 1 111 1 13260 1 65 1 14555 1 3803 1 7033 1 5926 1 9886 1 12383 1 2608 1 3169 1 11819 1 7563 1 3986 1 13322 1 11686 1 8323 1 2757 1 4271 1 1176 1 13856 1 abaxial 7421 1 9470 2 8012 1 3716 1 13099 1 13531 1 10812 1 10131 1 4446 2 14616 1 14212 1 unlikely 8438 1 15581 1 10363 2 10149 1 7888 1 15166 2 1185 1 6794 1 7154 1 4761 1 6433 1 16117 1 9375 1 15503 1 14494 1 15010 1 13214 1 16063 1 12970 1 4750 1 10269 1 5240 1 12188 1 9955 3 13139 3 2266 1 5901 1 6978 21 7413 3 10620 1 10646 1 14651 1 4594 1 11327 2 8113 1 2054 1 15058 2 7593 1 5971 1 14892 1 12260 1 9947 1 126 1 8946 1 13171 1 10130 1 6071 4 6444 3 12352 3 1611 1 2920 3 15123 1 12729 3 7170 1 10218 1 14934 1 13706 2 carburize 8613 1 4188 1 4464 1 undoubted 8946 1 6980 1 4495 1 expend 11205 1 7739 1 7114 1 9038 1 10103 1 nigerian_capital 14901 1 heiress 3815 1 10056 1 62 1 9889 1 16104 1 7170 1 7022 1 12877 1 12041 1 4912 1 ferdinand_magellan 3134 1 13801 1 505 1 4463 1 2938 1 4306 1 9285 1 13089 1 12109 1 7440 1 5756 1 9103 1 implantation 12955 1 10052 1 9219 1 8156 1 2312 1 11198 1 9543 2 11461 1 13692 1 4527 1 5615 1 4240 1 688 1 11122 1 8456 2 10685 1 7473 3 13855 2 452 4 13576 2 14911 1 16327 2 14992 1 12270 2 437 1 14698 1 14980 8 11999 1 16328 2 1014 1 1190 1 8110 1 4343 1 6278 2 6890 3 6979 8 11327 2 2691 1 5033 1 9662 1 1153 2 2795 1 2902 1 4831 1 9728 1 15445 1 9860 1 2815 1 13965 1 11983 1 270 1 12507 1 2986 1 12841 1 8830 1 procreate 892 1 885 1 1053 1 779 1 7661 1 7230 1 9268 1 11708 1 8055 1 10934 1 outmoded 10363 1 8954 1 15023 1 6734 1 13440 1 7992 1 4523 1 14728 1 1028 1 971 1 3596 1 14626 2 5888 2 11886 2 1982 1 8458 1 10357 1 6230 1 5648 1 16492 2 13259 1 6981 7 11279 2 2513 2 14183 1 7260 1 4706 1 13649 1 12136 1 11701 1 chatty 12177 1 392 1 3730 1 5528 1 12315 1 7169 1 7230 1 1148 1 3902 1 pungency 5262 1 8582 1 1620 1 11088 1 179 1 2951 1 13110 1 coaxingly 5739 1 5528 1 573 1 achievability 11678 1 5072 1 8156 1 6433 1 clef 4529 1 6786 1 3144 1 12522 1 14009 1 9819 6 5311 1 6336 1 5121 1 8853 1 4761 1 14104 2 7210 5 6982 7 14818 1 11728 1 11294 6 1239 6 6398 1 6656 1 clobber 387 1 5778 1 11819 1 11033 1 729 1 8026 1 9359 1 13171 1 13301 1 12448 1 ilosone 5878 1 438 2 10654 1 3571 1 13223 1 3280 1 2705 1 10605 1 12059 1 southern_beech 11292 1 14840 1 12812 1 7574 1 lithomancy ordinate 135 1 6011 2 10483 2 12645 1 5718 1 10340 1 1309 1 14318 4 548 1 13683 1 7260 1 2797 2 2572 1 1562 1 14047 2 electron_shell 4031 1 12721 1 15880 1 3700 1 7824 1 7559 1 10816 2 14287 1 5931 1 compromise_verdict 11552 1 15576 1 4480 1 summery 14323 2 11327 2 9644 2 14002 2 7667 2 12848 2 order_hymenoptera 12418 1 2027 1 7744 1 12385 1 11181 1 10111 1 4263 1 internationality 16399 1 11172 1 11266 1 9242 1 13011 1 12371 1 1595 1 7671 1 3064 1 4408 1 13648 1 14018 1 15753 1 4363 1 11182 2 11347 1 chromium 5209 2 12231 1 15692 4 9358 1 6299 1 1616 1 4261 4 15470 2 1836 2 1395 2 14921 1 7824 1 13583 2 16236 1 2873 1 10253 1 3176 1 6592 1 1205 3 1088 1 4531 1 5742 1 5810 1 5626 1 3684 1 8666 5 5088 1 7025 1 4534 1 12054 1 4464 1 11984 1 7421 1 7833 1 3556 1 5332 1 14831 1 10559 1 4732 1 10906 1 6910 1 8643 1 9546 1 1099 1 3084 1 15342 1 15918 1 4169 2 6695 1 12485 1 567 1 6760 1 10143 1 8449 1 12978 1 3508 1 7393 1 9918 1 13311 1 16143 1 6983 13 poseur 8517 1 unappealable 16164 1 3032 1 4938 1 1450 1 13956 1 10426 1 14626 1 5810 1 12994 1 6236 1 apothegmatic 1635 1 10422 1 8984 1 9143 1 sacrifice 14256 1 14052 1 8624 4 4049 1 9423 1 6958 1 13552 2 15259 1 4950 1 9821 1 13674 3 9886 1 3396 2 7372 1 1587 1 2326 5 11819 1 5210 1 8750 1 8203 1 11437 1 13930 1 9822 1 12302 1 7760 1 14855 2 5647 2 5214 1 1811 1 3786 1 1595 1 8348 1 7581 1 13752 1 8758 1 13877 1 15058 1 1466 1 13432 2 15 2 8211 1 13434 1 7245 1 13171 1 12569 1 268 2 5168 2 14270 2 13362 2 7383 1 8829 1 399 1 4250 1 16015 4 10492 2 15839 1 8093 3 4660 2 905 2 906 3 7451 1 5785 4 9833 2 965 1 15541 1 2208 1 10559 1 15913 1 12059 1 6473 1 5470 1 4539 1 779 1 585 1 10907 1 10846 2 7956 1 3560 1 3119 3 11924 2 12443 1 14941 2 13374 1 5102 1 536 2 591 1 2577 2 16398 2 8292 1 11999 1 6230 2 14217 2 11512 2 6667 1 12977 1 3197 1 6980 1 12448 1 2214 1 2071 1 101 1 7718 1 2585 1 13181 1 10376 1 7260 1 12260 1 5110 1 33 3 6671 1 12071 1 10981 1 12657 1 13053 1 11457 1 288 1 6984 42 7267 1 8784 1 10503 1 665 1 4156 2 11728 1 15729 1 10795 1 8546 1 5805 1 7539 1 15975 1 7099 1 6556 1 610 1 15861 1 3582 2 1176 1 2873 1 11188 2 13712 1 8991 1 225 1 6497 1 7607 1 1309 1 1063 1 9061 1 16051 1 4928 1 934 1 10325 1 13395 1 15933 2 5745 1 12795 1 126 2 937 1 1916 1 12997 1 5427 1 16303 1 11733 1 5995 1 7802 1 3078 1 9067 1 11735 1 10997 1 8137 1 8069 1 3082 1 4762 1 9545 1 11743 2 7806 1 10208 1 6757 1 1982 1 13473 2 2246 1 10750 1 13546 1 880 1 8325 1 15459 1 16492 1 813 1 10600 1 4384 1 8328 1 6890 1 11748 1 14838 4 10212 2 13606 1 8146 2 13476 1 4889 1 16494 1 11895 1 2694 1 8393 1 15251 1 8746 1 15034 2 7181 1 6520 1 14635 1 12353 1 7234 1 12620 1 11612 2 unfrequented 11678 2 13681 2 8462 2 11315 2 14373 2 7888 2 11123 2 12609 2 14765 2 amass 2447 1 3145 1 12002 1 2214 1 6980 1 13736 1 9937 1 10756 1 8467 1 14864 1 12957 1 15 1 13171 1 8197 1 15115 1 tuber 1330 1 11431 1 8879 1 5896 1 15994 1 8338 1 253 1 12109 1 6585 1 10023 4 13086 1 4522 1 2436 5 9285 3 11619 1 11972 1 8202 1 7574 2 15200 1 11353 1 2560 2 7442 1 3677 1 12822 1 8458 3 8702 1 8012 3 8886 1 15404 2 13987 1 12892 1 10 1 3992 2 15267 4 5213 1 10840 1 8021 1 5849 1 5783 1 9237 1 2847 4 6535 2 1596 4 10094 1 15770 1 8581 19 9768 2 7952 1 15840 1 9635 4 5718 1 9032 4 16280 1 5173 1 9834 1 6287 3 16281 2 12318 1 12968 1 2062 1 15544 1 4196 1 208 1 9103 2 11572 1 6419 1 2134 1 481 1 14077 1 7716 1 918 1 10303 1 6480 1 5546 1 10176 1 7457 1 7900 1 6727 1 11514 3 11577 1 9973 1 1560 6 8180 1 13309 2 13379 1 9587 2 14672 1 7904 2 6668 1 1357 1 9843 1 417 1 5112 1 6487 1 3348 1 3200 2 1487 1 9922 1 1755 4 6985 42 9115 4 13385 3 11859 1 5556 2 14607 1 8185 1 12326 1 4263 1 15973 1 12786 2 14288 1 8480 1 7158 1 7471 3 4490 1 3439 1 607 1 2599 1 8309 3 13583 1 13712 2 363 1 8733 1 11594 1 10321 3 13462 1 14352 1 2152 2 16481 3 14159 1 6681 1 1621 3 10512 2 11256 1 9463 1 9124 1 3760 2 13720 1 10128 2 10327 1 11530 1 9064 1 9536 1 447 1 12087 1 799 2 6801 5 11474 3 12866 1 14623 1 13967 1 13004 3 3147 1 13970 1 1709 1 11134 1 11411 1 2881 1 3720 1 14962 1 4698 1 1932 3 7230 2 13734 1 11202 1 67 3 16314 1 9353 1 16439 1 10079 1 9081 1 10941 1 7120 1 12811 1 6389 3 15753 6 5140 6 14972 1 9555 1 polyphosphoric_acid 8353 1 10906 1 common_land 1925 1 7072 1 13639 1 mediocre 7532 1 10363 1 13367 1 12392 1 6986 7 6300 1 12742 1 14626 1 3173 2 15848 1 11620 1 12268 1 14525 1 4198 2 8732 1 12929 1 7669 1 15765 1 7429 1 813 1 8314 1 6241 1 1595 4 6760 1 15159 1 4912 1 1844 1 16387 1 7181 1 11831 3 1368 1 7722 1 15662 1 5957 2 build_in 6011 1 7302 1 10234 1 125 1 12053 1 3816 1 10564 1 8582 1 9978 1 2114 1 14525 1 actuated 12492 1 11400 1 12721 1 13395 1 15436 1 8382 1 14052 1 3367 1 9219 2 13171 1 14626 1 12620 1 scrambled 9926 1 12718 1 5253 1 1014 1 11375 1 8338 2 1732 1 4532 1 10958 1 13736 1 6987 7 12785 4 13422 2 13720 1 1240 1 9064 1 7273 1 4412 1 7731 1 14215 1 11354 1 11106 1 14121 1 12131 1 465 1 591 1 13443 1 11771 1 6452 1 lingeringly 1924 1 6596 1 12817 1 12034 1 10492 1 5002 1 15058 1 11712 1 15459 1 1296 1 critic 3163 1 3834 1 6011 1 11289 1 15891 1 573 1 14052 1 1660 1 13418 1 1939 1 704 1 2697 1 12556 1 15195 1 7880 1 10278 1 11147 1 14457 1 3778 1 6135 1 5319 1 3779 1 5841 1 5778 1 4840 1 14127 1 9956 3 10485 2 960 1 3483 3 16156 1 1034 1 5214 1 2122 1 14199 1 6715 1 14585 1 9506 1 3107 1 1735 1 13829 2 12368 1 9830 2 12435 1 11702 1 10628 1 12643 1 14328 1 9902 1 8834 2 14592 1 13110 2 4603 1 11776 1 5788 1 9038 5 14941 1 13301 1 1604 1 9778 1 5102 2 8769 2 13696 1 2404 1 2646 1 483 1 3860 1 8108 1 6102 1 15967 1 9247 1 278 1 7649 1 9583 1 12003 1 3862 1 1607 2 2214 1 14812 1 13765 1 11455 1 6671 1 11584 1 13706 1 10578 1 7778 3 288 3 7468 1 4982 1 16117 1 12848 1 14088 1 3064 1 6988 40 7603 1 9189 1 13253 1 12663 2 16474 1 7099 2 6109 1 3280 1 12207 1 5415 1 10667 1 2873 1 7975 1 4159 1 7729 2 6620 1 6498 1 14233 1 13260 2 368 1 15441 2 4098 1 9400 1 1310 1 15568 3 11407 1 6500 2 12795 1 796 2 15445 1 679 1 7545 1 6564 2 10070 1 10262 1 10396 1 3820 1 10265 2 10333 1 7488 1 14363 1 8930 1 8137 1 7927 1 10077 1 4110 1 8615 1 14625 1 8192 1 4381 1 134 1 1982 1 11544 1 7996 1 13075 1 16202 1 7178 1 10465 1 813 2 10600 1 11681 1 9873 1 15312 1 12159 1 2767 1 12617 1 14969 1 12102 1 11282 3 12416 1 3234 1 954 1 11963 2 8946 1 144 1 6070 1 7751 1 16318 1 1656 1 4176 1 11490 1 10218 1 16144 1 5957 3 mermaid 9015 1 10442 1 volumetric_analysis 1836 1 4164 2 6328 1 4643 1 12020 3 16438 2 11403 2 15572 2 1389 2 12228 2 2620 1 tensely 8524 1 13982 1 outstretched 7541 2 7365 1 6230 1 927 1 4962 1 5858 1 7857 1 12439 3 3393 1 16378 1 3171 1 15897 1 1292 1 3825 1 5745 1 5929 1 3016 1 5356 1 7776 1 7026 1 7304 1 8193 1 siesta 3749 1 2976 1 14048 1 9274 1 barbecued 5972 1 11353 1 3112 1 3133 1 2241 1 4365 2 13706 1 conflict 7624 1 6011 1 7366 2 15629 1 15398 1 13551 1 3776 2 9013 1 1525 1 8881 2 2326 1 11147 1 9285 1 1943 1 16000 1 10899 2 9154 2 892 1 12630 1 3679 2 14719 1 4648 1 12636 2 15406 3 12637 1 527 2 9574 1 8167 1 905 1 1406 1 15908 1 13035 1 9833 1 8026 1 7522 1 3926 2 6033 1 2723 1 8029 1 8900 1 14728 1 11309 1 12775 1 13576 1 15152 1 5176 1 12776 1 10100 1 8039 2 15157 1 5914 1 6788 3 7649 1 7899 1 12002 1 2581 1 1289 1 14473 1 14738 1 1169 2 5918 1 8180 1 1053 1 16036 1 11849 1 924 1 15784 1 14338 1 14339 2 4087 1 5184 1 5803 1 6300 1 5187 4 2352 1 5412 1 546 5 12145 1 10664 1 5041 1 1570 1 5805 1 16189 1 3950 1 14232 1 1176 1 12790 1 7667 1 3136 1 15294 2 7413 2 5043 1 12669 2 3001 1 10125 1 4928 2 6685 1 15570 1 12674 1 15933 1 4688 1 12020 1 16055 1 2368 1 13722 2 10131 1 13965 1 1849 1 9996 1 11330 1 5431 1 11604 1 14099 1 14243 2 5686 1 9203 1 12534 2 4110 2 13214 1 2755 1 8323 1 5056 1 811 1 1580 1 7554 1 2099 2 5311 1 813 1 65 1 4700 1 10524 1 15814 1 7932 1 314 1 11749 1 14378 1 11205 1 4706 1 4707 1 14636 1 12813 1 5443 1 2385 1 7820 1 2114 1 13610 1 11210 1 15321 1 13490 2 11619 1 3320 1 15953 1 6707 2 15472 1 13091 1 14650 2 10837 1 11495 1 15833 1 5459 3 3493 3 15058 3 10424 1 3036 1 11769 1 9509 1 4466 1 16455 1 3183 1 472 1 5330 1 8831 2 5331 1 8833 1 93 1 11223 1 8834 2 13628 1 837 1 8104 1 14941 1 1604 1 11108 1 3741 1 14010 2 11512 1 11646 1 12064 1 12448 1 6727 1 8474 1 3054 1 1896 1 3345 1 14018 1 6234 1 4482 1 2920 1 601 1 16110 1 15499 1 3350 1 2520 1 12586 2 3202 1 13523 1 12458 1 7850 1 14948 2 10985 1 3204 1 13251 1 7210 1 14153 1 12721 1 6989 57 15729 1 5347 1 4743 1 1908 1 496 1 738 1 5240 1 7729 1 10868 1 6498 2 9398 2 9535 1 9662 1 15983 2 3646 1 10068 1 15877 1 15611 1 1635 1 3367 2 3886 1 13799 1 14557 1 8135 1 9937 1 10077 2 3223 1 13273 1 5888 2 510 2 7226 1 13274 1 4039 1 15886 5 3898 1 10598 2 11680 1 9144 1 13011 1 1136 1 11278 2 16494 1 4890 2 7012 1 11282 1 2824 1 10343 1 11686 2 16371 1 5766 1 12620 1 10081 1 indenture 6269 1 7740 1 8632 2 pivotal 2680 2 6168 2 1649 2 13214 4 11222 2 12178 2 8909 2 14925 2 14704 4 5412 2 sexually_attractive 2797 1 13171 1 13801 1 8914 1 scopolamine 15704 2 14623 1 15840 1 13967 1 7523 1 4303 2 12326 1 4263 1 9775 2 15710 1 14074 1 6990 7 11798 1 10253 1 11956 1 8342 2 4590 2 2945 1 15918 4 13910 2 12761 1 5478 2 12898 2 5527 2 3760 2 10143 1 9465 1 5366 2 7122 1 5407 2 dial 351 1 11901 1 2989 1 6732 2 16298 1 8156 1 4182 1 8238 1 11461 1 4523 1 15635 2 3515 1 3611 1 6991 23 7665 1 16237 1 2997 1 2148 1 5934 1 1062 1 4925 1 15362 1 3138 1 9021 2 11657 1 12430 1 14461 1 9705 1 14718 1 14927 1 7191 1 16355 1 676 1 14857 1 8576 1 5271 1 8246 1 14891 1 14892 1 13432 1 3918 1 11766 1 14270 1 6445 1 9606 1 938 1 14271 2 8438 2 4933 1 3592 1 1370 1 3079 1 10556 2 7218 1 8582 1 11197 1 12703 1 10632 1 62 1 14494 1 9672 1 9169 1 6228 1 15339 2 10206 1 4907 1 4882 1 16284 1 14005 1 5689 1 8716 1 8076 1 6230 1 13944 1 484 1 14009 1 10011 1 3504 1 11958 1 2581 2 1719 4 13606 1 3971 1 1749 2 1169 1 4172 1 8180 1 14015 3 7904 1 8393 1 3534 2 7393 1 163 1 14340 1 yardstick 1836 1 5268 1 15576 1 4164 1 1389 1 14052 1 mary_mccarthy 2797 1 5616 1 5500 1 1370 1 13334 1 8246 1 polar_coordinate 2928 1 3404 1 10018 1 2423 1 14044 1 7210 1 alertness 1331 1 3145 1 3113 1 7821 1 9400 1 11461 1 16494 1 14319 1 7122 1 15933 1 13684 1 7587 1 14805 1 15762 1 2316 1 14636 1 11355 1 rejection 1331 1 5443 1 14257 1 14259 1 8395 1 14709 3 12555 1 14977 1 9221 1 15195 1 15636 2 7374 1 2326 1 4951 2 6829 1 7239 1 13089 1 6082 1 4286 2 11080 1 4399 3 14925 1 14458 1 4591 1 13932 1 7189 1 15269 1 5271 1 3728 1 7135 1 2902 3 1091 1 7702 1 2055 1 13099 3 5849 1 8580 1 11917 1 10363 1 5785 2 3257 1 3926 2 719 1 10907 1 10097 1 4855 1 4907 1 15594 1 483 1 8108 1 14010 1 11512 2 920 1 2581 1 6294 1 3630 1 14738 1 8719 1 8180 1 6359 1 14225 1 2737 1 14147 1 3431 1 13050 5 6162 1 14946 1 12923 1 16114 1 4809 2 1492 1 3440 1 1232 1 2873 1 6992 22 4558 1 5878 1 4680 1 7729 1 1363 1 5621 1 6175 1 13260 1 1915 1 14616 1 1181 1 8438 1 869 1 2371 2 3145 1 11602 1 11674 1 6505 1 4823 1 5307 1 8870 1 3221 1 943 1 8323 2 7226 3 4699 1 6066 1 15886 1 15118 1 13075 1 8195 5 9874 1 10144 1 1656 1 12228 2 viper 1864 1 12691 3 3145 1 13726 1 12231 1 13004 1 884 1 1691 1 14664 1 4143 1 13836 1 1006 1 7574 1 2869 3 2560 1 14306 1 4829 1 11956 1 6927 1 6993 11 9210 1 5418 1 185 1 4099 1 3532 1 8021 1 7393 1 10131 1 850 2 5180 1 1140 1 13706 1 gracelessly 14626 1 12718 1 suite 6198 1 12731 1 543 1 3144 1 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15884 2 11063 1 11002 1 15029 2 2760 1 5311 2 16491 1 7429 1 3971 2 3231 2 3158 1 5366 1 4045 1 11282 1 8393 1 2953 2 14504 1 11686 1 10018 1 16143 1 16318 2 6894 1 darning 7837 1 14092 1 1076 1 5180 1 12231 1 mylodontidae 1457 1 freight_car 11462 3 10421 1 10834 3 6713 2 11706 1 12466 1 10751 4 1566 1 2392 1 14114 2 9219 1 6771 1 6958 1 9444 1 1893 1 10223 1 8419 1 15968 1 13804 1 6830 3 15572 1 12590 1 5089 1 10131 4 4129 1 5379 1 6643 1 7493 10 15028 1 accuracy 10182 1 9733 2 14052 1 1723 1 10247 1 14024 1 7912 1 1993 1 4950 1 15636 1 7757 1 4643 1 7661 1 3357 1 7785 1 5321 1 14578 1 16325 2 11188 1 1499 1 763 1 5268 1 3404 1 4925 2 9021 1 15864 1 2121 1 16190 1 6279 1 14464 1 616 1 1595 2 3788 1 2366 2 4792 4 126 1 5628 1 8523 1 6469 1 7421 1 8166 1 1635 1 8093 1 4164 3 13209 1 4728 2 529 2 13997 1 16392 1 10741 1 8582 1 3925 1 7488 1 11197 1 532 1 7387 1 14243 1 585 1 6186 1 13575 1 1547 1 13008 1 2881 1 7006 1 7455 1 14007 2 2425 1 536 1 7494 16 11172 1 5475 1 2857 1 14913 1 7148 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1301 1 15010 1 13711 4 2873 1 7667 1 44 3 15799 1 14233 1 12931 1 11327 5 1065 1 7671 1 4217 1 4437 1 2605 1 1574 1 15301 1 556 1 14759 1 13723 1 7421 1 4933 1 11876 2 10816 2 14493 1 559 1 15025 1 3457 1 3719 2 8192 2 1319 1 2377 2 11339 1 2882 1 13472 1 12943 1 4699 1 8936 2 9482 3 16065 1 14500 1 13973 1 812 2 8939 2 3158 1 4451 1 2888 1 4705 1 12416 1 14504 1 5694 1 9491 1 10018 1 2111 1 4944 2 15581 1 10824 1 14258 1 11073 1 6203 1 14259 4 4950 3 11906 1 5962 1 2117 1 13745 2 4951 3 13491 1 8456 1 9229 2 7697 1 5964 1 5449 1 7942 1 6211 1 3480 2 10544 1 11081 2 16082 1 15048 1 13987 1 8703 1 15831 1 4957 1 9232 1 1593 1 7189 4 15833 1 1595 1 15056 2 2902 3 3998 2 13752 2 4247 3 3494 1 11631 2 3182 1 4466 1 4732 1 3191 1 3192 1 15339 2 8220 1 11641 1 2646 1 12973 2 14010 1 2136 1 2135 2 8979 1 3744 3 1607 1 5479 1 595 1 16344 1 15079 1 6483 1 600 2 4740 1 3200 1 12719 1 856 1 5736 1 13251 1 4263 1 15089 7 11654 2 5236 2 12721 1 15359 2 6735 1 12205 1 120 2 121 1 4024 1 2148 1 5240 6 4751 1 1363 1 9801 1 11401 1 6498 1 10871 1 6497 6 1625 1 11406 2 14823 1 15105 1 1916 2 8997 1 12470 1 2159 1 11668 1 375 1 10070 1 869 1 1635 2 12732 2 13003 1 13795 2 13796 1 7487 1 2419 1 872 1 10077 1 6508 1 8739 1 5248 1 3223 2 3527 1 9546 2 1646 1 6254 1 7496 99 2945 2 9550 1 4041 1 6257 1 3532 1 14838 3 2691 1 1136 3 13283 1 12226 1 3234 1 5766 2 4515 1 1389 1 11426 1 12228 1 1141 1 7015 2 387 1 12229 1 1660 1 6525 2 11691 1 6267 1 1145 1 10611 2 9013 1 1395 1 15636 1 15637 1 4528 1 9285 1 3248 2 13824 1 8512 1 11150 1 6530 1 5015 1 5778 2 6024 1 3549 1 4531 2 13024 1 8517 1 6771 1 14323 2 897 1 13563 1 6533 1 641 1 14588 1 6535 1 10093 1 1406 3 9576 1 16391 1 2720 1 4536 1 6032 1 7768 1 11987 1 16165 1 6287 1 13575 1 15411 1 11452 2 12775 1 8533 1 16396 1 13576 2 7772 1 8769 1 11997 1 14078 1 11999 2 5278 1 2734 1 10643 1 9308 1 2214 1 15422 1 6542 1 5033 1 3265 1 15162 1 9587 2 2736 1 16177 1 10376 1 7260 6 16178 1 16179 1 6791 1 13581 1 13053 6 14086 2 12263 1 3273 1 15925 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13422 1 11693 1 1053 1 5254 1 15788 1 11600 1 5888 1 8654 1 6801 1 asplenium 11956 1 3404 2 4925 1 13783 1 12441 1 1014 1 8879 1 4291 1 11414 1 5361 2 commons 6913 1 905 1 4340 2 2404 1 3145 1 13035 1 11512 1 4155 1 704 1 8529 1 13096 1 1338 1 11825 1 1300 1 4272 1 3986 1 15637 1 13322 1 7389 1 15164 1 8603 1 12244 1 10899 1 canicula 11489 1 15665 1 66 1 fluorochrome 3676 1 15687 1 287 1 343 1 10155 1 2129 1 2312 1 10120 1 thoracic 5442 1 12719 1 13158 2 9849 1 8822 2 3096 1 15759 1 1300 1 7302 1 5347 3 6 1 13904 1 8427 1 5616 1 5965 1 15472 1 7474 2 1805 1 11865 1 10867 1 3404 1 14980 2 1874 2 14160 1 11085 1 6279 1 6310 1 7882 1 7133 1 12595 1 1667 1 12569 1 2815 1 13264 1 13504 1 3685 1 906 1 6689 1 5853 3 12342 1 8256 2 15381 1 7358 3 2822 1 7319 1 5756 1 590 1 4382 1 10464 1 14563 1 2857 1 10305 2 1986 1 8369 1 9618 1 7500 9 16494 1 16175 1 5366 1 9587 1 7120 1 14185 1 1328 1 5694 1 14047 1 muscicapidae 1457 1 hunt 10415 2 6011 2 15818 1 15582 1 11008 1 4641 1 11350 1 13821 1 885 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1924 2 1782 1 236 1 12342 1 6118 1 1314 2 16137 1 15114 1 12608 1 4698 1 11956 1 12537 1 6385 1 2760 2 185 1 5439 1 3381 1 8942 1 16258 1 13606 1 6635 1 7501 57 10755 1 12747 1 1262 1 4707 1 11686 1 14504 1 5514 2 13482 1 2551 1 11612 3 toward_the_sea 15170 1 8391 1 6530 1 12123 1 15584 1 7563 1 5347 1 12930 2 12733 1 16096 1 10131 1 8868 1 4446 1 2826 1 6448 1 genus_pilosella adoringly 9937 1 2939 1 2546 1 cheerfully 363 1 5877 2 10658 1 10321 1 484 1 7240 1 3094 1 16250 1 4099 1 933 1 10106 1 9165 1 12398 1 14237 1 11960 1 4901 1 14503 1 9038 1 7502 6 11677 1 4495 1 14109 1 bunker 4659 1 8093 1 9996 1 12362 1 4728 1 5900 1 309 1 12973 1 8047 1 10970 1 4216 1 2802 1 10223 1 2912 2 4803 1 1519 1 5994 1 12225 1 6830 1 3172 1 1357 1 14393 1 12206 1 6064 1 16318 1 2277 1 mullet 12123 1 1457 1 13728 1 5179 1 9635 1 3258 1 13935 2 14782 1 719 1 11259 1 6020 1 9285 1 11228 1 2089 1 3366 1 7868 1 13582 1 1403 1 4185 1 12148 1 9384 1 7503 7 13706 1 tocantins_river 6601 1 6175 2 8989 1 3164 1 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12352 1 7751 1 6390 1 15123 1 12620 2 2826 1 7014 1 1657 1 16144 1 prickly_heat 2797 1 3853 1 2581 1 4445 1 6102 1 7413 1 9873 1 10605 2 reciprocality 12725 1 2155 1 3290 1 bindweed 3760 2 2128 3 13970 1 11474 1 1095 2 4407 1 3623 1 8738 1 platelet 5442 1 5443 1 15725 1 10757 1 8156 1 1868 1 14025 1 4949 1 2865 1 4528 1 8309 1 14120 1 1119 1 2490 1 5240 2 16082 1 10253 1 3546 1 4925 4 13859 1 8890 1 8244 1 614 1 14030 1 6442 1 6467 1 15700 1 14618 1 16162 1 8580 1 9094 1 10330 1 3685 1 10492 1 8168 1 4933 1 9097 1 7803 14 14469 1 3258 3 531 1 11739 1 2642 1 7146 1 13837 1 15880 1 16026 1 14626 1 11312 1 9273 1 11678 1 14597 2 12943 3 1013 1 10782 1 13973 1 16203 1 8979 1 5279 1 14278 2 15493 4 7902 2 5033 8 5729 1 8180 1 14251 1 8449 1 15251 1 14504 1 13665 1 5694 14 9491 3 3435 1 12620 1 14190 1 1296 1 9920 1 shield_fern 14673 1 1560 1 15192 1 9680 1 13007 1 12862 1 1403 1 6785 1 1685 1 5173 2 6287 2 14972 1 7350 1 genus_linum 2335 1 1560 1 8012 1 15267 1 projective_test 13209 1 5689 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9639 3 2646 1 14010 1 5104 1 1892 2 1104 1 3863 2 5729 1 5981 1 8590 1 10047 1 12716 3 11517 1 10182 1 351 10 1903 12 15858 1 5120 1 2143 4 9650 5 10453 1 12075 1 4487 1 14287 3 15728 1 5347 1 611 1 7976 2 12854 1 7729 4 13531 2 7214 2 10869 1 6498 7 9662 1 8246 1 5131 1 10197 3 12088 2 8997 4 9664 4 2159 1 14956 1 6627 1 12342 11 15377 3 13402 3 2542 27 12476 1 12092 1 14558 1 15880 3 3223 3 12480 1 13805 1 13273 1 9415 1 10339 5 4041 1 6257 1 3900 6 13408 3 1651 2 11280 1 7120 1 14969 6 2693 1 6389 2 3534 1 9009 7 15252 1 10216 1 13665 9 12353 4 12228 1 sale 4283 1 15890 1 3388 1 7366 1 5007 1 16262 1 14052 1 14053 1 8153 1 9423 3 13419 1 1939 1 12622 1 1145 1 12885 2 9427 1 7238 1 7757 1 8750 4 1273 1 2703 1 11821 1 13930 1 16270 2 2332 1 9021 1 8886 2 10483 1 4530 1 4650 1 5271 1 1947 1 5527 1 7133 3 12635 3 6774 2 8021 1 11017 1 10770 6 13434 2 6536 2 10491 1 10627 1 12508 3 5785 1 9164 1 3113 1 5022 1 11709 1 8026 1 9716 2 15910 1 13570 1 7141 1 9717 3 1282 3 531 4 15913 2 9036 1 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4466 4 1737 1 7583 1 472 2 11635 1 15840 1 5597 1 13628 1 1475 1 12317 1 12059 7 719 1 13888 9 7587 1 15339 1 7714 1 1604 2 3859 1 14007 1 9639 2 2646 1 6102 1 484 8 10044 1 213 1 1893 1 12448 1 3506 1 11784 1 11236 1 15077 2 5608 1 9914 1 6105 1 13765 1 13380 1 9915 1 16469 1 12323 1 598 1 4365 1 13249 1 9920 1 6489 1 7969 1 1224 1 6854 1 4368 1 2655 1 10723 1 7851 1 4616 1 15089 5 4370 2 5236 2 9116 1 10456 6 6109 1 8381 1 4751 2 611 1 13390 1 5241 1 5621 2 1501 1 2305 2 16356 1 9662 1 11406 1 10195 1 14823 1 16483 2 14428 2 15740 1 2159 1 4029 1 16130 3 5995 1 9665 1 1635 1 12216 3 13795 2 14555 1 2419 1 2420 2 2422 1 10998 1 2686 1 2177 1 14835 1 1381 1 9943 1 1650 1 14838 2 5257 1 16494 2 1791 2 14570 1 2694 1 8876 1 14185 1 10343 1 9949 1 12352 1 6520 2 11425 1 1656 2 3238 1 lois 6688 1 15875 1 overweening 9509 2 4148 2 9541 2 6896 2 10363 2 5734 2 14913 2 1386 2 5097 2 3586 2 11738 2 8984 2 13767 2 4546 2 9038 2 5888 2 5451 2 2036 2 time_of_day 11735 1 475 1 9451 1 5307 1 62 1 14319 1 2680 1 13460 1 8428 1 12015 1 14885 1 11339 1 15965 1 7811 6 5178 1 13893 1 9601 1 12003 1 66 1 8576 1 4100 1 152 1 8349 1 14428 1 1535 1 10770 1 12163 1 11410 1 13104 1 alaudidae 1457 1 whet 11870 1 2697 1 14066 1 admitted 9882 1 12229 1 758 1 10825 1 7368 1 3242 1 1939 1 703 2 8954 2 2115 1 10950 1 14056 1 9821 1 6587 1 7372 1 11147 3 12887 1 3676 1 1943 1 10951 1 1999 1 2439 1 5586 1 5705 1 4186 1 5901 1 11495 1 9705 1 11153 2 8517 1 4718 1 13933 1 6715 1 13751 1 267 1 14 1 12569 3 10038 1 4250 1 4466 5 10363 2 529 2 7522 1 14469 1 4537 1 4731 1 15845 1 5024 1 3189 1 779 1 8100 1 5599 1 3858 1 1604 1 4970 4 8039 1 14600 1 211 1 5724 1 3126 1 2290 1 3744 1 12003 1 6980 2 3630 2 1169 1 12256 1 7593 2 9787 1 6670 1 7776 1 13848 1 11939 1 13312 1 12586 2 9184 1 15287 1 11793 1 39 1 7851 1 12011 1 7405 1 5412 1 15170 1 11728 1 10456 1 12464 1 362 2 13712 1 11188 1 4158 1 15294 1 10867 1 5128 1 1362 1 1839 2 3707 2 14682 1 6498 2 6929 3 16356 1 4928 1 3952 1 9194 1 14617 1 5941 1 12795 1 3882 1 13263 1 4991 1 15572 1 10131 2 15573 1 1632 1 4495 1 14829 1 10460 1 7350 1 13914 1 13209 1 12471 1 8673 1 2877 1 12341 1 6446 1 10137 2 11133 1 16195 1 2422 1 15306 1 5137 1 5573 1 10208 1 3895 1 13473 1 14307 1 10523 1 16491 1 4169 1 7812 34 10526 1 14634 1 6818 2 4705 1 3533 3 6892 1 2953 1 14776 1 11686 1 5635 1 7435 2 16144 2 clock_in 16261 1 10594 1 4653 1 3617 1 14979 1 15933 1 7811 1 475 1 7512 1 4180 1 enslaved 3531 1 1737 1 7729 1 13609 1 6787 2 8868 1 8254 1 6782 1 4539 1 5210 1 5849 1 535 1 7357 1 13491 1 7759 1 2770 1 stalemated 7944 1 15886 1 11686 1 3126 1 15790 1 13581 1 13274 2 huddler 2596 1 10702 1 1893 1 water_mill 8408 1 13552 1 14145 1 polio 10758 1 12906 1 12924 1 4180 1 13689 1 11308 1 547 1 427 1 7992 1 13268 1 8309 1 5838 1 7390 1 7945 2 11108 1 11866 1 13325 1 11512 1 5826 1 5228 1 16173 1 15932 1 8758 1 489 2 7121 1 6389 1 9009 1 3025 1 8408 1 6853 1 15124 1 7878 1 stuporous 4158 1 542 1 6713 1 4169 1 15750 1 5858 1 14546 1 6542 1 1066 1 16078 1 8180 1 3729 1 7238 2 779 1 2306 1 6969 1 5987 1 5694 1 8590 1 1916 1 3874 1 6292 1 6894 1 bode 8314 1 6011 1 6853 1 14009 1 5926 2 meditation 10448 1 5785 1 12523 1 5073 1 5148 1 5412 1 8738 1 14274 1 16078 1 2974 1 10249 1 11333 1 7374 2 15899 1 1421 1 14626 1 1099 1 7454 2 6903 1 10989 1 1176 1 5977 1 9581 1 4475 1 13075 1 3990 1 6003 1 14373 1 12669 1 7649 1 468 1 13011 1 16356 1 12334 1 10324 1 158 1 12674 1 8209 1 10144 1 1055 1 7813 7 1072 2 10701 1 7436 1 15574 1 chewing 573 1 4714 1 8693 1 1270 1 12109 1 3539 1 2256 1 13552 1 6340 1 14782 1 16080 1 12044 1 12821 1 5778 1 393 1 3992 1 5456 1 13096 1 6345 1 897 1 13029 1 6146 2 6717 1 1596 2 8093 3 5594 1 12643 1 10965 1 9512 1 10706 1 5718 1 8529 1 1472 2 9775 1 4143 1 11923 2 9303 1 11994 3 13576 1 11171 2 12186 1 11572 1 12578 1 11999 1 3937 1 2012 2 15548 1 922 1 8180 1 10375 1 15082 1 10656 1 10855 1 11386 1 12388 1 16179 1 9590 1 9648 1 13771 7 5344 1 354 2 11120 1 12981 1 8595 2 10790 1 13129 1 12326 1 4093 1 5347 1 9393 1 3754 1 2224 1 12723 2 11655 1 5616 1 7410 1 2873 1 5739 1 10866 1 12270 1 437 1 9057 1 15736 1 1770 2 1309 1 13718 3 5562 1 12998 1 9604 1 8317 1 11733 1 4030 3 15241 1 1707 2 12342 1 16431 1 6247 1 2679 1 562 1 4110 1 3719 1 7175 1 8678 3 9206 2 15028 1 15940 1 15120 1 13150 1 185 1 2247 1 7744 3 6635 1 16494 2 5828 1 15392 1 14504 1 4327 1 7816 27 12353 1 11612 3 12295 3 8948 1 amplify 10691 1 4740 1 9527 1 6013 1 6854 1 5833 1 13899 1 14747 1 11903 1 13552 2 16321 1 8377 1 4950 9 15759 1 14480 1 7661 1 5153 1 5347 1 13903 1 5584 2 6964 1 12014 1 3173 1 2046 1 10544 1 2960 1 6655 1 12889 1 15093 1 2466 1 6710 1 14819 1 12172 1 13875 1 12050 2 994 1 14092 1 14464 1 16049 1 4027 1 14953 1 12431 1 10092 2 13263 1 14828 1 9539 1 7800 4 7859 1 6375 1 14095 1 11833 2 11834 1 10492 1 15478 3 3685 1 4933 3 1782 2 15910 1 1602 1 3082 1 16435 1 14005 1 7006 1 7390 1 8076 2 484 1 1680 1 9872 1 7559 2 2581 3 7009 1 6294 1 9912 2 12005 1 3971 1 16494 1 6328 1 5366 1 2513 1 8449 1 1450 1 9082 1 6389 1 13344 1 11040 2 7815 19 seedless 16075 1 3200 2 8879 1 14532 1 13708 1 4775 1 10310 1 7158 1 7661 2 7159 1 14712 2 5449 2 12590 1 11353 2 2836 1 11215 1 4748 1 3444 1 5901 1 15329 1 15473 1 3843 1 15905 1 15267 2 14065 1 1621 1 7103 1 14616 1 7636 1 13878 1 8434 1 5298 1 9064 1 16358 1 1818 1 1432 2 15301 1 9238 3 11443 1 11022 1 505 1 12643 1 4728 1 8894 1 12438 1 10742 1 12181 1 14137 3 13885 1 7868 1 1510 1 13656 1 5361 1 8366 1 4698 1 15887 1 2581 1 3630 1 11177 1 13309 1 7120 1 11848 1 7904 2 15753 1 7814 11 3571 1 9555 1 ani 12072 1 5126 1 6480 1 15267 1 arsenide 10253 1 9285 1 1088 1 14831 2 4185 1 4261 2 electron_tube 5347 1 12431 1 1680 1 14819 1 9539 1 7815 1 9912 1 homopterous_insect 11827 1 7746 1 11636 1 4343 1 10487 1 13674 1 stretchable 13277 1 4944 1 10255 1 10273 1 14956 1 bum 10291 1 905 1 10363 1 9833 1 8026 1 5263 1 12011 1 607 1 9412 1 2424 1 15172 1 6465 1 14540 1 11999 1 11417 1 9892 1 1204 1 14682 1 2083 1 13408 1 152 1 12398 1 6534 1 4401 1 15933 1 5848 1 7484 2 2875 1 6520 1 6444 1 12054 1 13830 1 7817 6 sphenoid_bone 9550 1 4680 1 2263 1 6907 1 3964 2 13126 1 8977 1 16460 1 12981 2 3223 1 12214 1 9148 1 5616 1 10131 1 3580 1 7006 2 12600 1 7720 1 11474 1 13504 2 immunoglobulin 7656 1 2143 1 4368 1 12924 1 427 1 3169 1 6366 1 4399 1 2025 1 16327 2 9153 1 10120 1 2534 1 9859 1 11403 1 15441 1 4343 1 7636 1 6442 1 1428 1 768 1 12277 1 8021 1 11017 1 15572 1 8408 1 15674 1 4933 1 3113 1 13996 1 562 1 969 3 5434 1 3376 1 16026 1 2755 1 510 5 3084 5 9272 3 13444 1 3830 1 3744 1 7042 1 3863 1 5033 1 6232 1 16177 1 16405 5 14147 2 9728 1 8449 1 5694 2 13665 1 9491 1 8115 1 3571 1 12228 3 7818 9 disrepair 11406 1 12813 1 11843 1 9021 1 2042 3 11084 1 10800 1 7070 1 3223 1 13367 1 7189 1 12439 1 7009 1 2111 1 9920 1 ido 897 1 14018 1 4408 2 unchangeable 4158 1 6713 5 1305 1 7819 13 3679 1 3145 1 3967 1 16482 1 704 1 5810 3 1595 2 9166 3 12145 3 6969 1 16484 1 2833 1 9230 1 6369 3 family_zygnemataceae 1457 1 family_holocentridae 1457 1 inheritance 4283 1 15581 2 6897 1 15398 1 2255 1 7820 30 13417 1 14317 1 328 1 14318 1 7757 1 11819 1 8202 1 6707 2 5776 1 9891 1 3103 2 10956 1 830 1 14515 2 14650 1 8963 1 8517 1 16160 1 14585 1 9570 1 8209 1 267 1 2784 2 13171 1 13829 1 12569 6 8408 1 6283 1 527 1 8167 1 4659 1 905 2 4466 5 3497 1 8170 1 9242 1 4850 1 9298 3 3187 2 6909 1 2009 1 14803 1 1283 1 6784 1 4855 1 14941 1 8533 1 16462 1 15340 1 536 1 5544 1 12579 1 2509 1 11232 1 8177 1 11109 2 5279 1 6423 2 6424 1 11174 1 4203 1 16104 1 4360 1 11846 1 15494 1 4912 1 3054 1 8180 1 15162 1 10977 1 14018 1 1055 1 4423 1 11935 1 11789 1 2920 1 14946 1 2409 1 9920 1 10911 1 4367 1 13313 1 9390 1 2525 2 7210 1 12848 1 15089 2 14609 4 6168 1 12145 2 15974 1 15436 1 6989 1 5872 1 13323 1 5240 1 9926 1 4433 1 4680 1 10868 1 7413 2 12792 1 13260 1 1309 1 5810 1 12994 1 790 1 6685 1 1774 2 13965 1 15241 1 1924 1 12533 1 8069 2 9671 1 13541 1 12534 1 11882 1 687 1 14626 1 4502 1 1510 2 11273 1 1982 1 9415 1 13009 1 11002 1 11680 1 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4115 1 12034 1 15186 1 7558 1 8942 1 10528 1 8195 1 6330 1 7563 2 15579 1 4706 2 5317 1 10018 2 6580 1 12037 1 5443 1 7298 1 12548 2 1992 1 6077 1 15950 1 704 2 3982 1 2895 1 5696 1 9886 1 12041 1 11755 1 3169 1 4588 1 12042 1 956 2 8083 1 3986 1 8339 1 8203 1 9629 1 1459 1 11216 1 1873 1 2490 1 4955 1 2266 1 4840 1 11627 1 14929 1 15330 1 12962 1 13749 2 6089 1 15058 1 3493 1 13498 1 9235 1 3848 1 8211 82 1093 1 4002 1 14270 1 7314 1 10701 2 15836 1 2277 1 9898 1 3038 1 6835 2 8093 1 15479 1 9241 1 1737 2 3329 2 10165 1 16459 1 1470 1 93 1 8834 1 7317 1 12315 4 14000 1 10167 1 1097 1 9775 1 12704 1 4143 2 5221 2 10846 1 11924 3 14666 1 14941 1 12319 1 11229 1 11779 1 14667 1 14808 1 3341 1 11108 1 14277 1 2013 1 11235 1 11512 5 11646 1 13893 1 3744 2 3862 1 3197 1 6980 1 14527 4 1896 1 12838 1 14282 2 13765 1 1750 1 11785 1 1107 1 5862 2 2918 9 598 1 11936 1 2921 2 13897 1 2142 1 5609 2 11517 1 1901 1 5344 1 3350 5 4017 2 109 1 6984 1 1488 1 7095 5 4155 2 9793 2 4156 1 14948 1 12848 2 6106 1 2658 1 12327 1 1617 1 6864 1 9116 1 15090 3 8120 2 11796 4 7855 1 362 4 6369 1 5240 1 5739 1 12726 1 7213 1 4159 1 1362 3 10868 2 6372 1 2535 1 15734 1 10191 1 16482 2 12995 1 10324 2 11406 1 3644 1 2156 1 10196 1 2416 1 11529 1 9663 1 10328 2 12337 1 126 1 12213 2 11666 3 6375 2 3647 1 15877 1 14686 1 2160 1 3523 1 8491 1 1635 3 7734 1 505 2 10071 1 12602 1 10742 1 3886 1 15110 1 1507 1 2166 2 11268 1 10204 6 13801 1 9937 1 377 1 10077 1 4762 1 1007 1 12609 1 7868 2 9414 2 1510 1 2943 1 2171 1 379 1 5888 1 3084 4 6123 5 5508 1 8743 1 4998 1 10596 1 1649 1 1253 1 4169 1 15248 1 14838 4 7872 1 1136 1 10892 1 11278 3 16494 3 7012 2 5512 1 4511 1 16495 1 12102 1 11282 2 14444 1 6010 1 3903 1 11686 4 13411 1 6520 1 9010 1 10080 1 9421 2 12620 1 16373 1 suffocative 869 1 5778 1 3145 1 8309 1 13040 1 9074 1 6873 1 exhale 15085 1 1331 1 557 1 2254 1 5262 1 6731 1 11602 1 10660 1 12393 1 9451 1 12011 1 7585 1 5174 1 14904 1 12059 1 14243 1 3890 1 587 1 8322 1 7973 1 6171 1 119 3 1101 1 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12546 1 11346 1 6580 2 10532 1 15688 1 7068 2 1198 1 14111 1 15818 1 573 1 14507 1 10535 1 8199 1 1332 1 9359 1 11753 1 6337 1 8953 1 4335 1 9361 1 15824 1 7302 2 6207 1 15198 1 6081 2 10543 1 9228 1 10952 1 6963 1 4338 1 8341 1 12044 1 11081 1 8702 1 10421 1 2263 1 10836 1 10956 1 2268 1 3992 2 4957 1 1732 1 5214 1 12563 1 5971 1 7312 1 5088 1 4723 1 7313 1 471 1 3038 1 3182 1 1211 2 5594 2 4468 2 8462 1 8213 72 10965 1 11920 1 13626 1 9635 1 12315 2 16334 1 4352 1 16092 1 10295 1 7711 1 12575 1 10846 2 590 1 6477 1 9103 1 16463 1 12579 1 6977 1 12971 1 3051 1 8108 1 12973 1 8587 2 13118 2 5228 1 14012 1 14944 1 3744 1 100 1 16104 1 15077 1 4611 1 2407 4 3056 1 1357 2 4613 1 2921 1 601 1 9648 1 8116 1 12718 3 10983 1 4368 1 10310 1 7851 1 12326 1 2525 1 9258 1 4616 1 606 1 3354 1 15729 1 8731 1 3875 1 5873 1 5238 1 4749 1 4158 1 14681 1 4160 1 13785 1 2304 2 13908 2 10870 1 8486 4 6997 2 1624 1 11406 1 2306 1 1240 1 16484 1 3365 1 10879 1 10068 1 11535 1 2036 1 4164 1 10881 1 4030 1 8388 1 15987 1 6502 1 9667 1 1924 2 1925 1 5505 1 2312 1 1508 4 8870 1 7992 1 2421 3 13801 3 9937 1 13404 1 11413 1 5506 1 3655 2 3084 1 2426 1 2945 1 3661 1 11002 1 15247 1 16491 2 8875 1 7360 1 9416 1 12350 1 14838 1 11811 1 3228 1 3382 1 5257 1 4772 1 6389 1 11547 1 12227 1 15518 1 11686 1 145 1 7751 1 1792 1 13016 2 1140 1 6764 1 5140 1 14189 1 6641 1 2826 1 11287 1 rabbet 5442 1 1211 1 14118 1 13323 1 8980 2 7344 1 13736 1 mucopolysaccharide 13484 1 5443 2 11636 1 16213 1 10116 2 15636 1 4951 2 4855 2 3874 2 3894 1 6398 1 7006 2 13576 1 7946 1 4591 1 14599 1 6740 1 8662 1 3992 1 13150 2 6889 1 6498 2 7189 2 7344 1 6929 1 8180 1 10195 1 12416 2 14044 1 11424 3 5180 1 868 1 10678 1 untying 9506 1 12597 2 597 1 7565 1 2918 1 5055 1 14101 1 mony 102 1 15790 1 12059 1 equality_before_the_law 9203 1 9415 1 14077 1 15216 1 15974 1 5042 1 14 1 7462 1 12043 1 12569 2 10333 1 7031 1 13674 1 biconvex 3676 1 15120 1 6941 1 12733 1 10093 1 5180 1 11811 1 2466 1 soothsaying 2843 1 6081 2 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5089 1 5964 1 6655 1 6644 1 excitability 5443 1 2945 1 3241 1 11512 1 424 1 2720 1 4169 1 6497 1 4155 1 10220 1 4648 1 5313 1 531 2 8982 2 7133 2 12606 1 6924 1 5131 1 8218 5 14979 1 16185 1 10252 1 11831 1 3435 1 5616 1 13848 1 12620 1 molecular_weight 14816 2 6317 1 12231 1 10816 2 1490 1 15636 2 8219 6 2627 1 3223 2 4907 1 15964 1 13063 1 611 1 9153 2 9415 1 3404 1 15491 1 3285 1 14010 1 8939 1 13118 1 438 1 4169 1 9310 1 10374 1 9728 1 3684 1 10424 1 2495 1 14018 1 1357 1 8997 1 15572 1 12353 1 7733 1 11182 1 to_what_extent 15572 1 8702 1 2945 1 recline 9199 1 11978 1 15773 1 5745 1 9853 1 10951 1 4335 1 11939 1 family_corylaceae 13086 1 9634 1 10321 1 4994 1 4295 1 5173 1 hoarder 8512 1 13984 1 11819 1 484 1 sensationalism 3072 1 3032 1 15403 1 3145 1 15146 1 12858 1 1964 1 12810 1 7223 1 5319 1 13016 1 8838 1 6709 1 11997 1 esophagus 13051 1 12297 1 12298 1 13772 1 7368 1 1660 1 15629 1 15821 1 9849 1 12142 1 10660 1 8822 2 2862 1 14259 1 10579 1 12956 1 15759 1 9364 1 9794 1 6527 2 16082 1 2269 1 10387 1 4957 1 6497 1 15736 1 6310 1 8755 1 14582 1 8282 1 15056 1 10195 1 3552 1 1667 1 2089 1 2335 1 11631 1 1596 1 6283 1 1736 1 12338 1 8762 1 9768 1 11771 1 14956 1 10964 1 869 1 4438 1 6689 1 9833 1 5853 2 1438 1 8465 1 4142 1 12059 1 8256 1 532 1 7836 1 8220 21 12186 3 9970 1 7496 1 1351 1 11999 1 5475 1 7998 1 2012 14 13150 2 12915 1 2581 1 12580 1 5033 1 14634 1 8180 1 15079 1 9587 2 11895 1 12416 1 3386 2 4282 1 600 1 doliolidae 1457 1 kulanapan 13648 1 10070 1 quadraphony sure_enough 9830 1 573 1 9833 1 4759 1 6032 1 14494 1 14693 1 2326 1 406 1 7956 1 1547 1 7239 1 10952 1 5190 2 6655 1 15594 2 8193 1 5240 1 9021 1 484 1 4169 1 11048 1 10324 1 6145 1 443 3 15207 1 2089 1 10411 1 13517 1 8128 1 2920 1 14109 1 act_on 11023 1 12508 1 12718 1 9164 1 2989 1 13996 1 14023 1 6032 1 11903 1 10247 1 9477 1 2419 1 7911 1 13109 1 8029 1 4539 1 6924 1 6080 1 15636 1 12606 2 9116 2 13887 1 3065 2 6290 1 7239 1 9055 2 9761 1 3703 1 4338 1 8221 8 11081 1 12131 2 1787 1 591 1 6925 1 8391 1 9429 1 9006 1 8414 1 15957 2 8223 1 16419 1 6498 1 8314 1 14719 1 3721 2 2214 1 15590 1 7479 1 5608 2 101 1 11827 1 2846 1 6749 1 4994 1 2815 2 12620 2 1634 3 3038 1 6776 2 pacifistic 8764 1 15901 1 quota 3924 1 5717 4 5852 1 9451 1 1282 1 13352 1 1642 2 14905 1 4528 1 2170 1 8323 1 11553 1 15308 1 6767 1 536 2 9304 1 8222 6 14994 1 4925 1 7376 1 4680 1 14888 2 523 1 14233 1 6512 1 3506 1 4650 1 11662 1 11278 2 1180 1 11869 1 12068 1 15251 1 16484 1 2652 1 8779 5 11831 1 11666 1 9469 1 1792 1 genus_erodium homo_soloensis 11614 1 13878 1 13171 1 15301 1 12181 1 mimidae woolly_thistle 9186 1 9435 1 2921 1 13821 1 indiscretion 16436 1 4466 1 12471 1 3032 1 5685 1 13795 1 5312 1 846 1 4545 1 12574 1 6635 1 5498 1 16450 1 2155 1 11524 1 10846 1 15191 1 6263 1 8761 1 3320 3 11791 1 13712 1 autosome 1903 2 8309 2 6889 1 13320 1 868 1 11308 1 infrequently 12757 1 4529 1 16045 1 15011 1 12833 1 2332 2 5416 1 11082 1 16491 1 1501 1 10310 1 4335 1 11989 1 492 1 15434 1 10572 1 3375 1 8512 1 14525 1 12178 1 12033 1 2036 1 9920 1 gospel 12522 1 6200 1 12555 3 1229 6 6168 1 11728 1 2190 1 15729 1 4398 1 3726 1 13192 4 15761 4 7099 2 13583 1 10617 1 5321 1 1176 1 13856 1 4529 1 2332 2 15797 1 10123 1 10485 4 7729 1 7189 1 15137 1 13261 2 8891 1 2966 3 153 1 4688 1 9861 1 2056 2 902 1 12729 3 5945 1 4271 1 2161 1 10202 1 1185 1 58 1 8529 1 4761 1 8709 1 15616 1 9036 2 11227 1 779 1 14905 1 5200 1 15114 1 12478 1 8289 1 1099 1 1510 1 5913 1 8839 3 3084 1 12943 1 12537 1 15940 2 1166 1 15991 1 14374 1 13078 1 8224 21 6890 2 10340 1 14838 1 16494 3 4451 1 10144 1 11487 1 9107 1 12453 1 6070 1 1656 1 1861 3 5181 1 13482 1 15348 1 16144 1 whistling 6011 1 11430 1 12718 4 16350 1 11615 1 3636 1 9219 1 11859 1 8786 1 13821 1 4950 10 9794 1 13490 1 6211 1 9190 1 14291 2 6558 2 12209 1 10546 2 10769 1 4062 1 14585 1 15933 2 14391 6 7138 1 7800 3 1208 2 14270 1 6178 2 11535 1 10965 1 3113 1 14900 1 10265 1 1438 1 4761 1 12125 1 9720 1 13404 2 8031 2 5028 1 6186 1 12939 2 16029 1 4446 1 5202 1 16397 1 8470 3 8558 1 13042 1 14009 3 7998 1 1219 1 10340 1 13606 2 6542 3 8225 26 16314 1 12159 1 13082 1 13844 1 5034 1 11960 2 14739 1 6043 1 1054 1 11964 1 6894 1 601 1 11287 1 lucent 4433 1 3286 1 1738 1 13266 1 7711 1 11406 1 1290 1 11227 1 16142 1 3676 1 16028 1 11249 1 4088 1 electrical_system 1406 1 5705 1 14010 1 16438 1 1782 1 7378 1 14792 1 7738 1 9912 1 5033 1 9587 1 10495 1 9234 1 7260 1 15933 1 6244 1 13263 1 6736 1 13273 1 12470 1 11633 1 589 1 syphilis 9847 1 15283 3 12324 1 13158 1 15430 3 5412 1 12848 1 3170 1 1420 1 12588 1 1907 1 8309 3 13089 1 10796 4 2705 3 2873 1 10120 6 7541 1 6769 1 12270 2 5966 1 7729 1 3992 1 10800 1 12083 1 14616 3 9707 1 7636 1 3916 2 12308 2 10092 6 10288 1 9664 1 15301 1 10701 1 11985 1 3145 1 9635 1 3258 1 15378 1 13654 1 531 1 11308 3 778 1 13071 1 8739 1 16026 3 11104 1 2757 1 12512 1 11925 1 4938 3 14009 3 186 1 6890 1 14738 3 8226 22 8421 1 8180 1 5257 1 6455 1 10214 1 2693 1 2549 1 5766 1 4977 1 10605 1 head_over_heels 11646 1 4532 1 12644 1 4190 1 unused_to 1560 1 4529 1 3072 1 4707 1 12862 1 4335 1 1516 3 12178 1 12033 1 4567 1 13174 1 raw_wound 7421 1 12178 1 7836 1 yemeni 243 1 9420 1 2613 1 12848 1 depositories 9092 2 6109 1 3106 1 5347 1 7497 1 sumatra 9714 1 505 3 9634 1 4048 1 1755 1 13398 1 9635 1 9834 1 13799 1 4352 1 546 1 8738 1 15973 1 12786 1 8641 1 11693 1 7144 1 15746 2 5188 1 13071 1 2063 1 11000 1 11941 1 14196 1 3764 1 13583 1 2757 1 464 1 4829 1 10956 1 3992 1 15248 1 8243 1 5228 1 3532 1 539 1 15281 1 14584 1 159 1 1449 1 8227 10 13295 2 14841 3 9420 2 15272 1 284 1 9899 1 fantasize 14034 1 4595 1 10525 1 12556 1 15077 1 9937 1 9571 1 1413 1 16388 1 5250 1 1164 1 4891 1 2490 1 keratoscope 7421 1 10831 1 2862 1 3580 1 15552 1 10092 1 waitress 10415 1 5171 1 8975 1 9819 1 4774 1 8047 1 11709 1 9298 1 4352 1 9937 1 14319 1 13745 1 11395 1 6655 1 12578 1 991 1 6102 1 7230 1 484 2 8325 1 10465 2 9582 1 12563 1 8560 1 16483 1 11419 1 15933 3 267 1 15 1 4118 1 12023 1 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15857 1 5733 1 3636 5 1613 1 7851 2 4490 1 9393 1 5188 1 11123 1 1909 1 13582 1 13583 1 2662 1 6494 1 13712 1 7541 3 1913 1 3758 1 12669 1 8243 38 14159 1 5349 1 1426 4 6113 2 11595 13 9535 1 6442 1 3644 1 13648 1 1844 1 792 1 1238 1 4310 1 2306 2 12020 1 12280 1 2605 1 12862 2 7169 1 12863 2 13068 1 11474 1 13592 2 300 1 8998 1 4030 1 13398 1 1437 1 3013 2 5949 1 9135 2 7355 1 9672 1 12092 1 16061 1 9869 1 2755 2 4882 6 13656 1 2757 1 2243 1 14910 1 4829 2 13147 1 9680 1 7178 1 3302 2 1134 1 7744 1 1516 1 15461 1 16314 1 7501 1 12036 1 7746 1 11895 2 68 1 12812 1 12353 1 12546 1 1656 1 14189 1 14972 1 14638 1 10468 1 2111 1 neoplasm 10757 1 7803 1 14691 1 13837 1 4951 1 8309 1 7824 1 16026 1 6366 1 120 1 11080 1 13576 1 11312 1 3084 1 12578 1 5240 1 12943 1 4925 1 673 1 5436 1 15493 1 7189 1 3744 1 5104 1 8244 9 897 1 5033 3 7636 1 8180 1 1067 1 16177 1 8449 1 13099 1 9009 1 1328 1 8667 1 16162 1 5694 1 9491 2 14110 1 3942 2 magical_power 7712 1 3582 1 7226 1 14955 1 5307 1 inedible 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6 14271 1 3626 1 askew 14528 1 4590 1 4293 2 927 1 10131 1 3727 1 strengthening 13034 1 5372 1 3793 1 13728 1 16043 1 15305 1 7585 1 7692 1 12424 1 562 1 16269 1 13526 1 970 1 14525 1 6320 1 14458 1 9172 1 15093 1 7665 1 6321 1 7256 1 14855 1 14980 2 10321 1 15864 1 8718 1 7010 1 14236 1 12764 1 9506 1 11895 1 5681 1 5067 1 14 1 3267 1 9918 1 16408 1 11612 1 10491 1 8250 10 14956 1 sporadically 6102 1 fundamentalism 11684 1 7970 1 12165 1 13320 1 293 1 3204 1 mosul 15703 2 6403 2 5228 4 3164 2 9218 1 3189 1 3134 1 7012 2 6520 1 15301 1 10617 1 9266 2 15688 1 1982 2 4829 1 resigned 7969 1 15350 1 4944 1 4426 1 13315 1 15926 1 4156 1 4262 2 6017 1 13674 1 10791 1 4490 1 7604 2 2224 1 10899 1 6053 1 4529 1 392 1 7376 1 1362 2 7729 1 2360 1 3992 1 3483 1 14160 1 9532 1 16419 2 10926 1 15933 1 126 2 2814 1 10879 1 4793 1 7313 1 12215 2 12640 1 13468 1 12149 1 905 1 8251 12 5331 1 10333 1 8287 1 12153 1 4539 1 7491 1 12870 1 15071 2 5474 1 6230 3 15941 1 2734 1 11315 1 9247 1 5228 1 7360 1 2214 1 1893 1 13843 1 8195 1 14225 2 11749 1 12195 1 4805 1 2551 1 trillium 8252 5 6731 1 13967 1 3258 1 6287 1 13821 1 13458 1 10295 1 2436 1 9674 1 14458 1 6419 1 9599 1 12537 1 16083 2 6977 1 3301 1 5455 3 3992 1 16124 1 1893 1 11256 1 897 1 11088 1 3760 1 1583 1 5499 1 10326 1 1685 1 7350 1 7124 1 doorkeeper 5011 1 5941 1 3766 1 8376 1 ultra_vires 8397 1 7302 1 4466 1 3766 1 5176 1 financial_obligation 7366 1 4680 2 8253 5 12905 1 10646 1 7851 1 15405 1 1145 1 8028 1 4180 1 1180 1 7302 1 7757 1 622 1 12887 1 12416 1 5176 1 capture 6646 1 4394 1 9423 1 6898 1 252 2 11816 1 12624 2 8628 1 13422 1 3396 2 14455 1 11819 1 8881 1 3907 1 4896 1 5380 2 12629 1 6903 1 2560 1 6655 1 5646 2 9955 1 10485 1 14715 1 766 1 11557 1 13428 1 5385 3 8889 1 13935 1 6146 1 13434 1 13171 1 8166 1 10492 1 12643 1 2850 1 11025 1 5908 1 5654 2 9718 1 778 1 21 1 779 1 780 1 6415 1 7389 1 1549 1 4907 1 3933 1 2575 1 536 1 2577 1 3122 3 3692 1 16290 1 9444 1 14221 1 9971 3 14998 1 15281 1 12383 1 2346 4 3128 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1 14616 1 9400 1 7105 1 8246 1 10325 1 3816 1 14892 1 8436 1 15301 1 10068 2 9665 1 1920 4 8131 2 3367 1 9067 1 4761 2 13538 1 8869 1 9671 1 3223 3 14771 1 1646 1 5056 2 11807 1 11886 2 1982 2 4041 1 15459 1 14631 1 13408 1 1260 1 4631 1 16494 2 629 1 4707 1 13608 3 6956 1 14636 1 1140 1 5957 2 theism enquiry 8507 1 4143 1 5904 1 14864 1 12543 1 10343 1 12551 3 2881 1 7808 1 5176 1 french_bread 6762 1 cellulose_xanthate 2335 1 2662 1 11965 1 dicranales 261 1 13878 1 5901 1 14626 1 8529 1 6820 1 8366 1 beatty 1330 1 5005 1 573 1 8652 1 3094 1 8917 1 6474 1 2314 1 4143 1 12534 1 8083 1 12092 1 12609 1 3401 1 3875 1 334 1 14854 1 12444 1 14562 1 12410 1 3661 1 14563 1 16492 1 13076 1 8143 3 4279 1 4310 1 7121 1 2814 1 1684 1 11612 1 3038 2 13183 1 microscope_slide 16008 1 14855 1 343 1 2016 1 13002 1 outfield 4659 2 2915 1 3309 3 12522 3 6230 3 12973 1 1134 1 2910 3 6942 1 2802 4 13821 1 11133 3 16078 1 5458 1 16439 2 14693 3 11282 1 12874 1 8393 3 5448 3 10078 2 13467 1 12998 1 12569 2 8299 8 7255 3 globulin 8300 7 12732 1 9744 1 12509 3 942 2 10293 1 11308 1 12059 1 1602 1 14979 1 7824 1 2822 1 120 1 5616 1 6367 1 10544 1 10120 1 10891 1 16398 1 8859 1 3992 2 7413 2 13076 1 6888 1 10387 1 13150 1 5163 1 11403 1 15736 2 1463 1 10513 1 11327 1 9445 1 14066 1 1947 1 996 1 12277 1 8021 1 11017 1 2089 1 16276 1 5694 2 13665 3 5766 1 6535 1 8408 1 old_world_mistletoe 12537 1 5718 1 6801 2 11541 1 spectrometry 12416 1 mania 3509 1 12585 1 8301 6 3145 1 3113 1 5716 1 6525 1 7738 1 2115 1 15436 1 5099 1 9169 1 8739 1 4743 1 14231 3 16000 1 5888 1 11231 1 7391 1 11998 1 12529 1 9823 1 13177 2 5778 1 13150 1 7561 1 8180 2 11068 1 7169 1 2016 1 5669 1 emperor 6200 1 6704 1 15129 1 884 1 253 2 13552 2 12555 1 14317 1 13674 1 7184 1 1798 1 12424 2 7238 1 16268 1 15399 1 3986 1 13679 2 6134 1 3030 1 13289 1 7574 1 10899 1 10356 1 13930 1 4716 1 13229 1 7760 1 763 1 13618 1 5082 1 5586 1 13022 2 14980 1 14715 1 2266 1 14650 1 83 1 11 1 8517 2 8631 1 4595 1 6715 1 12962 1 7132 1 5847 2 8091 1 768 1 15058 1 3848 2 8523 1 11220 1 4002 2 10701 7 11769 1 10903 2 9370 1 3733 1 6836 1 4466 1 13505 2 7199 1 11836 1 8975 1 12643 1 16391 2 13035 1 9833 3 4140 1 8529 1 13572 1 1886 1 1216 1 5598 2 8175 2 15710 1 11924 6 12443 1 13039 1 7389 4 12244 3 11229 1 5913 1 5176 1 10233 1 14667 1 16398 2 5177 2 2404 1 6230 1 14600 1 8040 1 7457 1 9644 1 9782 1 1103 1 14738 2 15281 1 12383 1 10178 1 596 1 6232 1 2585 1 8845 3 4153 1 1055 1 8371 1 1685 1 1752 1 540 1 14604 1 11517 1 421 3 7466 1 7207 1 8302 53 6672 1 4426 1 7850 1 4616 1 8237 1 2077 1 7158 1 2865 2 5347 1 10119 2 13643 1 16189 1 2871 1 7161 1 5934 1 5617 1 10867 1 15294 2 5241 1 5042 1 10868 1 14351 1 4376 2 7055 1 16419 1 2931 1 13260 1 3002 1 12672 1 12671 1 3879 1 10195 1 792 1 10196 1 8384 1 12213 2 13722 1 9664 1 6375 1 1632 1 14094 1 2035 2 15574 3 557 1 3145 1 10071 1 11876 1 13968 1 11675 1 13798 1 8134 1 6449 1 13801 4 9937 3 4272 1 1642 1 2940 1 1644 1 3527 1 15115 1 5688 1 1982 1 4829 1 12871 1 15249 1 8942 6 14838 1 313 2 9212 2 141 1 4451 1 7012 1 4511 1 8816 1 5693 1 10944 1 15034 1 6192 1 10604 1 12228 1 11612 1 3976 1 15688 1 thematically 13228 1 1593 1 11457 1 chess_move 4466 1 14855 1 14256 3 5619 1 7997 1 5778 1 6230 1 3679 1 11512 1 6984 1 252 1 12905 1 8942 1 6890 1 10958 1 5148 1 16218 1 12059 1 12818 1 12053 1 1466 1 8393 1 9285 1 14270 1 4495 1 7383 1 12071 1 3766 1 536 1 13706 1 relic 5906 1 3436 1 8303 7 1213 1 2720 2 12392 1 10858 1 8612 1 10742 1 14690 1 4155 1 12181 1 3081 1 12554 2 12815 1 1096 1 1836 1 12786 1 16078 1 12705 1 779 1 5347 1 14002 1 12478 2 7239 1 7574 1 8512 1 1176 2 12666 1 612 1 7062 1 6003 1 4169 1 6560 1 15888 1 5257 1 15390 2 2953 2 15445 1 2797 1 2036 1 forgery 1714 1 3907 1 15864 1 14888 1 247 1 dumped 3349 1 5865 2 7643 2 12229 2 8304 12 1924 2 5373 1 10660 1 6131 2 7287 1 4155 2 15110 2 9451 1 11693 1 12818 1 11742 2 8412 1 6830 1 11954 1 5722 3 5250 2 13614 1 1087 2 13587 2 12579 1 15940 1 484 2 12429 2 9291 1 15981 1 5271 1 15077 2 12597 2 11750 2 14858 1 30 1 13722 1 15572 1 15164 1 6801 1 11733 2 7067 1 4865 3 14956 1 contralateral 1836 1 11504 1 4200 1 3065 1 10937 1 2335 1 5751 1 512 1 dismay 7945 1 5778 1 2546 1 4086 1 766 1 10970 1 6477 1 quadrillion 4529 1 2301 1 4925 1 2404 1 10119 1 7789 1 13606 1 14857 1 polyphone 4493 1 7260 1 15898 1 6186 1 5033 1 corpus_sternum 15940 1 10464 1 16417 1 146 1 11085 1 14047 1 4019 1 kitchen_garden 1596 1 13644 1 14030 1 10300 1 15184 1 cup_fungus 6442 1 1457 1 summoning 4726 1 2332 1 10900 1 8897 1 1199 1 11662 1 14261 1 8225 1 4043 1 13082 1 12795 1 12102 1 14391 2 15026 1 16128 1 6596 1 7800 2 14270 1 5202 1 9822 1 folks 14182 1 4539 1 3300 1 11869 1 12284 1 7239 1 11662 1 11807 1 13110 1 overcharge 4200 1 6479 1 11830 1 1716 1 westernmost 1773 1 12284 1 celtic_language 253 1 7158 1 5217 1 10070 1 16088 1 guncotton 2011 1 3793 1 13966 1 15473 1 302 1 12326 1 14546 1 389 1 14738 1 7805 1 1907 1 13089 1 8590 1 4220 1 12130 2 1881 1 discriminating 1211 1 14052 1 12586 1 10247 1 2697 1 13610 1 8738 1 12357 1 8306 7 14694 1 14367 1 12268 1 7509 1 16436 1 3072 1 14009 1 5662 2 3504 1 11495 1 6498 2 16329 1 10440 1 9400 1 8844 1 2140 1 10391 1 5849 1 1054 1 5531 1 10018 1 5180 1 15877 1 14271 1 defy 10492 1 15818 1 928 1 10333 2 15305 1 4732 1 5051 1 684 1 8305 6 4429 1 3739 1 1646 1 7496 1 5057 1 12410 1 3549 1 15248 1 5859 1 15832 1 13637 1 9176 1 3816 1 4707 1 7045 1 1611 1 4534 1 14361 1 9806 1 2016 1 14956 1 faithless 15479 1 4998 1 9937 1 4590 1 10376 1 2049 1 5312 2 9270 1 15123 1 5078 1 5400 1 hypercritical 6685 1 15987 1 14643 1 8729 1 12970 1 15901 1 6852 1 5654 1 11081 1 pfennig 14181 1 oblivious 4529 2 4944 2 11367 2 9470 1 12643 3 3061 2 3672 2 11774 1 12231 2 765 1 11646 2 13551 2 15657 1 3952 1 12145 1 622 1 14457 1 8307 8 4110 1 4247 1 10899 2 1951 1 3832 2 8769 1 7390 2 side_by_side 3350 1 1565 1 8592 1 9650 1 13054 1 8155 1 15230 3 2802 1 7912 1 8452 1 7210 1 2598 1 11819 1 8308 20 6830 1 13228 1 16414 1 3950 1 8381 1 611 1 5240 1 2490 1 5268 1 15929 1 7976 1 14980 1 14424 1 14160 2 13909 2 9659 1 6498 1 11823 1 5193 1 11327 1 8017 1 12856 1 11406 2 14267 1 6773 1 2902 1 8435 1 8021 1 9235 1 13433 1 10131 1 8128 1 16163 1 10702 1 10292 2 2308 1 9996 1 400 1 8636 1 12733 2 7218 1 13368 1 12439 1 8529 1 5173 1 2338 1 3822 1 4253 1 8932 1 4412 1 6123 1 9141 1 12943 1 12537 1 8292 1 8770 1 11002 1 6230 2 1014 3 16491 1 15459 1 6788 2 5228 1 13893 2 8534 1 3900 1 2613 1 66 1 1324 1 13736 1 4172 1 12006 1 1052 1 10143 1 6849 1 1291 1 11282 2 14044 2 9977 1 8269 1 2826 1 foreshadow 763 1 4523 1 2301 1 13283 1 1229 1 573 1 15034 1 2016 1 11312 1 15327 1 10514 1 inflectional 5896 1 9609 1 14010 2 866 1 5718 1 3776 1 6287 1 945 1 16494 1 10650 1 13648 1 4990 1 11205 1 1099 1 569 1 106 1 510 2 1459 1 contradictarily 9964 1 15194 1 recombination 7809 2 10453 1 16190 1 4855 2 groundmass 332 1 1088 2 12975 1 11349 1 10442 2 5505 1 13097 1 casting 15353 1 5984 1 1658 1 10824 1 13900 1 15318 1 12231 1 1225 1 13984 1 13552 1 12815 1 10541 1 16351 1 3439 1 2598 1 11820 1 10725 1 12957 1 3676 1 11394 1 7239 1 8511 1 8310 16 4338 1 11493 1 15588 1 2466 1 6739 1 2263 1 5016 1 14754 1 10226 1 7607 1 11404 1 12994 1 3812 1 14616 1 1180 1 8666 1 15933 1 15701 1 6282 1 9860 1 7139 1 471 1 9093 1 13882 2 7486 1 3732 1 11474 1 4464 3 179 1 375 1 7028 1 1211 1 8410 1 12508 1 5465 2 15541 1 3717 3 8611 2 1637 1 4352 1 10559 2 8613 1 7711 1 4664 2 623 1 8072 1 12909 1 971 1 9723 1 13008 2 8035 1 11572 1 7897 1 484 2 10369 1 5362 1 16104 1 9784 1 9728 1 15390 1 15555 1 6955 1 9448 1 13897 1 13286 1 6792 1 2826 1 taj_mahal 13801 1 13505 1 421 2 8302 1 2931 1 10944 1 5228 1 12244 1 9664 1 12181 1 9782 1 4829 1 15688 1 revisit 4155 1 15797 1 6655 1 9243 1 geological_formation 2961 2 4590 1 10511 1 2613 1 2214 1 5359 1 4031 1 6568 1 11406 1 14585 1 13801 1 4490 1 2680 1 1117 1 5745 1 6281 1 15518 1 14938 1 7929 1 13063 1 7321 1 12827 1 8311 5 2490 1 solar_constant 1635 1 5686 1 9429 2 13713 1 9456 1 15572 1 7218 1 7697 1 9759 1 6444 1 13286 1 maritimes 3760 1 15962 1 9727 1 505 1 2701 1 2263 1 13462 1 11871 1 12524 1 6535 1 13260 2 6890 1 3738 1 5990 2 9345 1 thunder 16261 1 15254 2 573 2 4332 1 12231 1 5638 1 3473 1 3164 1 10758 2 13673 1 7183 1 4950 6 4056 1 16078 2 11907 1 14319 1 11146 1 2326 4 6081 1 3676 2 12301 1 10697 1 11081 1 2490 1 11622 3 13092 1 6713 1 15403 1 12495 1 10485 1 7511 1 15136 4 9704 3 7189 1 7191 1 14323 1 14929 1 6344 1 8161 1 768 1 13360 1 14201 1 5781 1 6596 2 12569 1 13171 1 3791 1 7314 2 12434 1 9767 1 11701 1 12055 1 4659 2 581 1 9714 6 9831 1 3183 1 5594 1 12644 1 8213 1 5021 1 16165 1 9719 1 2132 1 7711 1 15648 1 3117 1 2341 1 9517 1 7587 2 10299 1 3048 1 16096 1 13301 1 4145 1 11712 2 5913 1 12971 1 11108 1 7960 1 14601 2 5228 1 4203 1 8534 1 9971 1 1681 1 1893 3 4912 1 4082 1 8421 1 8180 2 9380 1 5108 1 1053 1 13380 1 2586 1 7260 1 12068 5 6043 1 14411 1 2797 1 14021 1 284 1 601 1 13898 1 10448 1 5864 1 7598 3 12718 1 14675 1 8541 1 9982 1 1755 1 3636 1 9390 1 10660 1 15502 1 12011 1 8853 1 860 1 11589 1 8239 2 2598 1 9655 1 11797 1 11592 1 11125 2 12080 1 4814 1 2466 1 13712 1 9799 2 8313 35 7410 1 7788 1 2873 1 3072 1 7413 2 16047 1 6498 1 9398 1 11191 9 11662 2 16051 1 10514 1 11130 3 2306 1 6686 1 6747 1 12795 2 9860 1 374 2 13965 3 7486 1 6801 1 179 1 869 1 2674 1 4164 1 3077 1 12731 4 4933 3 1004 1 56 2 3218 1 10136 1 4499 1 3886 1 2419 1 14555 1 4761 1 13799 1 684 2 945 1 10075 1 8556 1 15456 1 2170 1 8872 1 15026 1 12094 1 12939 3 1130 1 13008 1 5251 1 877 1 13808 1 10465 1 9484 1 14838 2 7009 1 2691 2 13082 5 16494 2 6328 1 12416 1 15518 1 5694 1 11489 1 7436 2 10533 1 6894 1 2826 1 5956 1 11287 3 stole 3329 1 5117 1 927 2 11602 1 459 1 10453 1 2300 2 14259 1 13799 1 5505 1 6628 1 16117 1 15089 1 16308 1 12145 1 3906 1 1641 2 2753 1 10696 1 8322 1 4745 1 2866 1 10153 1 3764 1 7856 2 8312 8 842 1 14950 1 1766 1 6230 1 10485 2 13023 1 213 1 14502 1 12962 1 13623 1 101 2 11068 1 7260 1 7122 1 14828 1 13994 1 5882 2 6671 1 12620 1 6641 3 4069 2 399 2 anne_sullivan 15000 2 9340 1 7376 1 5210 1 10296 1 267 1 1631 1 8325 1 813 1 7990 1 2040 2 13110 1 11060 1 inexpressibly 13299 1 16182 1 13490 1 91 1 13411 1 8254 1 unemployed_person 5024 1 renal_lithiasis 3752 1 10867 1 12732 1 14428 1 10387 1 5694 1 7427 1 7002 1 5240 1 bankable 7969 1 13282 1 11227 1 9441 1 2694 1 13490 1 14057 1 14155 1 2324 1 10340 1 12995 1 androgen 4933 1 15918 1 3271 1 13973 1 13326 1 8595 2 4343 2 2802 1 2214 1 897 1 3188 1 11198 1 15215 1 14251 1 6227 1 2190 3 16196 1 1242 1 15251 1 7066 2 10521 1 11272 1 5776 1 4446 1 hysterically 12939 1 10815 1 6549 1 6853 1 antipathetic 9509 1 3834 1 12776 1 4529 1 2818 1 9882 1 16182 1 13416 1 6358 1 2415 1 10814 1 10013 1 12624 1 15195 1 8180 1 11684 1 3431 1 12534 2 16484 1 15784 1 8550 1 5920 1 5888 1 13338 1 13274 1 7390 1 intramuscularly 9415 1 10492 1 5878 1 151 1 15460 1 10503 1 614 1 531 3 14025 1 13321 1 14251 1 15251 1 9546 1 11851 1 13223 1 12511 1 9493 4 translucency 4017 1 3377 1 11329 1 2855 1 uriah 9502 1 15987 1 14149 1 10996 1 11753 1 6168 1 7211 1 9038 1 15046 1 blackbird 4529 1 4030 1 2999 1 3636 1 9174 1 9444 1 9601 1 14533 1 3258 1 8315 6 7379 1 16314 3 12785 1 3644 1 6018 1 13404 1 7251 1 5054 1 13583 2 12546 1 964 1 6801 3 15401 1 15763 2 childbearing 8869 1 8325 1 9599 1 indecisively 5528 1 15294 1 12643 1 1362 1 9882 1 3126 1 7812 1 10131 2 8607 1 9821 1 dodge 13275 2 12179 1 8153 2 7649 1 8582 1 11004 1 1893 1 4650 1 3231 2 3952 1 11486 1 16036 2 1642 1 13837 1 7529 1 8946 2 10068 1 5873 1 7785 1 8269 1 14637 1 dr 9550 1 10503 1 3539 1 7580 1 10775 1 14243 1 10313 1 12145 1 3559 1 9285 1 14 1 8644 1 174 1 12520 1 short_pants 7636 1 7774 1 11491 1 6375 1 7230 1 1561 1 9259 1 doorknob 3690 1 15743 1 7594 1 whiteflies 8620 1 9879 1 15625 1 3765 1 7744 2 2881 1 1671 1 genetics 6011 1 9494 1 5443 2 7504 3 13668 1 12299 1 13741 1 11075 1 15695 1 13423 1 12111 1 9015 1 6828 1 11147 1 2190 3 10542 1 10418 1 5449 1 5776 2 3322 1 14714 2 14126 1 16328 1 16005 1 3549 1 2120 1 16329 2 7189 1 2051 1 3997 2 6218 1 5849 2 2198 1 13991 2 1667 4 4722 1 13433 2 1879 1 13829 4 4723 1 9433 1 4250 1 6836 1 906 1 15478 1 905 1 4660 2 1737 1 8896 2 3113 1 5022 4 2448 1 6033 1 9298 3 8529 1 3258 1 3188 1 11308 1 3190 5 4539 3 5786 1 12707 1 13837 1 7587 2 4855 7 4354 2 4907 2 15964 4 13576 1 12578 1 155 1 9639 1 9041 1 6230 1 7527 2 14010 1 8907 2 5859 1 8718 4 16174 2 12713 2 10714 3 14280 4 3863 1 14738 1 5033 14 12006 3 9728 2 7904 1 3266 2 10978 4 12716 1 10047 1 5180 2 13519 1 7262 1 13581 2 11854 1 13849 1 351 6 12324 1 14816 1 1903 11 5120 1 9390 1 3133 1 14287 2 2525 2 6433 1 5412 2 12848 1 1836 1 12145 1 7661 1 4210 1 12266 1 3874 1 5240 1 4925 7 16478 1 673 1 16354 1 15235 1 14351 1 10869 2 7214 2 14159 1 16355 2 14616 13 935 1 8246 7 996 1 5131 1 10992 1 10197 1 12020 1 9860 3 8316 53 9337 1 14956 2 1635 1 800 1 4933 1 1075 1 16056 1 10265 1 15576 2 6627 2 4441 1 12342 3 13796 1 10333 1 2167 2 2680 1 1710 1 11739 3 12534 4 8932 1 9268 1 13214 1 10521 3 3223 6 12609 1 4502 1 2757 1 4224 1 1982 1 12943 1 7809 4 5689 1 15940 3 3022 1 12410 1 2320 1 6257 3 13150 1 11485 1 3900 25 13011 2 13736 1 16494 1 9147 1 14969 2 3534 1 15191 2 7748 5 13665 3 5891 1 12353 2 14047 1 11612 1 2111 1 drowsy 5443 1 16360 1 12315 1 5570 1 15010 1 4057 1 7507 1 12268 1 9779 1 9639 1 9021 1 10030 1 7342 1 13975 1 11327 1 1893 1 3629 1 5528 1 11959 1 12795 1 zionism 12471 1 1965 1 7970 2 12848 2 thermionic 8753 1 6090 2 1667 2 7030 2 1999 2 7623 2 13814 2 12848 1 combatant 5198 1 1362 1 2733 1 9632 1 5146 1 6928 1 9243 1 252 1 12923 1 12003 1 1065 1 3815 1 4401 1 7012 1 1818 1 10068 1 visual_impairment 8701 1 11678 1 8292 2 1213 1 15576 1 7189 2 10706 1 4661 1 14651 1 15736 1 4156 1 14557 2 8180 1 9775 1 3276 2 5828 1 6020 1 3676 1 12416 1 8667 1 924 2 7816 1 10093 1 3875 1 5180 3 13482 1 12388 1 6172 1 8317 7 put_down 543 1 6204 1 4335 2 8785 1 3803 1 10313 1 3675 1 6080 1 14787 1 5841 2 9955 1 4925 1 5244 1 16355 1 10513 1 12932 1 13434 1 8997 1 9433 1 15574 1 8318 8 11701 1 909 1 14329 1 13035 1 3686 1 13626 1 13795 1 14692 2 1473 1 8930 1 9203 1 13440 1 5570 1 6508 1 8494 1 1441 1 4113 1 2343 1 5176 1 14142 1 11108 1 1014 2 5914 1 11235 1 1556 1 1481 1 14221 1 16104 1 12159 1 2139 1 6818 1 3429 1 8983 1 11419 1 11686 2 2797 1 1656 2 9754 1 3238 1 horrible 6731 1 12532 1 13674 1 9693 1 1964 1 9887 1 6709 2 16237 1 12410 1 12249 2 3992 1 11512 1 15459 1 10800 1 5179 1 5545 1 486 1 3862 1 11748 3 12895 1 4172 1 13752 1 4511 1 9536 1 15537 1 16013 1 9768 1 too_bad 1784 1 2332 1 11749 1 2462 1 3533 1 12346 1 267 1 plaice 15547 1 10221 1 6638 1 close_to 6266 1 5897 1 758 1 9819 1 3389 1 12231 1 8155 1 5264 1 10223 1 1394 1 1799 1 7127 1 1395 1 3248 2 9285 1 7239 1 9563 1 6274 1 2438 1 16002 1 16003 1 9429 1 891 1 14855 1 8012 1 7376 1 9021 1 5778 1 15135 1 5648 1 13169 1 1534 1 7636 1 14585 2 4064 1 15642 1 8021 1 1536 1 13434 1 1818 5 6535 1 15769 1 8166 1 5906 1 905 2 8410 1 15908 1 10363 1 9833 1 3113 1 7385 2 8528 1 2722 1 9717 1 4075 1 3258 1 2972 1 1161 1 10905 1 7387 2 1949 1 10633 1 4412 1 8033 1 5793 1 6419 1 536 1 8292 1 10234 1 12778 1 9837 1 9971 1 2214 2 658 1 3429 1 2587 1 10376 1 924 1 13451 1 14741 1 1562 1 10502 1 15430 1 2596 1 11587 1 10660 1 12010 1 12786 2 12395 1 11728 1 7783 1 2466 1 7161 1 13857 1 44 2 4925 1 4680 1 673 1 7789 1 13717 1 3954 1 5564 1 15933 1 12020 1 10395 1 9863 1 7421 1 9996 2 8319 38 12284 1 16429 1 12403 1 9202 1 15023 1 11878 1 8067 1 3294 1 9869 1 8191 1 2755 1 15028 1 12537 1 16065 1 10140 1 15311 1 7559 1 16313 1 14501 1 2613 1 9875 1 16314 1 3305 1 2766 1 1859 1 8449 1 1860 1 3025 1 8563 1 10824 2 573 4 4332 1 12815 1 12041 2 11755 1 1725 1 6828 1 13745 1 6830 1 3320 1 10418 1 9228 1 7697 1 4129 1 10544 1 5839 1 11081 3 15048 1 15328 3 4590 1 2263 1 5214 1 6345 1 13360 1 10036 1 7581 1 5709 1 15058 1 3493 1 7310 1 8211 1 7312 2 13235 1 14270 1 3329 2 341 1 15591 1 9635 1 12439 1 2131 1 7711 2 11227 1 4734 1 16225 1 7587 1 14665 1 14666 1 14941 2 1604 2 14525 1 12973 1 10566 1 11646 1 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15016 1 5694 3 16276 1 5282 1 9664 1 6211 1 13264 1 4446 1 3641 2 unsporting 4164 1 323 1 3679 1 2581 1 13552 1 13422 1 9693 1 3082 1 4707 1 1230 1 4495 1 1495 1 10362 1 longitudinal 9494 1 4838 1 15523 1 15757 1 12815 1 13821 1 10541 1 5 1 11906 1 13086 1 9692 1 15759 1 4643 2 14782 1 11147 1 2625 4 9226 1 3676 1 6830 1 9428 1 9018 1 2560 1 7304 1 3481 1 8702 1 9021 1 13356 1 12892 1 2050 1 4531 1 9705 1 14718 1 8519 26 13935 1 1667 1 14658 2 10962 1 7025 2 1596 1 8408 1 15209 1 2204 1 9901 1 7140 1 15062 1 3556 1 3258 1 5173 1 13834 1 12318 1 534 2 15151 1 6419 2 8366 1 9639 2 11233 1 5179 1 7457 1 3195 1 14997 3 14014 1 11647 1 159 1 7904 1 12519 1 1685 1 9731 1 1296 1 8726 2 15353 1 12981 1 10310 1 9186 1 7658 2 6861 1 15230 1 4487 1 12326 2 3870 1 5412 1 7210 1 4263 2 12786 1 13058 1 14155 1 1761 1 931 1 15928 1 10319 2 16478 2 10321 1 13531 2 14424 1 10587 1 1428 1 10128 1 12596 2 5500 1 8435 2 2089 1 10131 1 7733 1 6876 2 13264 1 15574 1 2751 1 11474 1 4758 1 14956 1 7350 1 4030 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fugue 6336 1 11223 1 9245 6 5096 1 12181 1 1796 1 4761 1 12848 2 13674 2 12145 1 13214 1 3527 2 14333 1 3084 1 211 1 4202 1 11275 1 246 1 10513 2 10032 1 5678 1 11662 1 1180 1 12159 2 13283 1 3816 3 8407 1 compressed_air 14071 2 4944 2 14674 1 2988 1 13669 2 12231 1 9219 1 14193 1 1270 1 13552 1 12623 1 10760 1 9246 12 9558 1 3609 1 344 2 12094 1 3400 1 3320 1 7006 1 5873 2 1011 1 16237 1 12045 2 8193 1 5016 1 9801 1 2760 1 8939 1 6176 1 16008 1 2581 1 4323 1 3971 1 14163 1 3231 1 5729 1 14015 1 6328 1 2782 1 14428 1 15933 1 2953 1 10392 1 3730 1 6348 1 8269 1 6792 1 6894 1 2986 2 marrowbone 5106 1 4951 1 14949 1 peek 12424 1 4401 1 10234 1 5621 1 10952 1 12346 1 1968 2 10840 1 furuncle 8180 1 3051 1 11308 1 14066 1 buggy_whip 7992 1 4523 1 10357 1 7260 1 971 1 6981 1 11701 1 thuja 16314 1 11964 1 3416 1 quibble 9964 1 7098 1 7169 1 9430 2 8072 1 5084 1 2001 1 genus_piper 7471 1 12847 1 soliloquies 314 1 15987 1 3145 1 14810 1 4099 1 16321 1 3859 1 8914 1 subpopulation 3504 1 800 1 8156 1 11838 1 16278 2 15241 1 taking_hold 7541 1 8292 1 11233 1 2475 1 6228 1 3703 1 10223 1 13578 1 600 1 insider 5528 1 2431 2 5648 1 13888 2 8750 1 13833 2 2802 1 4029 1 12620 1 15077 1 330 1 bouffant 7068 1 11004 1 11681 1 2649 1 director 387 1 5372 1 1660 1 4774 1 14053 1 8153 1 3242 1 6017 1 13422 1 8881 1 4057 1 15637 1 10899 1 2195 1 4529 1 6530 1 392 1 2332 1 4186 1 3404 1 9021 1 10483 1 3679 1 8631 1 11826 1 4790 1 5528 1 8163 1 13684 1 11697 1 15271 2 10772 1 12640 1 905 1 8285 1 2852 1 11709 1 7522 1 13833 2 8529 1 6909 1 4539 1 646 1 5911 1 3559 1 1043 1 8903 1 8904 1 15915 1 13576 1 12131 1 10099 1 7390 2 16398 1 4200 1 11718 1 16174 1 9971 1 2214 2 13843 1 3801 1 659 1 1558 1 1051 1 15784 1 4425 1 5555 1 8047 1 424 1 13187 1 3701 1 12145 2 2224 1 7408 1 15437 1 432 1 14614 1 7056 1 16419 1 8796 1 15441 1 5810 1 3952 1 3815 1 50 1 15933 1 12023 1 10259 1 15301 1 7676 1 4689 1 16425 1 13468 1 16426 1 10809 2 11330 1 10265 1 10814 1 62 1 4695 1 4110 3 9071 1 6319 1 3153 2 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1 33 1 10598 1 5271 1 onomatopoetic 5442 1 13779 1 1014 1 1907 1 10430 1 13076 1 5790 1 15459 2 928 1 12979 1 4950 1 11287 1 sauteed 7625 1 5262 2 2019 1 8338 2 10310 2 12264 1 7658 2 14259 1 5264 1 13422 2 2258 3 7273 1 2489 1 14882 1 12590 1 4986 2 3875 1 6654 1 16045 2 8923 1 7668 4 5967 1 10387 1 16047 2 789 3 14265 2 1770 1 15672 1 4103 1 10959 1 2564 1 2089 1 16358 4 7731 1 15769 1 5972 4 13965 2 11022 1 12768 1 14859 6 9162 4 15451 1 6777 1 16487 1 14209 1 15062 2 13728 1 13999 1 5718 1 9998 5 4471 1 779 3 10002 4 13888 1 11029 1 5792 1 2134 1 1932 1 12942 1 11375 2 7230 1 5179 1 11746 1 10176 1 6387 2 9248 20 67 1 10272 1 7120 4 5035 1 1357 1 6609 2 5798 1 3699 2 echinococcosis 9219 1 13479 1 13609 1 14699 1 2986 1 4024 1 5240 1 jack_bean 3992 1 7262 1 idiographic 5841 1 reporting 15287 1 12421 1 11971 1 9984 2 15436 1 5319 1 3875 1 10899 1 15046 1 6369 1 16270 1 8732 1 1501 1 13561 1 5561 1 14233 1 14462 1 15833 1 14465 1 4900 1 12932 1 15207 4 14891 1 13329 1 4189 1 14828 1 13863 1 6801 1 15574 1 1211 1 529 2 3924 1 4537 1 7353 1 7488 1 7585 1 16195 1 12969 1 1283 1 14940 1 5887 1 7039 1 11927 1 5279 1 1650 1 13735 1 9249 10 5918 1 16494 1 1291 1 16177 1 14969 1 15518 1 6955 1 5982 1 164 1 6853 2 10981 1 15663 1 coagulation_factor 5442 1 15430 1 9298 1 6318 2 5686 1 13089 1 7427 1 5240 2 12943 2 11299 1 5878 1 13973 3 10782 1 5279 1 10387 1 921 1 6889 1 12539 1 11485 1 7902 1 14236 1 5729 1 9250 8 8180 1 4990 1 6969 1 868 1 1415 1 7934 1 eldritch 9910 1 6549 1 56 1 14555 1 15833 1 6143 1 14236 1 13800 1 1238 1 3676 1 14645 1 14405 1 877 1 15155 1 3671 1 beyond_question 6089 1 7567 1 7593 1 10598 1 1595 1 polyploid 3190 2 4925 2 5849 2 5022 2 8316 2 3900 2 5033 4 14616 2 9920 1 numerator 12868 8 1770 1 4956 7 5733 1 9251 7 263 10 squall 3136 1 4780 1 15579 2 4812 2 11602 2 1385 2 8272 2 15123 1 1050 1 2922 1 7210 1 foliaged 3760 1 6018 1 8171 1 12882 1 symmetry 15940 1 12643 2 91 1 10812 2 14975 1 10741 1 6318 1 2059 1 15089 1 14164 1 10295 1 4143 1 9722 1 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1 5906 1 2971 2 802 1 10704 1 15840 1 9635 2 8493 1 10556 1 5173 1 3258 6 6287 1 5307 1 2312 1 8765 1 872 1 5396 1 1709 1 12968 1 4143 1 15648 1 1042 1 6692 1 6785 1 8678 1 3501 1 9103 1 14370 1 14562 1 4542 1 6977 2 10464 1 13634 1 16438 1 4887 1 655 1 7900 2 16465 2 3532 1 12873 1 8774 1 5231 1 6635 1 5257 1 9945 2 7904 5 12518 3 15753 1 9216 1 11964 1 883 1 14972 1 ash_tree 3644 1 5295 1 1667 1 15267 1 15753 1 7218 1 1604 1 9398 1 13736 1 10372 1 9598 1 15594 1 myelomeningocele 12585 1 2138 1 15439 1 1769 1 5766 1 7615 1 7474 2 lecanora 11956 1 4468 1 9210 1 12852 1 15840 1 14921 1 13759 1 14107 1 1596 1 2653 1 11612 1 12847 1 whipping 6030 1 387 1 6334 1 572 1 5071 1 3218 1 12141 1 9256 10 8853 1 7210 1 3983 1 12785 1 3676 1 7051 1 4882 1 13301 1 3703 1 13461 1 11188 1 3798 1 9305 1 8938 1 2546 1 15459 1 15905 1 2511 1 9532 1 14810 1 7999 1 10176 1 13761 1 1607 1 8863 2 10407 1 1957 1 9766 1 4103 1 14892 1 6244 1 8537 1 5317 1 12862 1 10677 1 8490 1 revengeful 7630 1 12727 1 12181 1 12500 1 genus_juglans warning_light 1893 1 9206 1 raise_hell 1180 1 13474 1 15818 1 14838 1 8072 1 7042 1 11287 1 mercenary 8745 2 1145 1 783 1 10904 1 14236 1 2608 1 6774 1 719 1 3082 1 2694 1 13490 1 10879 1 1374 1 1656 1 12729 1 7786 1 14956 1 sashay 15117 1 5897 1 14002 1 15279 1 9046 1 8156 1 12977 1 4879 1 11133 1 acadia 15962 1 9727 1 505 1 2701 1 2263 1 11871 1 12524 1 13260 1 3738 1 5990 1 9345 1 nagasaki 2263 1 11728 1 16100 1 10335 1 13921 1 1134 1 15339 1 11508 1 5228 1 12609 1 10701 1 8724 1 13020 1 293 1 slug 34 1 13740 1 15818 1 387 1 15353 1 11431 1 10149 1 7657 2 6201 3 16471 1 2300 1 4641 1 4487 1 9258 15 7210 1 1963 1 15695 1 6080 2 5347 1 2529 3 9700 1 1275 1 5644 1 7072 1 2873 1 13712 1 7541 1 2263 1 1204 1 13935 2 14585 1 372 1 16087 1 2306 4 13878 2 10424 1 10066 1 9860 1 13434 1 11631 1 7058 1 4464 1 3181 1 14934 1 12731 1 6116 1 801 1 12643 1 8213 1 13004 1 9067 1 4961 1 8531 1 10774 1 9138 1 11228 1 3377 1 13273 1 3764 1 10004 1 9437 1 11312 2 6419 1 4698 1 11925 1 8716 1 14499 1 16101 1 2321 1 2581 1 7744 1 1104 1 6542 1 3230 1 16493 1 4803 1 14015 1 1053 1 9587 1 13515 1 11894 1 7122 1 15251 1 3386 1 13982 1 12228 1 crooked 12231 1 9922 1 3164 1 9186 1 1961 2 2698 1 9257 13 4488 1 10541 1 329 1 13086 1 12786 1 2598 1 12557 1 4185 1 1999 1 3677 1 7667 1 6656 1 3406 1 866 1 6438 1 5046 1 9959 1 8606 1 1534 1 10325 2 373 2 5297 1 1667 2 14658 1 13171 1 6535 1 12675 2 12570 1 7169 1 6801 1 13567 1 7350 1 2906 1 4293 1 3145 2 3524 1 2164 1 9635 1 11709 1 10556 1 14555 1 10559 1 872 1 8901 1 8766 1 6509 2 11171 3 877 1 3626 1 6949 1 15459 1 8875 2 8180 1 5257 1 1055 1 2043 1 articulately 9964 1 12556 1 4019 1 knee 7365 1 13669 1 758 1 5518 1 252 1 12752 1 3395 1 15759 7 1799 1 14979 1 3676 1 9563 1 7239 1 2045 1 3398 1 12755 1 5157 1 9018 1 14458 1 2332 2 3546 1 6770 1 15404 1 1532 1 14715 1 12633 1 16008 1 7762 1 1534 2 8892 1 7636 2 11827 3 2444 11 13684 1 641 1 14 1 8523 1 4533 1 902 1 7025 2 13171 3 10772 1 12369 1 10491 1 13567 5 10362 1 906 7 400 1 8285 1 528 1 7140 1 10628 1 13939 1 12645 1 4191 1 11307 5 10629 1 2852 1 8026 1 530 1 10775 1 1042 1 915 1 534 2 277 2 7039 1 8769 1 536 1 7256 1 7774 1 11999 1 2857 1 8040 1 2214 1 16403 1 658 6 4912 1 4300 1 7393 1 5282 1 3435 1 7776 1 1296 1 13454 1 5923 1 927 2 2862 1 10503 1 1961 1 10380 1 289 1 15788 1 2223 1 5926 1 1568 1 7158 1 8545 1 13323 1 1424 1 13063 4 12268 1 13711 1 12017 1 1176 1 931 2 7667 1 6925 1 7541 20 13196 1 3136 2 11189 1 673 1 13589 1 14485 1 14888 3 4215 2 8925 1 9601 1 11327 1 1309 1 6442 1 14757 1 12795 1 8435 1 16055 1 9860 1 12796 1 1072 1 7419 1 1575 4 8438 2 7421 1 6059 1 3592 1 1313 1 10932 2 5948 1 15678 1 12534 1 806 1 15184 1 10268 1 14905 1 11742 1 9869 3 1317 1 451 1 10682 1 14101 2 16435 1 9076 1 12033 1 2882 1 15028 1 12943 2 8683 3 14500 1 4227 1 10938 1 8942 1 2613 1 11343 1 1191 1 13736 1 13737 1 9490 1 4707 1 2381 1 5694 1 1861 1 2251 1 7068 1 11491 2 1331 1 5959 1 15690 2 13224 2 10693 1 8693 1 8822 3 2114 1 6462 1 4335 6 6585 2 12815 1 16078 2 4951 1 9228 1 12959 1 7942 1 15472 1 5965 1 6708 1 15588 2 14121 1 7304 1 958 1 4955 1 4590 1 828 1 4591 3 5967 1 4957 2 5214 2 14929 1 15056 1 8972 1 2394 2 4723 1 201 1 2277 1 9899 2 4464 1 10292 1 9241 3 16457 1 10965 1 8977 1 10430 1 2500 1 8708 1 12439 1 7585 1 16092 1 6226 3 2130 2 10295 1 718 2 4143 4 1216 1 7712 1 4353 1 7956 1 971 1 14525 1 3501 2 5474 2 11108 1 15073 1 6230 3 5978 1 10305 1 11110 2 8980 2 10442 1 2648 1 11381 1 14281 1 13895 1 12978 1 8849 1 9918 2 3346 1 2920 1 8851 1 1110 1 12980 2 9527 1 15500 1 12718 2 12842 1 16112 1 8592 1 3750 1 8234 1 6364 1 1225 1 112 1 2655 1 10453 1 12077 1 8853 2 15355 1 7210 1 9259 103 9116 1 13526 2 3874 1 14679 1 5616 2 9799 1 5618 2 6739 1 4160 2 866 1 14160 1 7344 3 10873 2 8246 1 7980 1 11257 1 13395 2 2671 1 9127 1 12727 1 13000 1 6375 6 13143 1 7486 1 14432 3 14172 1 1370 4 12472 1 1924 5 14555 1 8737 1 4761 1 8134 1 2312 1 1129 2 6629 1 15381 1 2939 1 9546 2 11677 1 13656 2 1131 1 11678 2 9812 1 2426 1 15245 1 9550 3 12871 1 1382 2 13150 1 4169 7 10752 2 16258 1 11004 3 13659 1 10340 1 14838 1 7872 1 2182 1 14312 1 1519 1 2548 2 11282 1 13812 1 9148 1 5766 1 11424 1 3536 1 12353 1 14047 1 9816 2 6644 1 overhead_projector adversaries 12508 1 792 1 3793 1 11796 1 213 1 7014 1 2912 1 grounding 12400 1 8209 1 3284 1 9143 1 10800 1 8122 1 10741 1 8192 1 201 1 5412 1 11133 1 antiquity 7398 1 505 1 15407 2 2236 1 14174 1 6825 1 12848 1 11693 1 7238 1 5506 1 5347 1 5126 1 5221 1 9260 7 14196 1 6121 1 13583 1 9457 1 16151 2 4026 1 5083 1 8325 1 8686 1 7178 1 5046 1 14163 1 2950 1 1560 1 13360 1 10286 1 10240 1 7904 1 15301 1 1685 2 13264 1 11474 1 8581 1 circuit_court_of_appeals 5586 1 4466 1 2680 1 16478 1 3907 1 7858 1 13956 1 6498 1 4907 1 6900 1 3875 1 590 1 foul_ball 2915 1 12973 1 massachusetts 5442 1 8334 2 3164 4 884 3 16321 3 13821 2 4523 1 15759 3 11147 1 8083 2 14644 1 3247 1 6830 1 10481 1 195 1 2263 2 15404 1 9956 1 2391 1 3992 1 3483 1 11 1 12896 2 10033 1 13878 1 267 1 8021 2 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1 10442 1 15790 1 5033 1 7210 1 5340 1 5366 1 373 1 3676 1 13767 1 3560 1 6419 1 9493 1 pteropogon 3513 1 3760 1 16350 1 5849 1 3223 1 447 1 15037 1 10886 1 2927 1 childhood 4391 1 5442 1 6011 1 8506 1 14314 1 7366 1 13741 1 10474 1 3981 1 14780 1 9425 1 14318 1 5148 1 13674 2 3675 1 11146 1 14979 1 13490 1 8512 1 1086 1 15587 1 15264 1 763 2 2263 1 9021 2 5966 1 1148 2 7377 1 6277 1 14580 1 1811 1 9368 23 7635 1 13360 1 4901 2 5971 2 8523 1 8761 1 4724 1 9371 1 14798 1 4466 1 12643 1 11635 1 9097 1 13627 2 8286 1 9298 1 3258 1 13574 1 7711 1 4539 1 11227 1 16166 1 13837 1 15544 1 5791 1 15339 1 1741 1 14941 1 15413 1 5913 1 14525 1 8769 1 3626 1 11713 2 2011 1 12378 2 8717 1 13698 1 5660 1 7088 1 657 1 9644 1 9176 1 8180 4 16178 1 1833 1 9977 2 3059 2 601 1 8726 1 981 1 9527 1 12324 1 15725 1 219 1 13900 1 4155 1 10380 2 7724 1 605 1 1963 1 3753 1 6167 1 1171 1 7660 1 7159 1 5487 1 8309 1 3070 1 3582 2 6172 1 13712 2 12270 1 4558 1 5877 1 7729 2 1969 1 7789 1 14352 1 12995 1 14236 2 5498 1 15570 1 13395 1 4989 1 14491 1 2036 1 16248 1 15987 1 3145 1 2164 1 3886 1 13799 1 6247 1 7925 1 9671 1 4110 1 5308 1 7427 1 3527 2 12480 1 8192 1 13656 1 3019 1 5252 1 6323 1 12943 1 15186 1 13809 1 5953 1 11002 1 2179 1 13973 1 10598 1 3664 1 15187 1 13150 1 14440 1 1788 1 3303 2 10211 1 11485 1 6890 1 13736 1 15888 2 12101 2 5366 1 3669 1 13608 1 11424 1 10532 1 12038 1 2111 2 water_hyacinth 3086 1 12138 1 13900 1 3215 1 1845 1 15412 1 11196 1 12467 1 5573 1 setaria_italica 8886 1 3354 1 12717 1 15267 1 rogers 6109 1 10318 2 family_psilotaceae 10457 1 14499 1 7159 1 13878 1 8879 1 2789 1 15446 1 15753 1 5449 1 9894 1 14212 1 dissuasive 10603 2 9465 2 7762 2 16318 2 12620 2 3382 2 clubhouse 14026 1 14525 1 10751 1 8047 1 14913 1 heartbeat 1793 1 15467 1 12392 1 14412 1 4156 1 6924 1 3276 1 4951 1 9285 1 13904 1 9599 1 2329 1 6770 1 5778 1 14612 1 2121 1 4530 3 13427 1 8517 1 6499 1 14464 1 3362 1 9465 1 5500 1 14428 1 998 1 15875 1 9369 10 15907 1 9371 1 15539 1 1004 1 13799 1 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2783 1 13029 1 12308 1 13099 1 2335 1 1596 1 9369 1 9371 43 4659 1 7707 1 9830 2 16164 1 10492 1 1406 1 15909 1 12904 1 5718 2 8286 1 13037 1 4964 1 13110 1 779 1 8645 3 16396 2 13576 1 12512 1 13116 2 8769 1 99 1 2577 1 11108 1 8717 1 15853 1 2734 1 10970 1 3744 1 6232 1 8180 1 9587 1 15079 2 13380 1 3346 1 8115 2 13581 2 9920 2 7598 1 981 1 12391 1 4156 1 6433 1 10116 2 1963 1 7158 1 6924 1 5188 1 1301 1 15436 1 8309 1 4372 1 8380 1 7539 1 10867 4 11400 1 9801 1 15235 1 7413 2 14951 1 6498 1 6497 1 10510 1 5350 1 6310 1 14821 1 11327 1 16356 1 9535 1 13648 1 10195 1 15513 1 3882 1 230 1 12795 2 3364 1 11872 1 15673 1 10067 2 15572 1 6178 1 800 1 376 1 5566 1 1004 1 16248 1 12732 2 1183 1 15987 4 56 1 1128 1 3145 2 10265 1 15576 1 10741 1 10333 1 7990 1 5307 1 7925 1 1372 1 11739 1 11543 1 4881 1 7358 1 15681 1 7427 1 3527 2 5888 1 9811 1 135 1 12943 2 5689 1 2945 1 13973 1 5311 1 8325 1 13150 1 15459 1 16438 1 10011 1 5000 2 1582 2 3532 1 16205 2 6760 7 16494 3 6330 1 10215 1 7122 1 1450 3 10943 1 12102 1 9009 2 12953 1 5694 2 5766 2 11424 3 4636 2 12620 1 13016 1 13482 1 1453 1 10605 1 2111 1 ventolin 9007 1 5608 1 14251 1 5366 1 2641 1 15251 1 3386 1 9798 1 11045 1 10131 1 9920 1 salmon 11614 1 1331 2 6334 1 7367 1 3310 1 8337 1 1660 1 1457 1 3473 1 15948 1 884 2 3164 4 16321 1 10539 1 13422 1 5898 2 11819 1 14782 2 2190 1 7239 1 5776 1 4185 1 14924 1 5779 1 15404 1 11300 1 14650 2 3992 1 4647 2 5387 1 469 1 11217 1 897 1 13935 2 11302 1 3413 1 13434 1 1596 5 8408 1 14270 1 12768 1 9372 31 5171 1 12123 1 10965 2 15062 1 10704 1 5096 1 4352 1 5466 2 3258 3 3189 1 12059 1 14274 1 13112 2 12968 1 12705 1 7388 1 6841 2 5722 1 2788 1 3423 1 8645 1 8104 1 2134 1 4296 1 6977 1 12833 1 12380 1 5179 5 11646 1 7457 1 14145 1 12450 1 4481 1 12978 1 15920 1 16178 1 2860 1 5798 1 11119 1 8426 2 16182 1 9050 2 9186 1 4155 1 1963 2 12785 1 3871 5 11123 1 6169 1 6869 2 15011 2 5295 1 673 1 8923 1 12669 1 932 1 16124 1 789 1 1770 1 6871 1 11259 2 2605 1 2089 7 10067 1 9467 3 13965 1 7350 1 179 1 8438 1 1635 1 4438 1 6378 1 14173 1 3219 2 8138 1 3295 2 4696 2 15381 1 4110 1 11270 1 13542 1 7868 6 9206 1 7007 1 16311 7 15940 2 1513 1 7230 1 3463 1 7179 1 6387 1 311 1 16367 1 12873 1 16314 1 10145 1 68 1 5764 1 4707 1 12546 1 5140 1 11612 1 7014 1 rh_negative 4283 1 1635 1 758 1 11078 1 779 1 969 1 12091 1 15339 1 14941 1 11312 2 14680 1 1763 1 6494 1 5240 3 1769 1 11823 1 2843 1 11327 1 1067 3 9353 1 10195 1 15190 1 12795 1 12102 1 5694 6 11601 1 predict 7582 1 9373 14 13237 1 4847 1 8026 1 13795 1 9717 5 7210 1 15089 1 8361 1 547 1 15695 1 7238 1 10097 1 6319 1 4907 1 16028 1 7697 1 14482 1 2532 1 5323 1 892 1 10698 2 11760 1 6230 1 14351 1 14352 1 3483 1 8262 1 7578 1 7899 1 13078 1 13011 1 14323 2 6499 1 7102 1 9535 1 14739 1 10343 1 599 1 5018 1 9606 1 clipper 16286 1 2904 1 15251 1 3145 1 15445 1 10735 1 10431 1 13705 1 6574 1 14317 1 15342 1 1096 1 impingement 6593 1 6287 1 10097 1 6801 1 717 1 8550 1 14236 1 bowing 15094 1 10492 1 422 1 4178 1 14009 1 13314 1 11902 1 15459 1 8852 1 14975 1 9566 1 4335 1 7762 1 15672 1 11155 1 16078 1 2767 1 14979 1 13895 1 4762 1 15752 1 6171 1 14108 1 14101 3 6925 1 vertical_fin 8595 1 13911 1 4470 1 9644 1 1041 1 13935 2 7636 1 7865 1 13099 1 11228 1 2089 2 3366 1 8661 1 2836 1 7436 1 11612 1 10719 1 phonology 4932 1 11517 1 10911 1 9494 1 7398 1 9219 2 1408 2 4156 1 13574 1 9374 8 4950 2 7034 1 6186 1 4429 1 1099 2 15667 1 8108 2 1969 1 7729 1 812 2 866 1 3483 2 15459 1 9416 1 1049 1 2051 1 13648 1 1562 1 7859 1 legislation 1330 1 3388 1 6648 1 1660 1 4774 1 2254 1 15692 1 8153 1 1145 1 13552 1 12818 2 11147 1 7374 2 14642 1 9224 1 9285 1 9563 1 3611 1 10899 2 15901 1 6655 1 13492 2 892 1 14855 1 6530 2 4340 1 2263 1 7073 1 5901 1 2963 1 830 2 15329 1 14515 1 15831 1 11495 1 1533 1 14719 1 5385 1 9089 1 14199 1 5271 1 8088 1 7133 1 14585 1 13683 1 4843 1 8408 1 201 1 8829 1 3038 2 4250 1 8167 1 906 1 4466 2 12643 1 9242 1 11505 1 7832 1 3113 2 5534 1 16392 1 1470 1 3926 1 15910 1 1282 1 5654 1 9375 38 4539 1 5655 1 780 1 9303 1 8903 1 723 1 5176 1 2404 1 4475 1 2646 1 14010 1 6978 1 1169 2 14015 1 16177 1 10376 1 8297 1 6483 1 2407 1 13246 1 1054 1 9179 1 31 1 11935 1 7966 1 13897 1 5668 1 13706 1 12585 1 5183 1 421 1 5922 1 15972 1 8376 2 12391 1 12524 1 6794 1 14875 1 4155 1 5736 1 7851 1 5926 1 3064 1 7210 1 6166 1 11121 1 5346 1 7158 1 12721 1 11395 1 6108 1 496 1 6170 1 16475 1 6798 1 12464 1 5240 2 4925 1 8382 1 5128 1 12466 3 7476 2 7729 1 6620 1 6373 1 6498 1 11327 2 2083 1 14236 1 3815 1 1180 2 996 2 1629 2 9663 1 15933 1 14489 1 12796 1 2815 1 10068 1 13914 1 11875 1 1635 1 12216 1 4933 2 7802 1 15987 2 2877 1 8870 1 4694 1 9203 1 8069 2 14246 3 14495 1 4110 3 14905 1 13214 1 16063 1 508 1 14626 1 15115 1 11339 2 3766 1 13338 2 2426 1 4576 1 10340 1 13011 1 1650 1 11896 1 14570 2 6518 1 14185 1 6009 1 10343 2 11686 2 8946 1 12954 1 1990 1 11687 1 15123 1 1657 1 pythagoras 14387 1 9830 1 5395 1 16494 1 16455 1 9728 1 5901 2 7831 1 5628 1 670 1 11087 1 4761 1 9033 1 12082 1 8573 1 vulnerably 12862 1 5877 1 9176 1 1627 1 pansies 1560 1 3760 1 5455 1 8021 3 11017 1 9922 1 14159 1 8259 1 7360 1 1403 1 6287 1 t_cell 7421 2 8712 1 7740 1 969 2 2143 2 6389 1 9833 2 6884 1 7066 1 9009 3 8456 1 9055 2 15022 1 4343 1 5033 6 9707 4 ud 421 1 12524 1 1470 1 8211 1 11229 1 5271 1 pointed_arch 15887 2 1134 2 7344 1 1893 1 12614 1 9376 5 13895 2 11421 1 4001 1 11873 1 10131 2 877 1 8547 1 12578 1 phone_number 1145 1 15590 1 11662 1 15888 1 7992 1 8209 1 6220 1 795 3 2846 2 3527 1 2593 1 12346 1 4446 2 2016 1 6053 1 13104 1 disabilities 11523 1 14438 1 2903 1 14 1 4528 1 1225 1 9546 1 8408 1 13408 1 14271 1 1136 1 twirlingly 14647 1 9021 1 arrive 6011 2 387 1 14052 1 5897 1 8747 1 884 1 1939 1 147 1 13820 2 14453 2 9821 1 11693 1 12885 1 13423 1 14319 2 7238 1 11147 3 14575 1 4643 1 15898 2 14979 1 4057 1 7507 1 2703 1 15762 4 8512 3 3401 2 6655 2 11821 2 3677 1 9822 1 12361 1 14197 1 4529 2 891 1 6530 1 12759 3 3404 1 5778 1 7376 1 9021 12 1148 1 3679 1 12898 2 1947 1 152 1 7134 1 5528 1 14585 1 2443 2 396 1 7764 1 12638 1 13684 2 267 2 2846 1 4792 1 14 1 14587 2 4654 1 902 2 10772 1 10625 1 1541 2 6660 1 9829 1 4658 1 4659 1 10492 1 9964 1 10363 2 5171 1 12643 2 15539 2 5785 1 3113 2 4406 1 9717 2 6287 3 779 1 11030 1 11309 1 2854 1 15277 1 6292 1 2574 1 8769 1 6419 1 14994 1 7526 1 918 1 5031 2 7649 3 13305 1 9443 1 7148 1 2214 3 3568 1 15423 1 13701 1 9839 2 16294 1 11849 1 12260 1 7653 1 11320 1 7776 1 14873 1 2349 1 2986 1 10244 1 1565 1 7048 1 4426 1 13315 1 12392 1 6298 2 15561 1 14605 1 6794 1 14343 1 10789 1 16043 1 10503 1 10380 2 15925 1 12011 7 5187 2 4093 2 5412 1 5927 2 14609 1 6168 1 6048 1 11728 1 2353 1 12787 1 7537 1 13461 2 8055 1 15795 2 4211 4 2466 1 2997 1 6053 1 16300 1 7410 1 10253 1 6557 1 4925 3 15797 1 14612 1 10123 1 12669 1 1063 1 11327 1 6929 2 440 1 171 1 9330 1 11192 1 1428 1 15570 1 10926 1 7796 1 12795 3 15933 4 5426 1 10392 1 15445 3 15572 1 6444 1 15301 1 8801 1 13965 3 11472 1 7170 1 299 1 1074 1 3143 2 56 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5228 2 11646 1 4384 1 2885 1 14838 1 9176 1 3630 1 7010 1 3231 1 14702 2 4279 2 11749 5 7012 2 489 2 11239 1 11686 1 2797 1 6670 1 5180 1 9816 1 7596 2 2986 1 7722 1 14604 2 5957 2 get_dressed 5528 1 5689 1 10881 1 10901 1 12795 1 12102 1 295 1 8055 1 13866 1 egypt 15892 1 15398 1 884 5 13419 1 2697 1 13821 1 3396 2 4523 1 8881 2 2326 3 3907 1 6019 2 9285 1 5012 1 7239 2 15134 2 7629 1 520 1 14059 2 2560 2 13930 1 4646 1 891 1 4529 1 2332 1 3546 1 2561 1 5778 2 11152 2 4287 1 15267 1 16156 1 14065 1 2052 1 14584 3 4401 1 5528 1 768 2 12899 1 16010 1 11017 1 11441 1 13171 1 14068 1 6535 1 12369 1 905 1 12123 3 11836 1 9833 1 15910 2 8529 1 6289 1 778 1 7035 1 535 2 649 1 15412 1 15914 1 10638 1 5913 1 2729 6 5176 1 11714 1 10909 4 16398 1 11716 1 7774 1 7257 1 8040 2 6788 1 16290 1 922 1 14219 1 7775 2 2214 1 14738 1 1169 2 7528 1 14223 4 1560 1 159 8 16036 1 4086 1 9046 1 1685 1 13312 1 10245 1 2861 2 6672 2 9186 3 11859 2 8918 1 7910 1 5187 2 3134 1 1836 2 1837 1 1300 1 7158 1 3135 1 11728 1 12787 1 5413 1 1965 1 13854 1 10918 1 8055 1 293 3 11188 1 8057 1 5560 2 15294 1 4925 1 45 1 7412 1 7279 2 11049 1 5680 1 6686 2 5426 1 12020 1 15445 1 447 1 10131 1 15301 4 16303 1 1576 3 802 1 12284 1 16305 1 58 1 8805 1 1314 1 11333 1 4110 1 9614 81 11742 2 11743 1 14626 1 4446 2 11481 3 14911 1 3961 1 1982 2 4829 4 12412 1 5438 1 13079 2 8942 5 14104 1 16205 1 8811 1 12293 1 3972 1 4706 1 187 3 10944 1 15034 4 9356 1 3975 1 4235 1 15688 2 5580 1 5442 1 16447 1 11900 1 4714 1 3164 1 11349 1 12555 1 16321 1 11212 2 13490 1 11619 1 13089 1 7574 8 13228 1 7697 1 10953 1 11215 8 7304 1 5841 2 8458 1 8702 1 2263 1 12171 1 3176 1 3992 3 15473 1 14650 1 5213 3 4957 1 468 1 3728 1 6715 1 6345 1 1595 1 9506 2 10286 4 3179 1 8211 1 9507 1 7314 1 14797 1 13104 1 6835 3 6836 1 1737 2 4728 1 8214 1 9243 1 15591 1 10555 1 3738 1 4143 1 7712 1 12705 2 5221 2 11924 1 971 1 723 1 15847 1 12578 1 8106 1 11232 1 484 1 3742 1 211 1 4358 1 11512 1 5228 4 3506 2 14145 2 12713 1 14527 9 11381 1 975 1 595 1 7845 1 13380 2 13895 1 14410 2 1357 1 1900 1 11516 1 11387 1 11517 1 9648 2 15229 4 12455 2 15599 5 1755 3 8596 1 4155 1 7095 1 16117 1 2526 3 12848 1 13777 3 6864 1 2659 1 12327 1 5126 1 5347 4 14155 1 11123 1 15976 1 5873 1 13645 1 11656 1 12726 1 865 1 8991 2 7729 1 8482 1 8484 1 226 2 13261 1 7103 1 13648 5 16127 1 11803 1 8997 2 9664 5 13000 1 14686 1 2036 1 10070 1 10994 3 13536 1 13144 1 5750 1 5998 3 8254 1 16251 1 7992 1 10204 1 13801 3 9937 1 3763 2 6382 1 10335 2 3896 1 3084 1 10337 2 9004 5 9415 1 11002 4 10465 1 13150 1 14565 1 4384 1 14838 2 8502 1 7010 3 4172 1 3232 5 1651 1 14042 1 6635 1 9945 1 16494 1 5002 1 7012 17 4511 1 16495 1 13284 1 14188 1 6390 1 9420 2 14047 1 1656 2 14972 1 16373 1 6644 1 hypertrophied 6739 1 5942 1 13099 1 14352 1 2335 1 11753 1 2229 1 5702 1 5033 1 5240 1 brucellosis 10796 1 15656 1 16350 1 11854 1 11925 1 sparta 9182 1 3329 2 9833 1 1470 2 11859 1 8156 1 3738 1 6825 1 7081 2 14317 1 12848 1 11212 1 8881 2 5221 4 5347 1 5929 1 2327 1 16365 1 9615 13 15861 1 9748 1 293 2 4698 1 762 1 7773 1 15294 2 6356 2 12726 1 14698 1 9021 1 1766 1 10868 3 8040 1 14650 1 11512 1 3126 2 8942 3 8891 1 12563 1 14527 6 9974 1 7012 1 14444 1 12213 1 13284 1 4425 1 8491 1 forever 4393 1 13128 1 9359 1 7909 1 14779 1 9497 1 4808 2 12041 7 13674 1 15089 1 14455 1 1301 1 10152 1 15601 1 10153 2 6367 1 4157 1 3278 1 12268 1 13324 1 3582 2 7856 1 11943 2 9598 1 9926 1 4558 1 12759 1 9021 3 10255 1 8515 1 13428 1 10840 1 3954 1 5458 1 14237 1 16422 1 5679 1 5812 1 13171 1 15301 1 12368 1 8128 1 6876 1 15907 2 529 1 9297 3 9407 1 4693 2 15409 2 16165 2 13110 2 8137 1 10075 1 377 1 3117 1 2284 1 10886 1 14941 1 9616 17 15594 1 7390 1 11544 1 7774 1 12971 1 14500 1 16312 2 5476 1 4701 2 12002 2 213 1 15077 1 5064 1 15462 1 12036 1 16495 1 6010 1 729 2 699 2 13452 1 11584 1 6195 1 16144 1 ratiocinative 9583 1 13552 1 12638 1 14018 1 traverser 11174 1 14585 1 15311 1 12718 1 2613 1 contrary_to_fact 1595 1 family_name 13794 1 1466 1 10863 1 13310 1 14010 2 3042 1 5830 1 14009 1 7729 1 866 1 3483 1 12244 1 174 2 10380 2 8585 1 8942 1 3084 1 5271 2 7210 1 house_of_commons 12471 1 4340 1 3136 1 4680 1 15886 2 10619 1 11999 1 7729 1 6200 4 9617 9 12524 2 15386 2 8517 1 12181 1 11209 3 5271 1 9203 2 16494 1 14026 1 11147 6 14570 2 3159 1 16484 1 1646 1 10773 1 14525 1 2035 6 3720 1 ultimacy 6011 1 9527 1 3729 1 2945 2 9021 1 10097 1 4110 1 14352 1 8203 1 14339 1 genus_cariama 15475 1 10221 1 6426 1 italian_bread 7731 1 13260 1 9946 1 13965 1 12147 1 9237 1 demagnetize 6008 1 8666 1 6830 1 7824 1 14809 1 13626 1 4261 1 15130 1 10559 2 9134 1 2467 1 engulfed 14314 1 4221 1 704 1 16321 1 10351 1 759 1 7711 1 14319 1 14979 1 3676 1 739 1 2703 1 16436 3 4433 1 11108 1 3741 1 12274 1 2415 1 16008 1 13449 1 11406 1 834 1 4387 1 4388 1 6261 1 15933 2 15572 1 13171 1 3920 2 neon 4668 1 10421 1 7257 1 1469 1 8939 1 8541 1 4169 1 14374 1 10556 1 9087 1 14221 1 7009 1 9728 1 11596 1 11054 1 9467 1 1999 2 12620 1 13793 1 4464 1 3867 1 dyspnea 9798 1 12943 1 11110 1 11045 1 9199 1 1984 1 15404 1 2338 1 1672 1 cavities 5442 2 14449 1 12229 1 13158 2 13668 1 7368 2 1660 2 15467 1 9219 1 16321 3 389 1 13821 1 12956 1 4950 1 15759 1 4951 1 11213 1 2704 1 16327 4 2838 1 14714 1 4590 1 3404 1 7946 1 4591 1 6342 1 1088 1 7189 1 4720 1 4343 1 7882 2 11628 1 1595 1 2782 1 13752 1 14394 1 4464 2 3685 1 3793 1 6837 1 5853 1 14469 1 15843 1 4470 1 10559 1 4142 1 14727 1 16336 1 10775 1 13113 1 2641 1 14470 1 11993 1 16283 1 11171 3 12186 1 12578 1 11842 1 4476 1 918 1 4417 1 10970 4 8112 1 12580 3 2648 2 5033 3 15423 1 1957 1 9587 2 9728 1 1561 1 13895 1 11939 2 349 1 7720 1 2986 2 12585 1 10983 1 15725 1 4017 1 14533 2 7210 2 1836 2 3065 4 10915 1 13904 1 7537 1 7473 2 9326 2 11865 3 13712 1 671 1 10867 1 2930 1 6740 1 14888 1 1698 1 9532 2 6310 1 7793 1 13718 2 1180 1 11131 1 5681 3 11802 1 8666 1 6244 1 7799 1 12599 3 9467 1 6178 1 5299 1 14759 1 12600 1 13793 1 14956 1 9996 2 15987 1 12342 1 5505 1 3219 1 7354 1 685 1 2423 1 9940 1 624 1 7427 1 9074 1 12609 1 14040 2 5756 2 9077 1 11887 1 2098 1 9550 3 2042 1 812 9 7359 1 14966 1 9618 37 16314 1 7500 1 7120 2 11895 1 1450 1 11750 1 14044 1 6520 1 12620 1 7436 2 14972 1 6894 3 contact_dermatitis 13216 1 4933 1 8702 1 3436 1 9680 1 8886 1 9209 1 1479 1 7189 1 16104 1 13821 1 5562 1 12795 1 8021 1 1907 1 3084 1 3348 1 7350 1 paul_revere 8334 1 16036 1 7195 1 7789 1 12524 1 3873 1 12315 1 14128 1 15208 1 6801 2 10462 1 2922 1 venous 869 1 3685 1 11614 1 10492 1 4438 1 12229 1 3980 1 13418 1 8822 8 10184 2 7988 1 12342 1 11726 1 1963 1 16078 1 15636 1 779 1 6 1 11213 1 3580 1 11592 1 7785 2 989 1 7474 1 3084 1 11997 2 1953 1 2148 1 6721 1 10867 2 4925 1 14980 2 10305 2 921 2 8755 3 6278 1 5328 2 11327 1 14967 1 1179 1 12292 1 2747 1 9619 13 9463 1 8535 1 13844 1 10572 2 12543 1 14504 1 3346 1 7058 1 12353 1 10656 2 artesian 7421 1 4944 2 11489 1 2747 1 bufonidae 12947 1 11002 1 8021 1 1457 1 7239 1 genus_polybotrya 9635 1 15082 1 5173 1 8879 1 8434 1 6801 1 13004 1 11000 1 unprovable 695 1 8180 1 5077 2 15191 1 4546 1 barbados 9415 1 14897 1 5671 1 505 1 12123 1 2263 1 484 1 1755 1 14012 1 5228 1 12181 1 5271 1 12848 1 5927 1 12705 1 8044 1 10335 1 9620 6 13089 1 8997 1 13994 1 9420 2 pinophytina 15940 2 3404 1 4971 1 11730 1 15241 1 15267 1 6498 1 834 1 15554 1 13878 1 1510 1 5361 1 510 1 electrometer 6107 1 3138 1 7120 1 15053 1 9477 1 7009 1 7934 2 2751 1 12059 1 red_ash 7421 1 15973 1 6442 1 12537 1 5295 1 10169 1 12811 1 3676 1 9186 1 13063 1 897 1 14212 1 9731 1 7350 1 2873 1 frame_of_reference 4659 1 5233 2 11428 1 10492 1 13668 1 219 1 7218 1 7049 1 1270 1 3393 1 15695 1 10313 1 4182 1 13917 1 1394 1 11907 1 8070 2 14005 3 15964 1 5573 1 7930 1 13680 1 1591 1 9459 1 6230 1 7326 1 211 1 866 1 5647 1 15053 1 11190 1 14461 1 13064 3 5178 1 12331 1 13277 1 9705 2 13078 1 10940 1 6499 4 9971 1 15056 1 11217 1 10324 1 1053 1 12596 1 996 1 12812 1 15191 1 1667 1 11686 2 13722 1 16178 1 2634 1 12569 1 2815 1 9621 6 bose 5026 1 9931 1 12410 1 3241 1 5159 1 14287 1 14483 1 8628 1 scudder 11461 1 5816 1 8351 1 15898 1 14600 1 1204 1 8073 1 8026 1 6375 1 4761 1 4446 4 5403 1 arthropod_genus 4833 1 4920 1 9041 1 nationalism 15285 1 9186 1 4487 5 9719 2 10559 1 7126 1 15112 1 13674 1 4808 1 12220 1 13676 1 8137 1 1617 1 13490 1 4907 1 1099 1 11312 1 6655 1 1787 1 4529 1 392 3 3705 1 14600 1 11512 1 14374 1 7607 1 1595 1 897 1 8180 1 10755 1 5500 1 15191 1 7309 2 3036 1 1055 1 11489 1 4087 1 governance 8507 1 8732 1 8772 1 5953 1 4041 1 3630 1 3997 1 8349 1 5904 1 11147 2 15637 1 12228 1 12131 1 9493 1 epidermal 8012 1 8879 2 5988 1 6498 2 2024 2 15472 2 11414 1 11969 1 5033 2 14212 1 thanks 7367 1 9622 13 9423 1 9359 1 6861 1 3393 1 4616 1 759 1 6106 2 2326 1 10727 1 9286 1 2873 1 1337 1 866 1 1621 1 6771 1 5880 1 15173 1 6715 1 370 1 11406 1 16483 1 15933 1 1916 1 1072 1 201 1 8167 1 906 1 12733 1 10265 2 1820 1 8528 1 3689 1 12181 1 10333 1 8137 1 62 1 1010 1 3626 2 12942 2 2405 1 10750 1 484 1 1134 1 5228 1 13946 1 12002 1 2885 1 13408 1 14838 1 4610 1 13735 1 6635 1 2765 1 14147 1 12416 1 16469 1 6195 1 13312 1 snowmobile 1211 1 8462 1 1014 1 16182 1 225 1 1205 1 15576 1 12315 1 12003 1 1942 1 4001 1 6509 1 5873 1 2277 1 2826 2 horsese 11008 1 9223 1 4921 2 pauropoda 7158 1 14126 1 11948 1 13076 1 15301 1 7974 1 5361 1 11274 2 maim 1211 1 9209 1 8199 1 9833 1 6830 1 10939 1 13374 1 5873 1 8146 1 mayer 11450 1 7729 1 5820 1 1398 1 13079 1 12995 1 4844 1 15077 1 dysphemistic 8094 1 9210 1 13076 1 564 1 9563 1 4363 1 6801 1 deaden 9339 1 9509 2 15818 1 2801 1 3113 1 9623 6 8653 1 7126 1 10613 1 4950 3 15808 1 5347 1 5100 1 8659 1 6830 1 13040 1 7358 1 15666 1 1999 1 9599 1 9152 1 14484 1 3584 1 10509 1 2857 1 2760 1 14819 1 7189 1 4802 1 5499 1 15932 1 11686 1 11631 1 883 1 730 1 11287 1 brickle 7693 1 9550 1 13626 1 7139 1 10961 1 10369 2 6444 1 2067 1 1928 1 13002 1 3938 1 lawsuit 10415 1 12585 2 4712 1 14052 2 14918 1 4049 2 11492 2 10247 1 14748 2 7911 1 8542 1 7851 1 12815 1 2352 1 9624 18 9324 1 6167 1 12721 1 11147 2 13490 3 3320 1 8730 1 7441 1 5586 2 13196 1 4160 1 7729 1 14888 2 5648 2 7413 2 5621 1 14321 1 12397 2 6498 2 4243 2 5810 1 2965 1 14236 2 5423 1 6716 2 2902 2 12795 2 16484 6 2201 1 12569 2 747 1 3038 1 6776 1 9830 1 8167 1 4466 4 8975 1 4537 1 9300 2 7893 1 12059 1 6062 1 15025 2 7992 1 15677 1 2422 2 8070 1 10998 1 8322 2 2755 1 14941 1 8644 1 11230 4 11886 2 1552 1 16227 2 7256 1 6230 1 4041 2 12411 2 16033 1 6723 2 11680 2 10340 2 8647 2 6513 1 6697 1 12003 1 6426 1 15077 1 3382 1 12255 1 5918 1 10755 1 4451 2 9355 1 3534 1 12416 1 8946 1 9109 1 donation 6976 1 906 1 13275 1 4680 1 2646 1 12586 1 6794 1 4203 1 2911 1 8097 1 14616 1 6249 1 9625 5 8449 1 15777 1 7519 1 10795 1 11017 1 5694 2 2797 1 7759 1 7545 1 14626 1 11633 1 1181 1 6643 1 cylinder_head 13034 1 10421 1 2499 1 11657 1 9031 2 2500 1 14193 3 2581 1 4142 1 14584 1 7134 1 7505 1 7037 1 1367 1 6316 1 13008 3 1686 1 2594 1 capacity_measure 4432 1 11716 8 14958 1 12760 1 4376 8 9219 8 7017 2 2831 1 7280 3 5271 8 9188 1 9626 8 1493 3 9138 1 3699 8 utricularia 10790 1 3555 1 6708 1 parks 7969 1 5008 1 8652 1 11903 1 39 1 12815 1 13821 1 12848 1 4263 2 11212 1 11147 1 12395 1 1526 1 9627 13 14196 1 3320 1 3401 1 2836 1 10419 1 763 1 8962 1 12822 1 8702 1 13646 1 9021 1 7729 1 10255 1 14515 1 7789 1 14159 4 13260 1 265 1 10158 1 1915 1 4620 1 4562 1 6998 1 11662 1 7983 2 16127 1 4564 1 447 2 13434 1 1918 1 10094 1 14270 1 3037 1 10361 1 1369 1 1211 1 9405 1 10809 2 12284 1 1279 4 12341 2 343 1 9203 1 4031 1 2452 1 13007 1 8259 1 11414 1 14525 1 1932 1 1165 1 8177 1 14077 1 6480 1 5228 2 9644 1 12003 10 9176 1 13843 1 6727 1 7746 1 16177 1 6702 1 2108 1 fatty_liver 16225 1 8322 1 5694 1 10961 1 10473 2 15588 1 13897 1 4299 1 5240 1 2111 1 9920 1 1953 1 purplish_pink 10444 1 2412 1 16314 1 4143 1 14549 1 5184 1 15294 1 13089 1 14921 1 7798 1 14275 1 16329 1 9665 1 every_inch 16436 1 10492 1 13684 1 6835 1 refreshing 9255 1 4017 1 6502 1 13035 1 15840 1 14555 1 5307 1 12145 1 10664 1 564 2 11123 1 4907 1 9628 6 15916 1 14909 1 1119 1 10253 1 13634 1 4680 1 12051 1 9704 1 655 1 4887 1 8517 1 11129 1 4912 1 2692 1 4082 1 6442 1 9465 1 12838 1 2416 1 11092 1 7884 1 12177 1 13171 1 4994 1 12368 1 3536 1 6161 1 6894 1 15611 1 8581 1 5832 1 beater 9705 1 7501 1 16290 1 14270 1 11686 1 promise 16260 1 9279 2 5261 1 4284 1 13668 1 11552 1 1939 2 1145 2 10086 1 14318 1 4054 1 1393 1 9426 1 7880 1 7757 2 15636 1 2326 4 9562 1 8749 3 9563 1 2703 1 10899 1 6655 1 6656 1 12757 1 6530 2 9021 2 10483 1 14064 1 8517 2 6027 1 2051 2 3550 1 6774 1 8758 1 9826 1 267 1 13684 1 2846 1 13171 2 15271 2 1540 1 7765 1 269 1 8167 1 10492 1 5785 2 10363 1 3113 1 4537 1 9717 3 8529 1 8028 2 7031 1 531 1 13690 2 13836 2 8288 1 16168 3 5791 1 11309 1 16283 1 8645 1 15415 1 9439 1 7390 3 7391 1 8176 1 4200 1 3428 1 918 1 5914 1 7648 2 278 2 10646 1 8534 1 2214 2 13843 1 12382 1 4082 2 14225 2 10376 1 4422 2 8045 1 1055 3 1054 1 13847 2 6161 3 5181 2 11854 1 5922 1 12139 1 12389 2 5038 1 13456 2 5926 1 7660 1 7537 1 15667 1 8055 1 12268 1 3582 1 12789 2 7787 1 11188 1 4558 1 46 1 6678 1 7413 1 15670 1 8314 1 9989 1 6680 1 16051 1 15015 1 6685 4 12793 1 15570 1 11869 1 13329 1 14359 5 15572 1 9861 1 7800 1 6444 1 10131 2 10808 1 13863 1 10395 1 7170 1 9742 1 7420 1 12027 1 15576 1 6804 1 5307 1 15934 1 945 2 62 3 12534 3 562 1 3596 1 5309 1 2882 2 1716 1 6323 1 6189 1 15186 1 2481 1 14373 1 6327 5 13604 1 10012 1 5954 2 6697 1 312 1 15312 1 13476 1 2765 3 7065 1 4451 1 11749 1 6330 1 12416 2 4707 2 14504 1 4118 1 5317 1 10018 1 13223 1 5957 2 15581 1 573 1 7298 1 14918 1 8821 1 15468 1 10537 1 703 2 2114 1 6204 4 3608 2 7302 10 6080 1 10279 1 8083 1 10543 1 3320 2 5964 1 4338 2 9629 91 11081 3 5841 2 2266 1 3177 1 10283 1 11763 3 15330 4 15207 1 3495 1 8211 1 12569 1 12312 1 2395 1 9508 1 10290 1 4250 1 8093 1 7582 1 15479 3 8975 4 3043 1 13626 1 13997 1 8582 1 9903 5 12059 1 15486 1 7711 1 6351 1 719 2 2641 1 11776 1 12969 2 10846 1 7587 2 15339 1 1099 1 15964 2 1604 2 4860 1 11108 2 3741 1 6230 2 13118 4 13244 1 2793 1 6980 2 1746 1 15077 1 14672 1 6359 1 13767 1 14018 1 2797 1 15225 1 15856 1 2518 2 6853 2 9920 1 15500 1 1486 1 2300 2 7851 1 3063 3 4156 2 4616 5 16117 1 357 2 7210 1 16234 1 13903 2 14155 1 7973 1 12206 1 6109 1 611 1 12726 1 5878 1 866 1 6241 1 10324 1 4493 2 16244 3 9663 1 1242 1 16484 2 126 2 997 1 3520 1 5502 1 12602 1 14691 2 14555 2 10593 1 8137 1 7992 1 7740 1 13271 1 12609 1 9270 1 7742 1 1787 1 3659 2 11002 1 879 1 7230 1 10465 2 4169 1 1256 2 13408 1 11811 1 14838 3 7872 1 8146 1 2952 1 7011 1 6517 2 2694 1 14570 1 12875 1 14186 1 13153 1 7622 1 12620 1 5142 2 soulfully 5752 1 2546 1 had_better 9339 1 4466 1 7657 1 2298 1 3367 1 10265 1 15110 1 15436 1 4059 1 3949 1 972 1 9675 1 11509 1 4987 1 15310 1 1030 1 958 1 15918 1 8908 1 13277 1 538 1 16008 1 13606 1 5728 1 1573 1 13752 1 12567 1 2444 1 12102 1 14429 1 4930 1 5694 1 8408 1 geologist 10253 1 7421 1 9630 5 2143 1 7970 1 3483 2 7218 2 6997 1 14811 1 1136 1 5412 1 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2312 2 4407 1 1709 1 4031 1 7711 2 6474 1 7928 1 7713 1 11000 1 8259 1 9674 1 14731 1 9206 1 409 1 9172 1 6419 1 15027 1 13807 1 11233 1 9005 2 9681 21 6890 2 13734 1 14965 1 1893 1 16344 1 15968 1 1560 6 10079 1 8394 1 16442 1 12070 1 1685 2 2043 1 9216 3 12546 3 14189 1 14972 2 by_all_odds 14494 1 14917 1 4165 1 8946 1 8055 1 11081 1 rockers 6110 1 2161 1 400 1 15886 1 8108 1 7230 1 14374 2 7218 1 15110 1 7912 1 5271 2 1963 1 14268 1 4705 1 7122 1 9905 1 10131 1 5756 1 536 1 stride 11119 1 9086 1 16182 1 572 1 10535 1 6525 1 9219 2 9451 1 8156 1 4335 1 14948 1 1523 1 3205 1 6434 1 3396 1 9223 1 14154 1 11728 1 3907 1 3676 1 1493 1 9285 2 8309 2 2836 2 4921 5 5321 2 14197 3 7541 1 10385 1 5900 1 9367 1 5966 2 4530 2 9705 1 8517 1 10871 1 16008 1 933 1 6499 1 11327 1 152 2 7215 1 1238 1 5500 1 7581 2 15299 2 503 1 13684 1 9264 1 15445 1 1818 1 13171 1 8997 1 2394 2 1208 1 11831 1 14094 1 682 1 15591 2 16305 3 14469 1 13368 2 3258 1 13110 1 8555 1 1641 1 9478 1 5790 1 13543 1 13007 1 8904 1 14626 1 12578 1 11716 1 12912 1 15812 1 3964 1 2245 1 6230 1 4169 4 9682 19 2483 1 9971 1 4385 1 139 1 12255 2 7528 1 12714 1 8180 2 9588 1 7122 1 11320 1 15392 1 15920 1 2918 1 15225 1 1389 1 6894 2 284 2 8373 1 1296 1 sodium_hydroxide 13209 1 1513 1 12732 1 3600 1 4358 2 11403 2 10556 1 9219 1 12809 2 16336 1 8449 1 14921 1 7837 1 11532 1 2043 1 14170 1 12228 1 9565 1 6172 1 2873 1 betimes 3377 1 7507 1 amphibious_operation 8284 1 3350 1 4041 1 13749 1 10874 1 15935 1 2650 1 1836 1 12232 1 6080 1 10077 1 6830 1 13434 1 496 1 12609 1 liquify 12943 1 2961 1 12955 1 3243 1 4850 1 9445 1 8042 1 5033 1 9645 1 4696 1 9728 1 12277 1 13665 1 10727 1 3874 1 11939 1 vermicular 801 1 777 1 2605 1 2796 1 lumen 7421 1 11716 4 8214 2 1660 1 3473 1 1698 1 9928 1 12181 1 4335 1 11154 3 9683 5 4397 1 14181 1 9932 1 13099 1 1493 2 8913 1 8997 3 15457 1 1736 1 3732 1 sweetening 15864 1 918 1 3992 1 9343 1 10311 1 13736 1 5307 1 12809 4 15232 1 9684 7 8618 1 13422 1 13379 1 9064 1 90 1 2159 4 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6792 1 2467 1 afflicted 10503 2 4335 1 606 1 9691 18 12848 1 13251 1 6167 1 15601 1 5987 1 15762 1 12301 1 11081 1 12361 1 828 1 1363 1 10731 1 16482 1 614 1 11662 1 15058 1 12795 2 678 3 3007 1 14205 2 14986 2 905 1 15240 2 12643 1 3145 1 10430 1 12905 1 4294 1 13797 1 479 1 2501 1 13335 1 944 1 4539 2 14470 1 3893 1 11572 1 12943 1 9942 1 3601 1 2789 1 7148 1 14838 1 10525 1 12383 1 3435 1 14636 1 1196 1 10533 1 3059 1 6264 1 medici 11548 1 7398 1 9956 1 4162 1 16292 1 4230 1 3082 1 587 1 3883 1 8100 1 10899 1 10903 1 12729 1 1176 1 16310 2 5491 1 longitude 8726 1 7421 2 15241 2 11587 1 884 1 7218 2 9451 2 10695 2 5950 1 7210 2 15695 1 8174 1 3134 1 9692 11 5347 2 15729 1 11619 2 14247 1 11311 2 16198 3 12988 1 15028 1 15928 1 2945 3 9021 1 9396 3 14010 3 7789 2 10190 1 5903 2 9292 1 11646 1 8519 1 14857 3 757 1 6772 1 7651 1 3490 1 6686 2 373 1 12020 1 1537 1 12953 1 12569 1 10018 1 11180 2 subclass_liliidae 3993 1 3760 1 14607 1 12697 1 3684 1 8866 1 13712 1 foul 11966 1 11429 1 5372 1 15254 1 15994 1 10757 2 10473 1 13552 1 13674 1 13422 1 11292 1 1799 1 13021 2 15636 1 9693 62 2436 1 9563 1 8512 1 3544 1 5776 1 2560 1 1805 1 4529 1 3679 3 1807 2 15267 1 14323 1 10768 1 5526 1 16273 1 642 1 6535 2 6283 1 9433 2 11443 1 10362 3 528 3 9576 1 2448 1 3258 1 5654 1 531 1 11308 1 5910 1 1043 1 15544 2 16026 1 8645 1 4198 4 4669 2 4542 2 11715 1 7774 2 10366 1 8176 1 11999 1 15419 1 5179 1 6039 1 2581 2 5032 1 6542 1 1558 1 1560 1 4803 2 2216 1 8180 4 13309 1 9728 1 6043 1 6668 1 9842 1 3941 1 2986 1 5184 1 14742 1 6548 1 8541 1 13054 1 10247 4 11859 1 8787 1 11861 1 1963 2 12786 1 12145 1 3439 1 8309 1 12267 1 15666 1 15293 2 13859 2 10800 1 4099 1 10512 1 1064 1 11327 1 13718 5 14616 2 6685 1 10127 1 3006 2 12795 3 12020 2 15179 1 1430 1 1845 1 15572 1 7420 1 4690 1 12284 2 13967 1 10932 1 14363 1 14246 1 7804 1 807 1 5818 1 13215 1 4446 1 12536 1 16200 1 8683 1 16065 1 9209 1 812 2 15459 1 15311 1 4701 1 13734 14 12414 1 15579 1 68 2 4707 1 12416 1 16442 1 10411 1 6580 1 9216 1 11612 1 16210 1 5443 1 8949 1 323 5 8199 1 9359 1 9219 1 4335 1 4950 1 6080 1 9887 1 13490 1 13985 1 6830 1 10153 2 5449 1 1999 1 2261 1 11911 1 10421 1 335 1 12047 1 3324 1 13096 1 198 2 13359 1 2905 1 14656 1 14658 1 1596 1 2277 1 8581 1 9768 1 1211 1 14135 1 9635 2 5853 1 8099 1 587 1 5722 1 8105 1 14525 1 2011 1 726 1 2915 6 3860 1 211 1 12192 1 12973 4 11645 1 4862 1 3744 1 16104 1 1893 1 15495 1 9784 1 13380 1 13767 1 8721 1 1357 1 11939 1 9920 2 7598 1 3200 1 14674 1 16350 1 8592 1 6731 3 10310 1 9115 1 12326 1 2802 4 7210 8 2527 2 5347 1 1230 1 13904 1 3070 1 1495 1 10189 1 15100 2 15736 1 3211 2 3760 1 13648 5 1240 1 9536 1 13263 1 8609 2 5628 1 14166 1 997 1 998 1 4495 1 6245 1 1635 1 869 1 4164 2 7110 1 12735 1 4761 1 8869 1 14557 1 8870 1 4031 1 11268 1 3082 1 12477 1 12610 1 1510 1 7006 1 6123 2 1932 1 2426 1 11956 1 4998 2 8745 1 9681 1 2042 1 16491 1 13150 3 7360 1 7744 1 14965 1 7010 1 4280 1 1519 1 14569 1 14445 2 12102 1 4512 1 13665 1 5766 4 16259 1 12353 1 1267 1 6894 6 1936 1 7014 2 11815 3 competitive 15085 2 3634 1 1224 1 2862 2 2114 1 1145 1 4156 1 13674 2 9821 1 8238 1 5898 1 15321 1 3169 2 15399 1 9694 18 8201 1 14643 1 5487 1 14289 1 4813 1 4338 2 1730 1 9429 1 15402 3 5082 1 10483 1 15831 1 9400 1 1595 2 14547 1 9465 1 16483 1 1156 1 641 1 126 1 9537 1 4220 2 9092 1 14270 2 7314 1 2277 2 7802 2 5785 1 15591 1 11133 1 9203 1 719 1 3082 1 13214 1 11777 3 4412 1 7957 1 1374 1 14005 1 13598 1 4198 1 7774 1 2915 1 3301 1 6230 2 13011 1 1324 1 6426 1 7010 1 11278 4 594 1 13013 2 12416 2 3267 2 16230 1 6792 1 3238 1 9920 1 13706 1 usable 11431 1 9819 2 13669 1 16212 1 6131 1 4335 1 12556 1 7505 1 9696 20 14920 1 7239 1 6903 1 2195 1 9429 1 10956 1 5648 2 9704 1 14323 3 10840 1 13935 1 1595 2 8209 1 15058 1 3918 1 13434 1 2335 1 1596 1 6535 1 964 1 7765 1 8581 1 4659 1 1211 1 4728 1 8214 1 13365 1 16391 1 14209 1 13367 1 10364 1 9635 1 3336 1 1042 1 7587 1 13888 1 16096 1 15277 3 15488 3 3859 1 15918 1 4201 1 15715 1 12449 1 1749 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2 2945 1 3342 1 2246 1 6230 3 6102 1 11002 1 16100 1 8040 2 14080 2 8843 1 7558 2 8942 1 9971 1 14104 1 14527 2 10238 1 16035 1 2737 1 1291 1 6328 1 7012 1 13846 1 14044 1 12161 1 10046 1 6520 2 2920 3 1295 1 2922 1 at_a_time 4944 1 6896 2 9819 1 15630 3 3242 1 12693 1 9219 2 5581 1 1939 1 6958 1 10023 1 3541 1 13422 1 1525 1 519 1 9698 42 8571 1 11553 1 8512 1 15201 1 7943 1 1999 1 11821 1 2960 1 15327 1 8752 1 8278 2 14855 1 5841 1 14460 4 3404 2 9021 1 9705 2 5325 1 12563 4 8282 1 5847 1 6218 1 14584 1 6145 1 7242 1 5528 1 768 1 8020 1 10424 1 13684 1 4722 1 5591 1 1596 1 6283 1 1540 1 5018 1 1211 1 15539 1 8635 1 3556 1 4537 1 13037 1 7030 1 10167 1 6972 1 4143 1 9167 1 12707 1 14864 1 7082 1 3048 1 8101 1 8645 3 12511 1 12131 1 15966 1 15594 1 2646 1 211 1 13759 1 656 1 4479 1 3863 1 5033 2 12383 1 14015 1 12067 1 1291 1 4421 1 11320 1 15720 1 14018 1 9107 3 12071 1 2016 1 11939 4 13706 1 9845 1 1296 1 7262 1 14226 1 13849 3 12718 1 1903 1 14085 1 2143 1 10114 1 12586 1 3636 1 12075 1 4155 1 7210 4 11461 1 15729 1 1907 1 13903 1 6366 1 15506 1 4921 1 12268 1 931 1 2226 1 9598 1 8056 1 5739 1 4617 1 4160 3 13531 2 225 1 13198 1 11403 2 10671 1 11327 1 171 1 13718 1 12531 1 15105 1 1242 2 10197 1 13263 1 9860 1 8801 1 9664 1 8128 1 16303 1 15303 1 4933 3 8998 1 14555 1 15614 1 8869 3 2312 1 9203 1 9937 4 12534 3 2169 2 9268 3 13273 1 11607 3 12346 1 12033 1 7809 1 15940 1 2426 1 6257 1 1014 1 4041 1 5953 1 15459 1 11747 1 5439 1 10340 1 10755 2 15252 1 6763 2 5694 1 13608 1 13665 1 12353 1 5578 1 16318 1 15946 1 7436 1 13871 2 2488 2 7878 1 11287 1 protestant_denomination 2332 3 3404 1 865 1 7165 1 2136 1 13259 3 12586 1 10219 1 13079 2 704 3 8897 1 3204 2 1081 1 9203 1 141 1 11147 1 10144 1 9695 10 6865 2 7226 1 14946 1 shimmery 11123 1 7421 1 15862 1 7667 1 military_operation 10469 1 7256 1 6633 1 2619 1 10881 1 4627 1 3350 1 2263 1 5619 1 14052 1 13418 1 5214 1 6958 1 12596 1 2306 1 6527 1 5425 1 9699 6 12416 1 11686 1 13434 1 9860 1 7025 1 10771 1 8276 1 7665 1 5957 1 annelid 801 2 34 1 3555 2 7657 1 12866 1 13004 3 4961 1 9635 1 9115 1 10790 1 13968 1 12342 1 6585 1 9258 1 4487 1 11541 1 12848 1 7158 1 2865 1 5347 1 13114 1 9700 8 12266 2 10583 1 5644 1 12017 1 6419 1 4698 1 11925 1 7541 1 7555 1 14714 1 10867 1 10321 1 10956 1 2321 1 6542 1 13935 2 10423 1 10529 2 13515 2 16087 1 8021 1 10978 1 10652 1 6535 1 2394 1 999 2 7058 1 11612 1 8802 2 12122 3 point_of_periapsis 2945 1 10018 1 9451 1 5385 1 11410 1 time_scale 14868 1 12516 4 5279 4 9701 9 10816 1 7006 2 3262 4 8576 1 2261 1 9153 1 bissau 9415 1 8484 1 11716 1 8997 1 13435 1 16154 2 484 1 coming_together 2496 1 4035 1 12980 1 3622 1 7169 2 2625 1 9209 1 6655 1 1136 1 grocery_list 10415 1 14532 1 573 1 6180 1 16051 1 362 1 16410 1 procrastinate 10415 1 4728 1 7366 1 12466 1 4332 1 2221 1 16471 1 7899 1 13108 1 10688 1 13110 2 15898 2 15445 1 2727 1 8408 1 10625 1 14814 2 11081 1 jane_austen 10492 1 6116 1 10363 1 323 1 12643 1 11457 1 14639 1 10430 1 965 1 6838 1 15410 1 15436 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5202 1 11886 1 12682 1 12410 2 7293 1 4041 2 309 1 7230 1 16492 1 1256 4 13734 1 13735 1 12159 1 141 1 11749 1 7012 2 4707 1 2825 1 2111 1 5957 1 luke 16248 1 13698 1 16494 1 12424 1 9506 1 1291 1 8749 1 7587 1 13192 1 2628 1 1072 1 7169 1 6477 1 7509 1 5945 1 3084 1 5668 1 jansenism 265 1 depiction 7398 1 10430 1 10041 1 13641 1 8655 1 10560 1 13674 1 9904 9 6035 1 7374 1 2623 1 4110 1 3676 1 14140 1 7239 1 2976 1 2882 1 1176 1 9926 1 3406 1 12871 1 8108 4 8745 1 11644 1 15967 3 151 1 3483 1 2151 1 6498 1 5228 1 13260 1 813 2 16174 1 13079 1 4912 2 14683 1 6090 1 9506 1 7195 3 629 1 16495 1 11282 1 11917 1 13863 1 11612 1 mandara 12537 1 13648 2 12399 1 14753 1 2263 1 8148 1 13228 1 9764 1 6498 1 2613 1 12710 1 10070 2 hubble_constant 16052 1 10133 1 8503 1 9527 1 9146 1 8325 1 5178 1 6279 1 568 1 5657 1 15056 1 14604 2 binary_operation 9764 1 10741 1 8529 1 y_chromosome 15515 1 14584 1 906 1 6080 1 2190 1 1903 1 3830 1 5214 1 herbert_spencer 8292 1 8004 1 12400 1 7660 1 12534 1 6828 1 918 1 15430 1 14398 1 3534 1 9390 1 12620 1 trough 7421 1 3436 1 5610 1 8541 1 7218 1 62 1 9905 7 8202 1 3874 1 7129 1 1999 1 5756 1 15342 1 536 1 16286 1 3481 1 6110 1 14514 1 6592 1 1409 1 14424 1 14374 2 9444 1 1236 1 3971 1 9400 1 14634 1 1180 1 14268 1 13844 1 7746 1 5245 1 8408 1 12827 1 11474 1 14956 1 traded 5183 1 8624 2 10535 1 13417 1 12693 1 10380 1 4616 1 12556 1 7302 1 1360 1 2326 1 13490 1 10456 3 16080 2 1871 1 5291 2 14481 1 3480 1 1530 1 14855 1 12726 1 10321 1 8991 1 3997 1 10840 1 8246 1 14585 1 4437 1 12795 1 15445 1 13722 3 8408 1 14270 1 16192 1 10200 4 7197 1 4250 5 10679 1 939 1 905 1 10363 1 3145 1 1279 1 11735 1 9298 1 9717 1 4762 1 8468 2 13888 6 3596 1 508 1 9906 20 14666 1 11339 3 13546 2 12778 1 13076 1 14810 1 2290 1 4203 2 13011 1 12064 1 15077 1 4172 1 10570 1 15519 1 15034 1 9421 1 9920 2 lawnmower 12596 1 4627 1 12410 1 7505 1 9696 1 5648 2 11742 1 10129 1 6131 1 15301 1 8801 1 15277 1 964 1 3859 1 undried 7068 1 15239 1 9680 1 3741 1 12576 1 9552 1 12761 1 15652 1 improperly 1635 1 10492 1 8592 1 2205 1 14744 1 3044 1 11753 1 37 1 10380 1 531 1 1941 1 15636 1 7757 1 10206 1 12610 1 9907 8 5573 1 8961 1 5856 1 10867 2 1829 1 5327 1 4480 2 15751 1 13752 1 338 1 9489 1 68 1 12795 1 10841 1 126 1 1721 1 1612 1 fly_honeysuckle 6442 1 rubbing 12621 1 3240 1 387 1 15817 1 15254 1 7657 1 4122 1 15467 1 10310 1 5186 2 5695 1 15230 1 289 3 8853 1 7852 2 16410 1 4950 3 3397 1 2190 3 8083 1 13583 1 15954 1 14885 1 2836 3 2466 1 2873 1 6530 1 2330 2 5295 1 2331 1 5083 1 5418 1 16008 1 11404 1 227 1 10840 1 13429 1 7133 1 11090 1 1815 1 9295 2 998 1 9467 1 13881 1 6375 1 2636 1 7350 1 14859 1 7421 4 13034 1 4933 2 10165 1 15960 1 1370 1 1924 1 13999 3 2448 1 8582 2 2337 1 6506 1 7711 2 561 1 4272 1 3296 1 6507 1 9612 3 1851 1 14905 1 9413 2 14275 1 1645 1 15883 1 9908 28 11171 1 6755 1 8586 1 11997 1 7040 1 8292 1 1513 1 625 1 5954 1 7744 3 6041 1 13736 6 4418 1 595 2 3831 1 8449 1 12874 1 13923 1 5764 1 1357 2 3235 1 9448 1 12353 1 7464 1 3237 1 14047 2 11321 1 16179 2 2349 1 1959 1 11287 3 enhanced 1635 1 13414 2 14053 1 3979 1 6473 1 16023 1 14319 2 585 4 14626 2 5702 2 7509 2 9909 7 5240 2 14009 1 3462 1 9705 1 6498 1 9176 4 15269 2 3862 2 11327 1 13736 1 1595 2 16177 2 996 2 14147 1 11112 1 11419 1 3237 2 14956 1 9768 1 14506 1 6264 2 repair_to 873 1 2945 1 7422 1 12934 1 6287 1 13110 1 12524 1 glidden 9429 1 1920 1 4256 1 7587 1 2558 1 7360 1 13101 1 13576 1 7210 1 domino 62 3 5705 1 16405 1 14650 3 9532 3 13656 3 5246 3 12578 1 15745 3 curlew 1576 1 6549 1 56 1 1457 1 14507 3 3636 1 9669 1 14555 1 5039 1 7355 1 13800 1 15261 2 3676 1 14645 1 14405 1 2869 1 877 1 15155 1 13712 1 4829 1 9910 11 11956 1 13462 1 6574 2 16258 1 11810 1 15833 1 6143 1 14236 1 14634 1 1628 4 1238 1 8021 1 7138 1 8023 1 3038 3 12573 1 3671 1 7350 1 13567 1 cowrie 3134 1 13878 1 2529 1 484 1 1457 1 7574 1 7868 1 7142 1 5096 1 14047 1 1104 2 13935 1 13712 1 metric 4529 1 11716 1 9494 1 9527 1 10059 1 11033 2 9911 6 4376 1 438 1 4335 1 7010 1 5729 1 7991 1 8180 1 6328 1 12674 1 4643 1 11728 1 9478 1 12161 1 8997 2 938 1 1389 1 361 1 birthrate 2945 1 8449 1 4238 1 745 1 14838 1 11339 1 2997 1 9920 1 sulkies 10056 1 4173 1 6437 1 15591 1 mileage 62 1 938 1 682 1 medulla 6739 1 9728 1 6290 1 15880 1 6498 1 4343 1 10466 2 823 2 uncontested 11278 1 10479 1 8292 1 13864 1 5176 1 velum 9853 1 12399 1 11351 1 victimization 15337 1 10594 1 1725 1 15776 1 15918 1 3078 1 2125 1 7189 1 5089 1 13583 1 2577 1 ripcord 7785 1 9277 1 173 1 generate 5770 1 5769 1 3096 1 13552 2 15636 1 5641 1 14979 1 3676 1 1943 1 2960 5 2838 1 6656 1 763 2 9429 4 3404 3 13931 1 10226 1 4648 1 14464 1 11156 1 7636 2 2846 2 15 2 14587 2 2335 1 11833 2 14071 1 8166 1 1406 1 11563 1 2720 1 14469 1 15910 1 2724 1 11838 1 9033 1 11990 2 10495 3 8032 1 14864 1 11568 1 4907 3 11716 1 4200 1 11999 1 14600 2 1680 1 7649 2 7900 2 16174 2 2581 1 9971 1 5033 2 13845 1 13701 1 9045 1 12261 1 7393 3 13311 1 10110 1 5183 1 2988 1 1565 1 9049 2 10247 1 2862 1 9451 2 2597 1 3701 1 15791 1 12145 2 11728 1 10664 2 12788 1 13063 1 2466 3 12790 1 3951 1 437 1 4925 1 4926 1 13713 1 10255 1 12669 1 2083 1 16049 2 14616 1 6442 1 12795 1 9195 1 14892 1 6935 2 7800 2 9197 1 938 1 5565 1 11474 1 7421 3 9608 1 4933 4 448 1 6689 1 4692 2 10812 2 15576 1 1438 1 14363 2 14243 1 11882 1 4110 2 12406 1 11884 1 6064 1 13598 1 2376 1 5952 1 8076 2 16438 1 13076 1 8559 2 1324 1 10526 1 566 1 13082 1 12159 1 3158 2 9213 1 13344 2 12416 1 3161 1 1082 1 7815 2 10018 8 7436 1 4237 1 3473 1 15467 1 14259 1 2114 1 12554 1 16321 2 12815 1 4950 4 15470 3 6080 1 3839 2 15827 2 4951 1 6341 1 7824 2 5584 1 8204 1 10421 1 6713 3 5324 1 960 1 12172 1 14792 1 4343 1 5458 1 13751 1 1091 1 13752 1 12431 1 2495 1 6596 1 2394 1 5089 1 4464 1 2499 1 15213 2 473 2 9772 3 14274 1 12318 1 10295 1 718 2 1602 2 968 1 4734 11 10847 1 721 1 971 1 15964 1 14005 4 589 1 6477 1 11510 1 14408 1 9640 1 7324 1 14010 1 16101 1 484 1 8472 2 3504 1 5228 2 9912 71 1893 1 11380 1 14147 1 9107 1 15225 2 1357 1 3865 1 11789 1 6486 2 4740 1 9527 1 6854 1 13899 1 2300 2 12459 1 10859 1 1490 1 7210 6 5346 1 5347 1 3065 1 11396 1 6736 2 6367 1 2026 1 14819 1 994 1 9532 1 10733 1 12856 1 11130 5 5993 1 15105 2 2811 1 6747 1 6244 1 9539 1 11409 2 8490 1 8998 1 1246 2 7107 1 3218 2 1782 5 10741 1 7738 1 10333 1 4761 1 1783 1 7991 1 4272 1 15880 1 9268 2 3223 1 3376 1 13273 3 4502 1 5756 1 9941 1 7006 2 877 1 4998 1 12095 1 11002 1 2546 1 10211 1 10340 2 6005 1 7009 7 16493 1 4172 1 14181 1 8394 2 14044 1 12353 1 1389 1 8269 1 14704 1 13814 1 meronym 14975 1 14251 1 1099 1 12395 1 5733 1 10317 1 feverish 12731 1 13092 1 12718 1 11400 1 3145 2 12633 1 4257 1 10503 1 9913 5 14066 1 8180 1 9223 1 8158 1 15933 2 13071 1 8021 1 12795 1 8309 1 12374 1 13040 1 13301 1 7314 1 6738 1 9920 1 processing 15126 1 12299 1 3242 5 4123 1 12421 1 1145 1 6017 1 13422 2 11076 1 7374 1 519 2 1458 1 2627 1 6962 1 5964 1 6655 1 2960 5 13354 1 2490 1 8752 2 8458 1 10421 1 3404 4 9021 1 14126 1 2263 1 3679 1 1088 2 4530 1 7189 1 15833 1 14464 1 12898 2 2845 1 10033 1 5458 1 9506 1 15058 1 154 1 14390 1 8523 1 11017 1 10490 1 10093 1 6349 1 4464 1 11985 1 10096 1 9830 1 905 1 18 2 13628 2 9717 1 2724 1 13574 1 12059 2 2642 1 14864 4 9377 2 8903 1 15339 2 4196 1 13576 1 3262 2 536 2 14994 2 2405 1 1478 1 10564 1 2789 1 2070 1 8773 1 15075 1 1893 1 15077 1 1169 4 9914 48 1168 2 5796 2 13380 1 10572 1 6850 2 3433 1 1357 1 9179 1 14873 2 9109 1 10980 1 13453 1 4365 2 6792 2 5183 1 14226 1 16410 1 11461 1 11728 1 9116 2 15729 1 14155 2 3703 1 2531 1 5240 2 2149 1 12270 1 6557 2 10867 2 366 2 3283 2 7164 1 9801 1 7729 1 12669 1 11403 1 13392 2 14234 1 10513 1 12995 1 2747 1 4928 1 9662 4 1180 1 3362 1 15105 1 5563 1 5131 2 12020 1 9860 1 8997 5 7678 2 14956 2 1635 1 15374 2 1246 1 1128 2 9473 2 12284 1 11736 1 5815 1 4761 1 15024 1 450 1 686 1 7290 1 1006 1 15880 1 3223 2 13804 1 3376 1 2755 1 8323 1 14248 4 13273 1 3527 1 4503 1 5202 1 16436 1 7810 1 7293 2 9143 1 1134 2 5825 1 14837 1 14374 1 7556 1 186 1 5362 1 4888 1 1516 2 7361 1 15312 1 4172 1 5828 4 5512 2 1450 1 14969 3 12543 2 697 1 12416 1 9949 1 13666 2 320 1 8005 1 14047 1 7934 1 headstall 13205 1 15539 1 572 1 1169 1 11701 1 15591 1 enrolling tenderized 13036 1 13619 1 4365 1 14949 1 youll 10415 3 6011 2 8507 1 1331 1 12718 1 10275 1 1793 1 573 1 13314 2 6204 1 4335 2 12848 1 6080 1 10863 1 15602 1 8750 1 668 1 5240 2 2873 1 81 1 4212 4 9021 1 3072 1 16478 1 14292 1 3679 1 1364 1 14351 1 7413 2 3992 1 1501 2 15930 1 1621 1 16356 1 4928 1 1947 1 11499 2 3362 1 1972 1 12795 1 12020 1 835 1 15 1 8800 1 126 1 3646 1 1818 1 6596 2 13829 1 15572 1 1209 1 13965 5 9574 1 1635 1 5785 1 8975 2 5331 2 15334 1 13569 2 13968 2 8834 2 13570 1 7548 1 2419 1 8531 1 8738 1 15486 1 6473 2 9775 1 6630 1 11452 4 6947 1 12346 1 5888 2 8769 1 3022 1 7810 1 9305 1 11002 1 3967 1 10211 1 11646 2 12002 1 16104 1 2214 2 13736 1 15423 1 12159 1 13082 2 10570 1 16494 1 12067 1 11959 1 13380 1 1450 1 9915 16 7260 1 6043 1 13411 1 16315 1 15225 1 6893 1 12620 2 284 1 7014 1 white_sauce 14859 2 12521 1 9769 1 3112 1 8338 1 4260 1 2208 1 16078 1 12785 2 13888 1 2788 1 2211 1 7537 1 7039 1 3501 1 2134 1 781 1 6452 1 3783 1 12246 1 1732 2 10176 1 789 1 67 1 13394 1 9739 2 5298 2 7023 1 9916 10 4001 2 1818 1 681 1 11022 1 4365 1 government_agent 16261 1 16177 2 7894 1 3222 1 hurrying 12725 1 12240 1 13634 1 6257 1 10800 1 15591 1 4155 1 9971 2 14838 1 11662 1 11479 1 9766 1 12400 1 14319 1 13767 1 15933 1 2797 1 13171 1 6444 1 4190 1 14075 1 social_status 5275 1 15481 1 13367 1 12524 1 15287 1 14469 1 10137 1 2312 1 4501 1 6351 1 12145 1 4110 2 15115 1 13063 1 510 1 13712 1 3404 1 2404 1 6230 2 15029 1 5324 1 4169 2 10511 1 11485 1 9397 1 3630 1 9400 1 1595 3 5458 1 13013 1 9381 1 1038 1 2231 1 15933 1 14186 1 14018 1 9917 9 13171 1 13000 1 5531 1 15836 1 6801 1 5995 1 1st 5442 1 3945 1 15126 1 8334 1 2738 1 2221 2 3314 1 14847 1 2324 4 11816 2 12011 2 6300 1 4019 1 7210 2 12848 1 1836 1 6864 1 2659 1 15898 3 5347 1 6527 1 6 1 1965 2 13192 1 9227 1 13063 1 7130 1 9891 4 7914 2 10867 1 7376 1 2390 1 11152 2 14351 1 8663 1 3483 1 16419 2 5676 1 9601 1 8891 2 1534 1 12962 2 15104 1 3213 1 14823 1 12674 2 5500 1 11697 1 15445 1 12569 1 11666 1 10131 1 1402 2 12215 1 3731 1 12216 4 4601 1 6780 1 13398 3 15378 1 9903 2 7033 1 11133 1 15935 1 9203 1 130 1 8363 1 1675 2 7358 1 13576 1 5202 2 811 1 4998 1 7715 1 5103 1 2915 1 8108 2 6257 1 14009 1 7558 1 15281 5 2950 1 12159 1 976 1 16405 1 414 3 15498 1 9919 27 2251 1 16318 1 1936 1 woodenly 13395 1 6329 1 9264 1 spontaneity 9509 1 12410 1 15118 1 12643 3 6371 1 10430 2 16019 1 1621 1 13797 1 5187 1 13421 1 12255 1 1595 1 7718 1 14456 1 12358 1 3025 1 2920 1 9093 1 12044 1 6852 2 3626 2 12941 1 yugoslavian 10325 1 12590 1 13648 1 4485 1 12181 1 12848 1 protuberant 10282 1 4200 1 803 1 3056 1 7123 1 9920 2 tree_branch 5618 1 7473 1 13034 1 poet_laureate 7789 1 7468 1 288 2 3483 1 1279 1 1084 1 16419 1 14838 1 10324 1 16494 1 14693 1 16127 1 575 1 8408 1 4023 1 2035 1 14232 1 scurry 12718 1 2306 1 6919 1 12543 1 1818 1 1368 1 591 1 arboreous 12318 1 5906 1 2263 1 14575 1 2452 1 8259 1 12738 1 8332 1 norwegian 1737 1 8285 1 12123 1 620 1 4091 1 3242 1 9921 18 884 1 8360 1 14557 1 5412 1 389 1 9221 2 11921 1 717 1 13110 1 11043 1 957 1 4110 1 670 1 7408 3 10899 1 12743 1 7840 1 6869 1 4884 1 9153 1 3531 1 223 1 2426 1 8108 3 6257 1 10485 1 15967 1 7729 1 3088 1 9247 1 3695 1 813 1 8631 1 13169 1 1558 2 15462 1 4912 1 16494 3 13648 1 14105 2 7195 1 11282 1 15933 2 13993 1 6625 1 16496 2 7966 1 4425 1 9384 1 682 1 unoriginality 16344 1 10889 1 5699 1 1510 1 3393 1 6852 1 11081 1 5240 2 7410 1 6972 1 backstop 2915 1 6109 1 slide_rule 4659 1 6230 1 1014 1 10237 1 16356 1 6454 1 6105 1 16183 1 8393 1 8567 2 971 2 6652 1 2960 1 5945 1 6123 1 sperm_cell 5442 1 4933 1 4467 1 5383 1 10782 1 12586 1 8559 2 6032 1 12075 1 9445 1 12485 1 14616 1 5033 2 3983 1 15729 1 3223 1 12177 1 4907 1 3580 1 7776 1 12228 1 overeating 101 1 6924 1 9761 1 6498 1 9477 1 4702 1 8221 1 13109 1 591 1 perfumed 15705 1 6180 1 7298 1 11505 1 10965 1 10430 1 6350 1 10660 1 6411 1 9219 2 11223 1 11542 1 9923 10 11211 1 13823 3 15543 1 9038 1 10543 1 1397 2 11976 2 1513 2 8686 1 6480 3 12273 1 11495 1 11608 1 7041 1 1240 1 14018 1 4220 1 1357 2 9010 1 6894 2 2986 2 pubic 16374 1 7774 1 15940 1 7068 1 3136 1 9550 2 2263 3 14351 1 6679 1 13736 1 7448 1 227 1 11827 2 9924 5 12786 1 6222 1 6375 1 1131 1 military_headquarters 13056 1 14529 1 8881 1 11686 1 1169 1 sabahan 13482 1 ransomed 4455 1 10492 1 12471 1 905 1 6359 1 7789 1 13322 1 8254 1 16189 1 11662 1 4271 1 fatten 5183 1 14848 1 1409 1 536 1 4540 1 scorpaenidae 6478 1 1457 1 gender_identity 5443 1 10867 1 2945 1 14009 1 10503 1 10333 2 15869 1 12003 1 6301 1 7835 1 8180 1 11090 1 779 2 7593 1 4706 1 5987 2 5766 1 11312 1 6477 1 11534 1 10575 1 archduchy 9644 1 12672 1 7300 1 literary_work 1660 1 8376 1 7299 1 3202 1 2862 1 10350 1 13821 1 13674 2 11121 1 10249 1 7374 1 14057 1 12113 1 333 1 8055 1 6655 1 11821 1 14232 2 9925 17 5841 1 9955 1 6557 1 15135 1 15864 2 1364 1 866 1 11495 1 4098 1 7917 1 12467 1 16191 1 6998 1 7981 1 14658 1 11769 1 3038 1 4933 1 529 2 15987 2 10364 1 2448 1 4499 1 9903 1 13110 1 14593 1 12534 1 3376 1 5795 2 14911 1 536 1 12578 1 3086 1 6633 1 1649 1 4672 1 9306 1 7560 1 15462 2 4912 1 11282 1 68 1 14018 1 14047 1 6792 1 perpetrator 1406 1 1737 1 3516 1 8462 1 10483 1 11330 1 11403 1 8875 1 10012 1 4335 1 6980 1 8088 1 3214 1 3534 1 12568 1 10544 1 2873 1 forewarn 11406 1 62 1 6167 1 7582 1 9937 1 10363 1 3159 1 13431 1 14128 1 6890 1 15472 1 1541 1 water_lettuce 5849 1 parka 11630 1 14377 1 6375 1 10690 1 pentoxide 5442 1 9488 1 16438 1 wonder_drug 5599 1 15479 1 11607 1 529 1 2266 1 10989 1 1873 1 hand_in_glove 13893 1 2643 1 15933 1 sulphur_bacteria 11061 1 at_hand 13668 4 491 1 14052 1 13315 1 8153 1 4260 1 10247 1 14574 1 9450 1 328 1 7405 1 16410 1 9283 1 10664 1 15199 2 6925 1 9021 1 12890 1 7413 1 9122 1 9927 15 11327 1 9400 1 1595 1 14236 1 11406 1 5528 1 793 2 12795 1 4247 1 12996 1 13467 1 11053 1 14658 1 13102 1 15674 1 14956 3 1920 1 16332 1 10492 1 4466 1 529 2 8442 1 9717 1 1784 1 584 1 4539 1 9479 1 14558 1 9101 1 2727 7 8192 1 2377 2 5913 1 15916 1 14007 1 4226 1 7256 1 4256 4 4041 1 14913 1 4887 1 13893 1 2581 1 1650 1 6980 1 5315 1 3568 1 5608 1 11749 1 5065 1 68 1 12260 1 5317 1 15123 1 13286 1 10605 1 601 1 perfectly 5442 1 5261 1 5443 3 4048 1 3473 1 3094 1 12556 1 3393 1 4397 1 4950 2 1395 1 9015 1 8083 1 5520 1 7823 1 16080 1 14647 2 334 1 2560 1 10544 1 12698 1 4529 1 14649 1 3404 1 6713 1 3992 1 3483 3 11358 2 5843 1 2843 1 5328 1 5271 1 6143 1 11154 1 12503 1 13751 1 10161 1 12569 4 13171 1 2634 1 11831 1 11769 1 6349 1 2057 2 3732 1 4250 1 10492 4 8214 1 11098 2 10430 1 8528 1 12439 1 4352 1 15845 2 16393 1 2131 1 3499 1 718 1 16338 2 2063 1 2285 1 11105 1 13576 1 8292 1 11716 1 2980 1 7649 5 6667 2 3862 2 10442 2 11784 1 1169 1 4912 1 14474 1 1957 1 12837 1 9728 1 1292 3 1055 2 2797 1 7776 1 1611 1 13897 1 6793 1 8373 1 3348 1 9184 1 2655 1 5802 1 2862 1 7049 1 6167 1 42 2 9116 3 2598 1 1493 1 12396 1 10727 1 5190 2 293 1 611 1 7667 2 990 1 4158 1 436 1 4680 1 5493 1 16417 1 8059 1 8796 1 9532 1 14160 1 9928 33 4927 1 2747 1 13790 1 14294 1 3449 1 8799 1 5500 1 553 3 3816 1 443 3 7481 1 9195 1 12795 1 795 1 14954 1 8997 1 10070 1 7421 1 10133 1 4164 1 4623 1 15987 3 10519 1 5430 1 9342 1 3367 1 14244 1 450 2 8676 1 12742 1 15457 1 6064 1 1250 1 5888 4 2377 1 877 1 16436 3 12034 1 14041 1 15886 1 1014 1 11002 1 15459 1 3830 1 6812 1 5439 6 2247 1 13975 2 16258 1 6890 1 7009 1 13736 1 9683 1 4830 1 11812 1 4704 1 4511 1 882 1 4707 1 15752 1 144 3 10018 1 12353 1 12620 1 14047 1 1861 1 16210 1 2956 1 commotion 12585 1 542 1 5184 1 4846 1 11538 1 10758 2 14382 2 12814 1 12011 1 13799 1 12815 1 13674 1 12041 1 7238 1 1964 1 4446 1 11821 1 4669 1 13092 1 4617 1 11001 1 14650 1 4530 1 9929 7 14323 1 11327 1 7042 1 11662 1 8180 3 5500 1 2306 1 15933 1 4086 1 13685 1 925 1 11287 1 genus_martynia 4105 1 14251 1 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13404 1 10298 1 10543 1 1802 1 11398 2 5573 1 842 1 11641 1 11108 1 13196 1 14612 1 13076 1 9532 1 7189 1 8124 1 9583 1 14162 1 11748 1 16356 1 2582 1 2054 1 2085 1 14636 1 13085 2 10051 6 cliftonia 3618 1 15082 1 10094 1 orphanage 11635 2 484 1 14820 1 6890 1 1963 1 4539 1 12534 1 5571 1 12795 1 5694 1 8512 1 10233 1 11474 1 9920 1 5840 1 2577 1 baggy 12537 1 3750 1 14432 1 1619 1 3483 1 5903 1 2655 2 5214 1 5286 1 845 1 9259 1 7158 1 1519 1 882 1 13323 2 1398 1 13171 1 6444 1 6375 1 2277 1 15153 1 15924 1 incest 10052 5 9186 1 10333 1 8156 1 2312 1 13777 1 15170 1 11693 1 11461 1 14979 1 4240 1 688 1 3875 1 15861 1 14698 1 15918 1 8223 2 6979 1 2104 1 2795 1 2902 1 8163 1 10943 1 12416 1 2815 1 11983 1 270 1 1656 2 15574 1 genus_delphinium 13967 1 15241 1 4099 1 7360 1 12711 1 14607 1 5173 1 9973 1 2312 1 28 1 1560 2 1200 1 15636 1 2191 1 6965 1 16444 1 10094 1 9216 1 589 1 2921 1 9206 1 7007 1 293 1 4698 1 611 1 truncated 5959 1 6378 1 9882 1 15987 1 8553 1 10053 7 5718 1 16092 2 7912 1 7143 1 13674 1 14212 1 15089 1 62 1 15827 2 647 1 9350 1 11941 1 1099 1 11572 1 5240 1 15797 1 12744 1 14009 1 14563 1 14424 1 4169 1 9704 2 9705 2 14810 1 11086 1 10257 1 2613 2 14583 1 7764 1 3450 1 13924 1 10018 1 11426 1 floral 1330 1 5671 1 7752 2 2850 1 16459 2 10535 1 7248 1 15241 1 10704 1 12981 1 10310 2 10054 8 13968 1 9186 1 13970 1 13834 1 12848 1 7711 1 15973 2 8555 1 1395 1 11147 1 14470 1 5347 1 5754 1 14196 1 13583 1 2806 1 7340 1 611 1 12943 1 15684 1 8012 2 2303 1 14980 1 15219 1 8796 1 16367 1 1627 1 3604 1 16405 3 3816 1 5942 1 998 1 13768 1 14047 2 9337 1 2714 1 7350 1 parttime 5963 1 13674 1 asia_minor 6645 1 10692 1 7365 1 15254 1 11431 2 8879 1 7570 1 10535 1 4238 1 884 1 253 1 3164 1 254 1 13159 1 14056 1 13821 4 7692 1 13086 2 1200 1 6018 1 6133 1 956 1 2436 1 8881 1 8083 1 13227 1 9285 1 8339 1 7574 3 3320 1 14196 5 15134 1 2836 1 2560 3 7442 1 12822 2 8012 1 8458 1 8702 1 5455 2 3991 1 3992 1 14065 1 8891 1 13096 1 3729 1 3492 1 2198 2 15701 1 5783 1 10701 1 12768 1 8581 1 2970 1 3733 2 7199 1 4468 1 12123 2 16459 1 10704 1 13300 1 13367 1 5173 1 6287 1 11637 1 7711 1 4007 1 4539 1 1216 1 10168 1 10169 1 4906 1 13176 3 11924 2 8645 1 12244 1 14005 1 2729 1 3501 1 6419 1 481 1 16398 1 6977 1 10234 1 8040 1 6480 1 2136 1 7647 1 10176 1 5228 1 7900 1 14998 1 14527 4 1560 5 8421 1 13309 1 9587 1 13845 1 7904 2 1107 1 1055 1 1685 1 2921 13 11517 1 7598 1 2861 1 5555 1 13708 1 15787 1 9922 1 1755 2 10310 1 9186 2 11859 2 6046 2 10055 37 4263 1 12847 1 7210 1 7158 1 5347 5 7853 1 8856 1 11796 3 9655 1 5189 1 293 5 2873 1 11188 1 10253 1 1765 1 10321 2 1766 1 13462 1 369 2 7103 1 11946 1 3642 1 10514 2 10325 1 3447 1 3760 3 6686 2 8434 1 12213 1 4994 1 10131 1 9664 1 11474 3 8491 1 7350 1 802 1 9132 2 13967 1 5997 1 15453 1 13970 1 6449 1 1709 1 9203 1 9937 2 1006 1 10743 1 11000 1 15026 1 12480 1 3457 1 1645 1 15939 1 9206 1 5361 1 2244 2 4829 1 8193 2 11678 1 12943 1 2426 1 13079 1 8942 1 10212 1 14838 1 2613 1 3228 1 8502 1 9945 1 12811 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9205 1 8339 1 5076 1 1685 1 1752 1 3110 1 15574 3 10070 1 trophies 4143 1 11527 1 14282 1 8527 1 7413 1 7178 1 2749 1 3779 1 2720 1 8980 1 2911 1 14525 1 5024 1 536 1 gaming 7711 1 14532 1 3741 1 10310 1 16008 1 2662 1 public_presentation 11461 1 5785 1 2179 1 15058 1 3242 1 12669 1 5957 2 parish 11966 1 15126 1 7708 1 13367 1 1084 1 3094 1 10247 1 15910 1 8529 1 704 1 12848 1 13422 1 6183 1 11147 1 9478 1 14644 1 13490 1 6109 1 10057 10 3766 1 536 1 6757 1 9304 1 11527 1 2332 1 13426 1 10255 1 3483 1 16419 1 5270 1 1893 1 12948 1 9644 1 13408 1 16356 1 12672 1 1595 1 5271 1 8892 4 14584 1 2456 1 5458 1 1291 1 16483 1 5139 1 1466 1 13451 1 15225 1 13434 1 12453 1 13171 1 1194 1 11769 1 4029 1 13103 1 panadol 9273 1 869 1 15700 1 11576 1 1205 1 12374 1 4949 1 sophistication 7532 1 7567 1 15398 1 1333 1 9390 1 4369 1 11905 1 14415 1 7126 1 9558 1 11588 1 12145 1 7374 2 15436 1 9285 1 4745 1 16081 1 3874 1 14060 1 2836 1 6903 1 4211 1 11081 1 10058 9 2332 1 9502 1 524 1 5214 1 1811 1 6929 1 4101 1 3813 1 9506 1 8209 1 12795 1 4219 1 9663 1 14954 1 14587 1 12312 1 8408 1 2445 1 15877 1 9830 1 8093 1 10363 1 8673 1 12643 2 9243 1 9407 1 2723 1 7387 1 62 1 8192 2 7616 1 14525 1 2944 1 2945 1 5226 1 15853 1 278 1 4169 2 4630 1 2581 1 6980 1 14838 1 10689 1 1987 1 6634 1 4706 1 4707 1 4512 1 8182 1 14187 1 6192 1 1611 1 11789 1 5957 1 receivership 6710 1 1145 1 1370 1 15089 1 close_up 10295 1 12410 1 6248 1 10165 1 5700 1 13893 1 8653 1 7169 1 14271 1 15978 1 linear_unit 15293 1 906 1 11716 2 8702 1 5705 1 9527 3 5733 1 12522 1 11033 1 9911 1 10059 10 14352 2 16258 1 7192 1 4335 2 2131 1 519 1 4643 1 15390 1 7291 1 8394 1 14018 1 2220 1 2394 2 16318 1 1389 2 9724 4 11312 2 2408 1 filter_tip 7973 1 eunuch 10182 1 13171 1 8751 1 14832 1 11269 1 anorectic 11870 1 4156 1 6389 1 531 1 woebegone 12585 1 12971 1 15773 1 13118 1 2546 1 13736 1 11209 1 10934 1 7587 1 15601 1 11547 1 7239 1 642 1 4833 1 6375 1 570 1 2873 1 soundly 16436 1 11033 1 1362 2 10868 1 8783 1 11358 1 11495 1 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6929 1 11327 1 15766 1 12563 1 2051 1 16051 1 10062 9 6873 1 11405 1 3729 1 5679 1 12279 1 8128 1 12572 1 906 1 1737 1 13035 1 3145 2 2720 1 8833 1 1980 1 9937 1 16224 1 5053 1 10097 1 4110 1 4038 1 13696 1 15886 1 14143 1 12099 1 8392 1 14996 1 12003 1 1103 1 14224 1 8180 2 14147 1 4388 1 1055 1 1656 1 checkered_lily 12822 1 8458 1 6080 1 12283 1 5455 1 2718 1 13462 1 447 1 15026 1 9922 1 1818 1 15693 1 4954 1 2036 1 11474 1 simplified 9830 1 10492 1 2639 1 9242 1 15774 1 9067 1 12371 1 2301 1 2754 1 4429 1 8729 1 3223 1 12724 1 5688 1 3404 1 13731 1 918 1 2534 1 3483 1 9764 1 3759 1 439 1 4150 1 9380 1 10063 6 13648 1 14428 1 14018 1 4328 1 11182 1 2488 1 largeness 1330 1 4995 1 14352 2 11492 1 8464 1 15996 1 8361 1 15089 1 62 1 10064 6 14979 1 4526 1 16119 2 14525 1 10575 1 12090 1 setaria 7015 1 8886 1 8171 1 6681 1 14606 1 10142 1 12502 1 3354 2 1546 1 6965 1 12717 1 family_nymphaeaceae 15412 1 6442 1 14355 1 3555 1 10065 5 11147 2 8321 1 11502 2 5173 1 6801 1 1457 1 delay_line 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3377 1 12590 1 7130 1 8769 1 2873 1 10866 1 11999 1 5914 1 7648 1 15279 1 11485 1 2247 1 13027 1 9368 1 8282 3 15672 1 14236 1 314 1 3290 1 11870 2 7730 1 11239 2 5971 1 1092 1 15 1 10770 1 2875 1 1596 1 7313 1 14571 1 10532 13 4724 1 10218 1 cough 16261 1 1331 1 10757 1 13552 1 16321 1 9691 1 13744 1 1397 1 6211 1 14458 1 11622 1 5841 1 6713 1 1807 2 15136 4 9704 1 85 1 1401 1 963 4 1595 2 14066 1 8161 1 12501 1 14583 1 8212 1 6660 1 14205 2 9714 1 9097 1 13689 2 5174 1 9775 1 9517 1 13442 1 7322 2 4476 1 2646 1 483 2 484 1 213 2 1681 1 3630 1 12003 1 1288 1 13514 1 4082 1 8180 2 13380 1 11240 1 13182 1 6161 1 3571 1 2016 1 12388 1 3699 3 10577 1 5114 2 6549 1 3750 1 8541 1 10660 2 10503 2 3511 1 16114 1 12011 2 15789 2 5126 1 8309 1 11045 1 10727 1 5616 1 2025 1 1119 1 8313 1 992 1 2665 1 7413 2 15098 1 12792 1 8314 2 15441 1 4099 2 13786 1 10195 4 996 1 6686 1 3761 1 12795 1 10262 1 869 2 4933 2 11266 1 3145 1 10813 1 13797 1 8133 1 8738 1 10077 2 12609 1 691 1 12943 1 2945 2 2760 2 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2547 1 2691 1 13736 1 4171 1 2767 1 14969 3 15251 1 12416 1 8561 1 6263 1 3026 1 9753 1 10533 1 5957 1 rhyming 15531 1 7527 1 9892 1 9210 1 3736 2 16047 2 15459 5 7900 1 13037 1 4359 1 10333 2 14174 1 4761 1 4950 2 8201 1 14140 1 4009 2 971 2 1562 2 1967 2 6801 1 11482 1 16216 1 6494 2 generosity 4394 1 13418 1 12693 1 13087 1 1587 1 6081 3 8512 1 3173 2 4338 1 8961 2 8517 1 7189 1 16330 1 14322 1 7022 1 1595 1 15959 1 10034 1 5087 1 13171 3 12312 1 11917 1 10842 1 12572 1 4659 1 9830 2 1405 1 12643 2 11635 1 11709 1 3689 3 8764 1 13575 1 7587 1 15777 1 9038 2 12910 1 14941 1 2212 1 16398 1 13696 1 2404 1 15918 1 484 2 2288 1 10369 1 13946 1 12003 1 8719 1 8180 3 7653 1 15497 1 8849 2 5110 1 416 1 7969 1 10578 22 7778 2 11587 1 12981 1 111 1 10247 1 15790 1 13777 1 12848 1 6988 1 2864 1 10795 6 10727 1 7099 2 3582 2 4211 1 8604 1 14028 1 3072 2 4159 1 8796 1 4099 1 6873 1 10062 1 1310 1 7544 1 7545 1 6936 3 8128 1 2159 1 8249 1 2036 1 10460 1 7802 2 3145 1 11536 1 13968 1 10884 1 1980 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1209 4 7705 1 10289 1 8975 1 10430 1 10556 1 8582 1 837 2 12830 1 5974 1 2912 1 9775 1 4253 1 14941 1 9907 1 9779 1 11106 1 6102 1 3502 1 10564 1 6231 1 11646 1 3744 1 3198 1 594 1 6849 1 13380 1 13765 1 13766 1 4612 1 2918 1 6485 1 13247 1 10577 1 7598 1 8592 1 15859 1 11793 1 12326 1 8122 1 3872 1 1762 1 5492 1 866 2 6498 1 1915 1 6871 1 6241 2 12995 1 2669 1 9400 2 10325 1 5353 1 5995 1 3076 1 1635 2 15987 2 10882 1 8135 1 11268 1 9937 1 3376 1 4502 2 12346 1 5888 2 2686 1 10750 1 2179 1 16491 1 7008 2 10340 1 1517 1 1137 1 9685 1 14969 1 11282 1 6763 1 14446 1 12352 1 10080 1 12353 1 1656 2 12228 1 7014 3 3238 1 calculating 10578 1 7567 1 7366 2 1793 1 1565 1 13313 1 5733 1 9184 1 12011 1 1490 2 7238 1 6081 1 13781 1 14060 1 6655 1 7376 1 3679 1 4561 1 8663 3 8517 1 5214 1 6929 1 4596 1 16158 1 12856 1 12898 1 9602 1 15058 1 15445 1 15572 1 3008 1 10842 11 5427 1 12643 1 15840 1 12341 1 15910 1 585 1 14693 1 15276 1 14864 1 7805 1 14558 1 1101 1 2316 1 12578 2 15940 3 9305 1 4041 1 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9632 1 4243 2 7191 1 13749 1 16218 1 8350 1 3617 1 2903 1 712 1 8211 1 12569 1 5089 1 2395 1 4138 2 3038 2 13883 3 1211 4 2906 1 8093 3 4466 2 15479 1 341 1 8975 3 3853 1 8213 2 5331 1 11635 1 11223 1 10559 1 10558 1 11921 1 16222 1 4143 1 8362 1 5598 1 15846 8 2284 1 10846 108 15339 1 14941 1 1099 3 13374 1 14005 1 1604 1 5102 1 3859 2 9639 1 4256 1 2404 1 3741 1 10564 2 8587 1 2135 1 11235 1 11512 1 2511 1 10044 1 14012 1 5228 1 10970 1 846 1 3744 3 213 1 15968 1 14282 1 6105 2 13765 1 9787 1 15497 1 2797 1 15225 2 15855 1 980 1 11791 3 2409 1 4865 1 10577 1 3061 5 4017 1 2143 1 12201 1 12586 1 6984 2 9650 1 4155 1 6859 2 4156 1 4616 7 16117 1 12848 1 9116 4 12721 1 1907 1 11395 1 6238 1 8730 2 13644 2 12590 1 5873 1 2927 6 9926 1 12725 1 3358 1 10867 2 16479 1 13530 1 5128 1 9122 1 866 1 2535 1 2153 1 6498 2 8863 1 5498 1 12593 1 8734 2 2670 1 10590 1 7858 1 10992 1 10328 2 14429 1 126 3 8248 2 10068 1 4029 1 15877 2 2036 2 4164 1 12471 1 5883 1 1124 1 15108 1 10071 1 15241 1 8611 1 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6596 1 7314 1 6375 1 7675 1 10903 1 12863 1 528 1 3218 1 4847 1 6566 1 13626 2 15591 2 10265 1 12906 1 1980 1 8529 1 13266 1 11739 1 950 1 1645 1 4859 1 1604 1 4446 1 724 1 11339 1 3896 2 11572 2 3341 1 7774 1 10851 17 14772 2 11108 4 484 1 2546 1 9444 1 213 1 9311 1 13408 1 13736 1 595 1 8983 1 7260 1 1054 1 6819 1 2797 1 12781 1 11789 1 16318 1 11612 1 600 1 11584 2 11286 1 11287 1 13085 2 negligible 10421 1 1170 1 6495 1 8076 1 8842 1 14352 1 9759 1 9477 1 5815 1 4361 1 8396 1 14726 1 2747 1 996 1 3778 1 11686 1 12353 1 10832 1 14956 1 13002 1 9153 1 rainy 9714 2 16015 2 12508 1 11752 1 3145 1 10136 1 6879 2 12011 5 11816 1 7238 1 10298 2 15544 1 15564 1 8445 2 4185 2 3501 1 14028 1 7914 2 6419 5 7410 1 14834 1 10852 9 7376 2 5455 1 5604 1 12669 1 11512 2 13893 1 7360 2 2563 2 14323 7 8967 1 1290 2 3760 1 16494 2 5065 2 6389 2 15445 6 13994 1 12546 1 3941 1 10395 1 11456 1 8332 2 capuchin 12335 1 2869 1 3988 1 transplacental 10492 1 12414 1 13855 1 1660 1 creditworthiness 11693 1 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7729 2 13260 1 14936 1 11675 1 9033 1 12083 1 12500 1 8246 2 3158 1 11450 1 6239 1 670 1 10018 1 13706 1 pitcher_plant 15817 1 1583 2 15404 2 12487 1 7564 1 16124 1 5751 1 3875 1 7687 1 12815 1 13718 1 11474 2 4263 1 3623 1 travel_back_and_forth 10018 1 1014 1 15933 1 11662 1 15386 1 omnipotence 10420 1 2326 1 9264 1 2369 1 4423 1 14482 1 2556 1 deucedly 4099 1 6390 1 7643 1 8395 1 16037 1 724 1 2307 2 7410 1 sandalwood 8166 1 1709 1 7015 1 10571 1 3653 1 7824 1 7922 1 13893 1 16115 1 7744 1 jagged 12198 1 15994 1 12138 1 14507 1 8153 1 4179 1 14607 1 8853 1 329 1 1200 1 7471 1 13711 1 3677 1 8886 1 10321 1 2999 1 13023 1 3992 1 14160 1 6681 1 745 1 4134 1 6442 1 10128 2 3729 1 6933 1 6281 1 10131 1 1368 1 7421 1 10492 1 4030 1 6379 1 11331 13 2164 3 14935 1 236 1 10431 1 13970 1 1821 1 14212 1 5029 1 14626 1 11171 2 877 1 12321 1 12578 1 1932 1 11956 1 13118 1 13698 1 13276 1 8980 1 3744 1 7148 1 1893 1 7010 2 9945 1 4387 1 7295 2 10079 1 13343 1 8720 1 9148 1 16178 1 9384 1 11427 1 5112 1 safe_harbor 6716 1 12957 1 8876 1 91 1 702 1 8862 1 15077 1 procurer 9545 1 9352 1 4202 1 12059 1 slyly 223 1 11399 1 3724 1 9186 1 13795 1 703 1 11048 1 5576 1 1941 1 548 1 10119 1 1069 1 15497 1 11283 1 13452 1 1968 1 2079 1 continuing_trespass 8292 1 7413 1 15187 1 13434 1 7477 1 3791 1 12986 1 5056 1 2583 1 11210 1 prestissimo 4998 1 4099 1 4761 1 1660 1 10544 1 284 2 monochrome 12409 1 14010 1 12020 1 12669 1 13277 1 12164 1 4907 1 16329 1 12807 1 4172 1 saved_up 7577 1 484 2 11697 1 3496 1 9717 1 genus_parkia 1932 1 4529 1 4143 1 11956 1 15267 1 9635 1 3258 1 9047 1 8581 1 4263 1 propanol 5442 1 8734 1 917 1 3793 1 10543 1 1009 1 in_hiding 7799 1 4158 1 5778 1 12368 1 1656 1 14765 1 5412 1 5311 1 correlation_coefficient 12730 1 9639 1 12943 2 13209 2 4367 1 1062 2 10698 6 3830 1 8156 1 12856 2 2312 2 12110 2 11332 7 14782 1 10097 4 13228 1 2572 2 3161 1 7733 1 12612 1 11490 1 11584 1 rejected 6896 1 7069 1 7366 1 14052 1 11552 1 1992 1 1145 1 12556 2 11971 1 16079 1 6080 1 11147 1 2326 1 11819 1 15899 1 7239 2 7697 1 8512 1 10617 1 15046 1 6465 1 10356 1 10619 1 15904 1 12047 1 14650 1 5648 1 8517 1 9090 1 10840 2 1595 2 8348 2 152 1 9506 1 12175 1 3493 1 5848 1 2394 1 4190 1 12122 1 10290 1 8167 1 13034 1 6149 1 1211 1 11367 1 5785 3 15774 1 13367 1 1820 1 8026 1 9716 1 3926 2 12181 1 2911 1 531 1 12317 1 8099 1 6351 1 7712 1 1162 1 8288 1 11372 1 5911 1 14140 1 5722 1 971 1 8104 1 2978 1 8365 1 410 1 2577 1 4542 1 6102 1 9306 1 11512 1 8178 1 13637 1 14527 2 6543 1 14225 1 16036 1 11383 1 14409 1 13182 1 2797 1 11933 1 15164 1 16347 1 4363 1 9109 1 2016 1 14946 1 7531 1 351 1 8047 1 5119 1 15430 1 927 1 5735 1 12458 1 16472 1 2223 2 4487 1 11588 1 10249 1 2865 1 6924 1 5414 1 6365 1 11796 1 7783 1 13583 1 1176 1 9598 1 7667 1 12270 1 11400 1 3707 1 4378 1 14614 1 14682 1 6241 1 11049 1 11662 1 7672 1 12334 1 14236 1 6746 1 10325 1 15237 1 12859 1 6685 1 2366 2 15933 1 10131 1 14956 1 8438 1 7218 1 12736 1 8255 1 6247 1 10137 1 8870 2 13800 1 8137 1 7992 1 11333 26 10076 1 7613 1 9614 1 4110 1 9268 1 13405 1 13214 1 8192 1 14626 1 1510 1 15115 1 5573 1 4446 1 5888 2 3298 1 7226 2 1982 1 8391 1 2759 1 1014 1 11002 1 13075 1 13809 1 15459 1 1933 1 8942 2 14838 1 5315 1 9485 1 11278 1 14775 1 5065 1 14776 1 4707 1 7813 1 13666 1 all_the_same 10415 1 4198 1 2688 1 6896 1 7531 1 5648 1 13276 1 10106 1 4193 1 15406 1 10804 1 9071 1 16469 1 10068 1 2879 1 2141 1 5609 1 blackfoot 13648 1 5461 1 10070 1 wedding_gift 839 1 6498 1 3089 1 3329 1 9081 1 2904 1 11269 1 11634 1 arabian_desert 2426 1 4590 1 7012 1 5347 1 15701 1 12213 1 15136 1 4585 1 14059 1 13552 1 4449 1 12484 1 328 1 14527 2 shell_out 6106 1 1784 1 12795 1 484 1 11602 1 8223 1 3820 1 12228 1 9493 2 3269 1 coherence 8292 1 9021 1 12643 1 16217 1 529 1 4995 1 11495 1 13640 1 8529 1 13736 1 10672 1 14281 1 3617 1 5828 1 10375 1 12126 1 9350 1 7309 1 14558 1 835 1 14044 1 1818 1 11334 5 8192 1 12353 1 11188 1 impassive 869 1 10396 2 12643 1 13626 2 1924 1 3475 1 7990 1 6062 2 13800 2 62 2 4695 2 4966 1 10076 1 13490 2 12741 1 8904 2 11335 8 7616 1 7742 1 7410 6 4158 3 11400 2 11781 2 9306 1 11002 1 9441 2 4999 1 8662 1 9532 3 15279 1 10600 1 2916 2 1681 1 9400 2 14702 2 9330 1 10376 1 4688 2 16484 1 7432 2 15445 1 13171 1 6444 2 13938 1 on_time 10415 1 387 1 1758 1 14453 2 12041 5 11906 1 7302 1 15729 1 13490 1 15528 1 6735 1 10726 2 5964 1 7856 1 8084 1 7410 1 11943 1 1201 1 5082 1 6530 1 4212 1 9021 1 6498 1 13260 1 4099 1 171 1 5847 1 6089 1 153 1 15444 1 13684 1 15933 3 126 1 5427 1 2036 1 8865 2 9340 2 9342 1 7059 1 16222 1 12032 1 6035 1 14693 1 7490 2 4624 1 3119 1 9377 5 3719 1 14771 1 11336 9 2682 1 10007 1 5573 3 2882 1 2686 1 12034 2 12971 1 2099 1 13634 1 6102 1 7148 1 16104 1 13408 1 14838 1 3158 1 3534 1 13084 1 12416 1 11963 1 10080 1 trapshooting 702 1 celebrity 15774 1 3113 1 3189 1 16117 1 1458 1 12607 1 7956 1 6365 1 1646 1 11338 8 4114 1 5190 1 8458 1 11843 1 4680 1 2915 1 11927 1 12003 1 14199 1 6294 1 7076 1 4172 1 14739 1 6010 1 14504 1 9976 1 8946 1 10577 1 corrective 7707 1 3634 1 1211 1 9240 1 5443 2 16391 2 10165 1 9472 1 5534 1 10265 1 5654 1 10116 1 1949 2 14905 1 2476 1 12375 1 2822 2 14626 1 2879 1 11337 8 536 1 2646 2 3601 2 12398 1 11684 2 12102 1 1389 3 12620 1 5714 1 9920 2 recreate 3113 2 11792 1 11477 1 12623 1 3701 1 6881 1 7661 1 16381 1 14626 1 13091 1 5888 1 10462 1 4556 1 9926 1 3705 1 4626 1 3546 1 9021 1 10483 1 6241 1 6871 1 2581 1 1681 1 12762 1 1180 1 314 1 11749 2 8163 1 6545 1 11282 1 15933 1 16387 1 15445 1 7611 1 7313 1 15946 1 1959 1 repeatable 14274 1 3815 1 5528 1 573 1 38 1 12056 1 2173 1 12255 1 15077 1 unforfeitable 12569 1 5810 1 silver_grey 16329 1 9206 1 13977 1 7410 1 inflectional_ending 15459 1 1099 1 8203 1 822 1 16242 1 14929 1 black_swan 12959 1 16024 1 ruddy_duck country_dancing 14197 1 9470 1 8214 1 3218 1 11709 1 1199 1 7210 1 12145 1 9116 1 16484 1 3520 2 12959 2 6801 1 10522 1 radioactive_decay 5254 1 1680 1 11340 5 13064 1 3473 1 12792 1 13532 1 4099 1 2129 1 15744 2 8628 3 559 1 1947 1 16177 1 11894 1 4527 2 5774 1 15513 1 10623 1 12416 1 15411 1 covet 15818 1 10202 1 12002 1 5846 1 15089 1 11406 1 2009 1 7660 1 14186 1 8100 1 15953 1 9109 1 9492 1 antiquities 16357 1 2513 2 3789 1 8223 1 14060 1 5412 2 unnaturalness 3824 1 14750 1 13183 1 succeeding 5580 1 4659 1 351 1 9770 1 9650 1 9717 1 15788 1 12703 3 14055 1 16117 1 7210 2 13439 1 9775 1 5199 1 1395 1 15729 1 11924 1 7473 2 10186 1 13461 1 1130 1 510 1 536 1 293 1 611 1 5618 1 12270 1 9021 1 3679 1 11341 6 6278 1 1322 1 2966 1 14838 1 7361 1 5033 1 7012 1 10901 1 1107 1 13411 1 12569 1 10604 1 4689 1 2920 1 10981 1 ajar 3320 1 4129 1 16258 1 appetizer 12521 1 7398 1 15618 1 7367 4 15062 1 789 1 8156 1 11342 5 15170 2 13422 2 9091 1 16494 1 7120 1 5298 1 3376 1 7731 1 1596 1 11106 1 collapse 5442 1 1793 2 1660 1 6524 1 15467 2 7939 1 13820 1 9886 1 1394 1 7757 1 2117 1 14979 1 9285 2 3398 1 13824 1 5449 1 6654 1 8084 1 4529 1 150 1 15048 2 892 1 10421 1 828 1 5778 1 4591 1 2266 1 11358 1 4530 2 7189 3 4243 1 5328 1 16158 1 15056 2 7636 1 7581 1 13029 1 5849 1 4654 1 1818 2 14658 3 9093 1 9369 2 2204 1 13505 1 9717 1 11308 1 2725 1 10295 1 6035 1 11227 1 585 1 779 1 13836 1 7836 1 15710 1 13575 1 3859 8 26 1 9172 1 155 2 13177 2 4200 1 2789 4 2858 1 12002 1 2214 1 12713 1 2582 1 11577 1 5033 2 1169 1 15968 1 8180 2 6850 2 7045 1 4085 2 7393 1 2592 1 14339 1 13053 1 12324 1 9390 1 10660 1 10789 1 1961 1 12924 1 5736 2 4156 3 4093 1 9259 1 547 1 12849 2 6167 1 3276 2 5347 1 4429 1 5988 1 120 1 15506 1 16475 1 8661 1 13711 1 7474 1 2532 1 5239 1 8056 1 2873 1 14886 1 14612 2 2152 1 16047 1 11403 1 675 1 9601 1 4099 1 1624 4 614 1 3813 1 10325 1 1428 1 5500 1 15933 1 14428 2 14618 1 13965 1 2232 1 14430 1 5995 2 13914 1 1004 5 3145 1 3367 2 3886 1 14691 1 9477 1 5358 1 7990 1 5948 1 13799 2 6447 1 3958 2 622 1 14693 1 11739 1 876 2 16196 1 10682 1 5819 1 9546 1 8192 1 5888 1 5056 1 7743 1 16436 1 12943 1 15186 1 10597 1 9550 1 14500 1 10751 1 8939 2 16437 1 13077 1 4169 2 2885 1 11343 35 4323 1 14634 1 4772 1 7120 1 2889 1 5067 1 14044 1 3386 2 4512 1 5694 2 10018 1 12353 1 12620 2 6894 3 3671 1 swinton 1635 1 10379 1 15576 1 6890 1 12448 1 15845 1 16274 1 14728 1 3082 1 51 1 4258 1 10383 1 15651 2 7426 1 8512 1 15115 1 6660 1 11612 1 chester 4719 1 936 1 russet 7541 1 15109 1 5179 1 9635 1 10660 1 4335 1 13096 1 4263 1 14931 1 16078 1 7158 1 7904 1 5035 1 2605 2 12022 1 4327 1 10962 1 15082 1 10259 1 14933 1 13583 1 2043 1 7314 1 3501 1 13302 1 6022 1 pectoralis_major 14388 1 10629 1 9996 1 5616 1 5171 1 4897 1 1225 1 16162 1 apocynaceae 8529 1 4870 1 logwood_tree 10494 1 3176 1 3653 1 14921 1 12326 1 3258 1 2873 1 9662 1 chili 14859 1 4933 1 6777 1 15451 1 3112 1 5262 6 13672 3 4676 1 10461 1 15826 1 2489 1 989 1 10253 1 12246 2 9057 1 16271 1 12192 1 13859 1 5348 1 10387 1 11438 1 11088 1 11204 1 3684 1 11344 7 11017 1 4579 1 16358 1 7393 1 4220 1 1818 1 1596 2 5972 1 4365 1 179 1 11771 1 clanking 3396 2 12439 2 2510 2 4950 2 7590 2 2873 2 family_cracidae 14782 1 15599 1 5555 1 1755 1 3636 1 9635 1 1596 1 14270 1 9180 1 tack 9494 1 9527 1 14208 2 10556 2 8272 2 6900 1 10559 1 5247 1 3676 1 12094 1 6707 2 4338 1 14855 2 9521 1 6557 1 1383 1 2415 2 5362 1 846 1 4359 1 8281 1 13736 2 15968 6 7565 3 13432 2 9860 1 11345 15 7169 1 reptile 13668 1 7368 3 12231 1 7879 1 16377 1 13419 5 13821 1 1146 1 10475 2 7237 1 11147 1 11295 1 3676 1 8512 1 10225 1 14065 1 6278 4 1534 5 13935 6 4062 1 14584 1 7636 3 15768 1 1667 1 13434 1 11443 3 11985 1 3555 2 6907 2 401 1 3113 1 9833 1 3556 2 272 2 5276 1 13037 1 2972 1 2062 1 20 1 11451 1 11171 2 13576 1 11572 1 7040 1 12513 1 4910 1 9587 1 5180 1 10656 4 15007 1 14227 1 10790 3 1691 2 15288 1 5412 1 11726 3 12785 2 7159 1 5188 1 12266 1 1968 1 5931 1 13712 2 7541 1 8057 1 1840 2 5418 1 6310 1 6440 3 3815 1 5426 3 12280 1 2089 5 9860 1 10131 1 14490 2 54 5 11474 2 802 1 9477 1 4698 1 16436 1 7555 1 14499 2 6573 1 9210 1 8559 1 185 2 12538 1 6574 1 16207 1 9875 3 12414 1 4831 1 5828 2 15579 2 6952 4 12543 4 11346 70 11612 2 11614 3 14111 2 3310 1 13609 1 4332 4 12554 1 5835 3 10539 1 3983 2 10416 1 7440 1 3841 1 8702 1 4591 1 9630 1 8574 1 3992 2 1874 1 3483 1 4720 3 6345 1 16220 4 13878 1 1596 1 14658 1 11632 1 11917 1 5713 1 9899 1 5090 4 8213 1 9635 1 11222 1 204 1 1472 3 12181 2 16334 2 4352 3 3189 1 4143 1 9103 1 3501 1 4606 1 12578 1 11232 1 16102 1 3195 1 4479 1 1104 1 12066 2 13515 2 6728 1 3056 1 12070 1 850 1 13382 2 2921 1 8851 1 6487 1 10719 1 15084 1 9791 3 9648 16 15353 3 2143 1 3636 15 8595 13 4155 3 8853 1 4263 1 5347 2 12723 1 11941 1 7473 1 1909 1 3875 3 5618 2 13783 1 16478 1 3074 1 1620 1 6997 8 11661 1 227 1 14427 1 14297 1 746 1 3366 1 7486 1 14956 1 7350 1 11670 2 505 1 9405 4 6502 1 13004 4 13652 1 12342 3 7862 3 3220 1 1508 2 5753 1 12092 1 13272 1 3223 1 1510 1 10745 1 5361 2 1131 1 11956 1 12223 1 6890 2 13922 1 7746 2 7120 2 10604 1 12353 2 14189 1 14972 1 bay_rum 10253 1 1227 1 raptorial 2090 1 3072 1 16153 1 15541 1 3636 3 15651 1 8512 2 12863 4 11612 1 3038 1 11929 1 16170 2 12848 1 selected 6011 1 1660 2 8153 2 9423 1 2553 1 15822 1 13422 1 11147 2 7374 3 5964 1 4338 1 334 1 763 1 10698 1 12561 1 5648 1 9291 1 11084 1 14198 1 6217 1 12563 1 8088 2 12898 1 7135 1 8209 1 267 1 8021 1 13684 2 14270 1 10038 1 2057 1 13034 1 1406 1 9242 1 13754 1 8582 2 12371 1 11989 1 11838 1 4408 1 14274 2 4143 1 4734 1 585 2 10846 1 13837 1 10097 5 4907 1 4857 1 3740 1 14141 1 10043 1 536 1 16399 1 8292 2 2509 1 12377 1 13633 1 5103 2 14010 1 14012 1 1166 1 11316 1 728 1 12381 1 13120 1 1749 1 13895 1 12260 1 14018 2 6921 1 2016 1 11182 1 13581 1 9920 1 5552 1 2989 1 5733 1 8047 1 6732 2 12524 1 7049 1 4155 1 7098 1 2352 1 16116 1 5412 1 15090 1 13903 1 5041 2 8989 1 7856 1 4750 1 6369 1 5934 1 4925 1 1364 2 3359 1 7413 2 12669 2 16419 1 6373 1 5936 1 8314 1 6498 2 15511 2 12333 1 171 1 7671 1 10325 1 13648 1 15933 1 14428 1 126 1 6749 1 15609 1 5245 1 11472 2 6801 1 800 4 1505 1 13209 1 4933 1 6180 1 2164 1 1185 2 12342 1 7992 1 62 1 2422 1 4272 1 623 1 14694 1 3375 2 7223 1 13214 1 15938 1 2377 2 2882 1 12033 1 11339 1 1982 1 10403 1 3022 3 3898 1 7178 1 13011 1 13660 2 5889 1 6815 1 14181 2 4172 1 15462 1 7012 1 16495 1 15191 2 4707 3 12416 1 8329 1 15753 1 7181 2 6071 2 16318 1 9421 1 5578 1 1656 1 11347 30 taxis 5864 1 5082 2 1014 4 7789 2 8652 2 11789 2 12368 2 14285 2 6656 1 robberies 5336 1 4256 1 12347 1 4131 2 1807 1 12669 2 13671 1 10620 1 13893 1 5718 1 5173 1 5679 1 7653 1 14155 2 6304 1 2496 1 11553 1 7169 1 2377 1 536 1 race_horse 11546 1 12315 1 1065 1 2603 1 argentite 10096 1 12402 1 5934 1 6105 1 12111 1 8076 1 10965 1 5742 1 9759 1 10277 1 11327 1 7032 1 14726 1 4464 1 freshen 12409 1 1331 1 401 1 1370 1 16491 1 12380 2 13909 1 9532 1 6203 1 5332 1 2563 1 11348 5 15110 1 8135 1 442 1 68 1 7239 1 10636 1 12461 1 14270 1 6655 1 2986 1 6894 3 quarry 7421 2 6073 1 14226 1 13724 1 3330 1 12284 1 14507 7 732 1 7218 1 5505 1 11350 16 7370 2 13821 1 15232 1 10561 1 3320 1 9414 1 2806 1 4338 1 14525 1 6675 1 13178 1 1014 1 6102 1 262 2 1088 2 13076 1 15365 2 14811 1 2613 1 8650 1 5271 2 1934 1 11828 1 7501 1 15933 1 9841 1 1431 1 13171 1 7765 3 13897 1 14956 1 fishbein 5808 1 8180 1 10867 1 10685 1 14070 1 10815 2 15472 1 15265 1 9920 1 subtleties 14687 1 11430 1 15812 1 15459 1 16471 1 5612 1 11349 1 13610 1 4487 2 171 1 5271 1 8799 1 12145 1 9867 1 1775 1 15922 1 forsythia pronounce 250 1 1688 1 9690 1 14052 2 3946 1 7971 1 12144 1 8596 2 4155 1 2352 1 4950 2 7505 1 2865 1 11351 29 12887 2 2598 1 2326 1 8083 1 9853 1 16474 1 12206 1 11081 1 2873 1 8793 2 5739 1 5586 1 5493 1 14981 1 2535 1 13260 2 1915 1 16050 1 3815 1 10128 1 12399 3 8091 1 2055 1 12795 2 15933 1 6244 2 8489 1 12568 1 12796 1 4993 2 5089 2 7706 2 2607 1 10517 2 10492 4 9340 2 4466 1 4692 2 1738 2 2337 1 7523 1 10333 1 8464 1 13267 2 945 1 9940 1 8072 1 8323 1 1099 5 2644 1 12578 1 15310 1 13042 1 3898 2 13810 1 15459 3 6004 1 11646 1 10525 2 6980 1 9212 1 4889 1 9945 1 10570 1 10755 1 4772 1 6043 2 9787 1 12416 1 14018 1 13124 2 1612 1 10110 1 13813 1 3238 2 3348 1 9493 1 saboteur 10325 1 7800 1 4975 1 4466 1 1844 1 14793 1 10077 1 15184 1 7067 1 12408 1 13674 1 on_paper 11428 1 7015 1 9733 1 15630 1 10828 1 16471 1 6337 3 6131 1 7658 1 11349 1 2352 1 9221 1 2301 1 519 1 15729 1 3676 2 4589 1 11352 15 13228 1 13583 1 6903 1 12268 1 2873 1 10833 2 5841 2 14460 1 13529 1 10321 1 9800 2 10089 1 1088 1 12497 1 2267 1 7413 1 5244 2 12995 1 10840 1 10875 1 8246 1 10033 1 4790 1 5745 1 4289 1 13722 1 7312 1 5088 1 5245 1 9094 1 6536 1 11022 1 4464 1 3452 1 14956 2 10492 1 4933 1 1246 1 529 1 6180 1 12866 1 15910 1 14555 1 7002 1 12534 4 3375 1 7614 1 3223 1 508 1 13273 1 12155 1 1099 1 5888 1 11312 1 11925 1 2041 1 918 1 211 1 2546 1 11746 2 11646 1 10012 1 11895 1 10572 1 5035 1 14969 1 12517 1 15497 1 12352 1 2516 1 1861 1 1140 1 13897 1 superfatted 10473 1 1513 2 13083 1 7438 1 roasted 4944 1 8338 1 10473 2 3539 1 3905 1 12622 2 14117 1 10152 1 16148 1 11619 1 15324 1 14196 1 3173 1 11353 32 2560 1 11081 1 14855 1 2332 1 10 1 14650 1 9291 2 15267 6 15905 2 10901 1 8021 1 3619 1 1818 6 7704 1 1596 2 5972 1 1736 1 6906 1 14467 1 11022 2 8581 3 4466 1 3112 1 9097 1 8094 1 3416 1 9243 1 6350 1 9635 1 2569 2 14725 1 6287 1 16281 1 11923 1 3929 1 7082 1 11924 1 15487 1 9724 1 6419 1 6230 1 2136 1 5179 3 12383 1 10372 1 14672 2 10651 1 5035 1 7904 4 4365 4 3699 1 6546 1 3269 1 15085 1 15499 1 16350 2 13708 2 1755 1 6985 1 11859 1 3133 1 15088 1 15089 1 1172 1 13853 1 9393 1 9595 1 1965 1 4986 1 12590 1 11187 1 9598 1 16045 1 10253 1 10321 2 14352 1 10387 2 1621 3 11256 1 4103 1 5298 2 9064 2 16358 3 4220 1 4994 4 15875 1 6801 1 11474 2 12027 1 505 2 6802 2 16487 1 11330 1 2312 1 9610 1 10461 4 2241 1 2940 1 14366 1 7868 2 10007 1 12682 3 10750 1 16258 1 13734 1 10405 1 67 1 11204 1 9081 1 142 1 12747 1 11686 1 4579 1 7814 2 11612 1 9555 5 pork 6458 6 11120 1 14532 1 13609 2 4714 1 4368 1 15523 1 7658 1 3836 2 6204 1 6987 1 15790 1 12785 1 13422 2 7273 1 5616 1 11354 26 6654 1 2836 2 12889 1 4024 1 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15916 1 8769 2 7390 1 16398 2 8292 1 8414 1 2858 1 15279 1 7649 2 12003 1 2214 1 13843 1 158 1 1053 2 1055 1 1054 1 5180 1 12387 1 13581 1 7399 1 12522 1 7154 1 8656 1 15170 1 6924 1 2598 1 8307 2 2355 1 4429 1 10507 1 1303 1 14482 1 10253 1 12270 1 12668 1 2083 1 11327 1 14236 1 1180 1 9063 1 5297 1 12795 1 15301 1 7169 2 5427 1 800 1 8438 1 4933 1 12284 1 3145 1 10265 1 12404 1 5815 1 684 1 450 1 11333 1 4110 2 13214 1 8322 1 4114 1 9210 1 3830 1 5439 1 10689 1 16206 1 9487 1 11894 2 10144 1 12161 1 12416 2 4707 1 15944 1 13608 1 8330 1 6071 1 8563 1 14636 1 13482 1 3832 1 11612 1 3472 1 4944 1 5443 1 4583 1 4713 1 3982 1 11349 1 704 1 16078 1 12424 1 11909 1 6962 1 3320 1 3480 1 14121 1 10544 1 1459 1 14126 1 10156 1 2266 1 1875 1 1593 2 7189 2 7191 1 15833 1 5846 1 8209 2 4247 4 10424 1 5087 1 1735 1 11631 2 2635 1 14270 1 12055 1 9509 2 11367 45 16455 1 9097 1 14398 1 92 1 10166 1 9776 1 7836 1 5599 2 1101 1 13240 1 9779 1 11108 1 6230 1 15967 4 11646 1 11377 1 12448 1 7718 2 2140 1 2918 1 9920 1 3061 2 9113 2 13128 1 12586 1 6856 1 14023 1 5737 1 13525 2 4616 1 735 1 7470 1 8728 1 16119 1 1907 1 3874 1 362 1 11943 1 4158 1 4266 2 11400 2 4617 1 369 1 9400 1 6623 1 12334 1 4989 1 10066 1 15877 5 15985 1 1004 1 4995 4 6247 2 6505 1 2680 1 13404 1 623 1 12740 1 8739 1 5249 1 5888 1 1011 1 4998 1 1014 1 3897 1 1013 1 16491 2 14440 1 1933 1 6890 1 10340 2 2691 1 1136 1 6760 1 16494 1 1519 1 629 1 7122 2 6389 1 14044 2 3234 1 6520 1 12620 1 brecciate 5505 1 navigate 10492 1 2971 1 11366 10 4030 1 8595 1 1707 1 1472 1 11227 1 1964 2 623 1 15729 1 6830 2 3277 2 12266 1 6109 2 6324 1 5689 3 6230 3 5437 1 2735 1 834 1 757 2 3158 2 1750 2 7260 2 14892 1 7796 2 14044 1 6130 1 1539 2 13264 3 11769 2 5273 1 15514 1 11601 1 matting 2953 1 447 1 4849 1 15753 1 7189 1 8430 1 calorifacient 7393 1 1596 1 4933 1 5262 1 11344 1 masochistic 12149 1 12410 1 7006 1 bladed 8438 3 5906 1 5184 1 4072 1 10628 3 11368 9 12231 1 3556 1 14469 1 7912 1 5136 1 14212 2 3609 1 5686 1 4143 1 13836 1 3354 2 9722 1 15952 1 6120 1 9018 1 3703 1 14911 1 15342 5 15028 1 16286 1 16287 1 8012 1 10321 1 13118 1 5213 1 9971 1 8180 1 7120 1 6389 1 9860 2 13768 2 postpose 945 2 10115 1 2229 2 11666 2 1014 2 3483 2 dower 14609 1 4466 1 9937 1 6176 1 2487 1 6697 1 hunting_knife 9860 1 7798 1 7010 1 orthilia 11292 1 3760 1 5347 1 14196 1 narcolepsy 9429 1 13177 1 1004 1 5878 1 15918 1 1788 1 5736 1 2115 1 11369 5 1963 1 16078 1 12932 1 8180 1 11870 1 8449 1 12795 1 9546 1 1703 1 5913 1 12512 2 11355 1 16210 1 6264 1 5832 2 simile 16278 1 15576 1 13676 1 7238 1 7291 1 9655 1 3874 1 7473 1 6064 1 15861 1 7114 1 8769 1 12207 1 7304 1 5268 1 10867 1 10385 1 1014 1 866 1 15698 1 15834 1 8180 1 15367 1 3816 1 11320 1 1735 1 1055 1 7733 1 14636 1 349 1 10289 1 unwieldy 7541 1 11911 1 1198 1 400 1 4728 2 12643 1 12644 2 10028 1 14352 2 6131 2 767 2 7585 2 530 2 13800 1 10064 1 4588 1 8728 2 5500 1 9264 1 3703 2 12353 2 11582 1 877 2 3238 2 6925 2 dysthymic_depression 9296 1 6002 1 779 1 15918 1 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10619 1 3992 1 82 1 4378 1 16190 1 11497 1 7022 1 15237 1 12859 1 3954 1 2306 1 5848 1 16484 1 11364 1 7383 1 12122 1 4138 1 9340 1 772 1 10202 1 3367 1 7218 1 12867 1 8255 1 12372 1 7387 2 13800 1 11333 1 10076 1 11372 7 9613 2 971 1 8904 1 8192 1 15594 1 11641 1 12776 1 15940 1 2404 1 13731 1 15886 1 483 1 2759 1 2511 1 1410 1 4169 1 5228 1 7041 1 13637 1 213 1 8942 1 4172 1 14702 1 4279 1 489 1 9009 1 7596 1 2986 1 6393 3 14604 1 libocedrus 12318 1 4933 1 9081 1 14974 1 3200 1 3416 1 172 1 14520 1 9133 1 2580 1 electric_field 9494 1 7398 1 12718 1 7756 1 8628 2 12145 1 7506 1 12664 1 5616 1 3280 1 15093 2 2705 1 9153 1 11188 1 2467 1 8753 1 4815 1 10421 1 14819 1 7191 1 3105 2 9533 1 6279 1 6499 1 13496 1 14163 1 14236 2 4789 1 10126 2 7983 1 15106 1 6747 1 5089 1 6876 2 12026 1 1737 1 10134 1 2499 1 11563 1 2720 1 1782 2 10816 2 4539 1 16225 1 11568 1 11373 14 5886 1 12156 1 13273 3 13008 1 11312 1 536 1 12943 1 5952 1 7715 1 13218 1 6664 1 7326 1 8108 1 15120 1 2857 1 4169 3 3744 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3 2405 1 11375 23 9582 1 2581 1 9248 2 16104 1 4739 1 5979 2 12008 1 10573 1 1609 1 4365 2 2986 2 14476 1 12521 1 7398 1 13900 1 4092 1 6987 1 11326 2 4093 1 12785 3 12786 1 3578 1 13853 1 3439 1 5347 1 4986 1 12590 1 3875 1 15976 1 2806 1 16045 2 5739 2 2663 2 9800 1 16047 2 5496 1 789 4 12994 1 13789 2 11662 1 8246 1 12531 1 9739 2 4102 1 1237 1 13720 1 5500 1 16358 1 4756 1 681 1 11733 1 5945 1 4933 2 1370 1 13728 1 558 1 9998 1 14555 2 13969 1 3651 1 13800 1 8072 1 14771 1 5253 2 11888 1 3463 1 13277 1 11746 1 14838 1 4507 1 7872 1 67 3 1136 1 12809 1 2692 1 11204 1 10755 1 7120 3 12416 1 16442 1 4579 1 6520 1 1991 1 5957 1 normative 4164 1 9472 1 14052 1 3679 1 11376 7 5534 2 13512 4 6525 1 8264 1 9566 1 5695 1 11838 2 6090 3 2865 1 13283 1 8090 1 12534 1 13182 1 3376 1 14018 2 3792 1 13285 1 irremovable 13034 2 12249 1 5419 2 16419 1 3608 1 5810 1 10284 2 12881 1 13674 1 5570 1 10279 2 6008 2 12206 2 4029 1 repressed 4659 1 9509 1 11367 1 8831 1 15430 1 3672 1 8747 1 927 1 14707 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1 14020 1 12323 1 16143 1 12620 1 11898 1 16348 1 pastor 2332 1 11730 1 15797 3 294 1 6230 1 11453 1 10255 1 5621 1 703 1 8897 2 11378 7 2648 1 1065 1 6562 1 15898 2 15933 1 12161 1 3726 1 11789 1 3766 1 1188 2 19th 3775 2 1660 1 15523 1 884 2 13551 2 2324 2 13552 1 12624 2 13821 2 14056 1 2556 1 11693 1 1798 1 15759 1 7238 3 11147 1 2701 2 3907 2 15898 3 3676 1 9285 3 5012 3 7239 1 6136 1 257 1 3099 1 10899 1 10356 1 15264 1 892 5 2332 1 3404 1 16004 1 766 1 8517 2 265 4 12896 1 5271 1 14584 2 639 1 12120 1 9826 1 16388 1 3790 1 13171 2 527 1 10903 1 15538 1 9830 3 10363 1 12123 1 13035 3 12645 1 8026 1 12904 1 8764 1 5173 1 6287 2 6035 1 11450 1 535 2 12244 1 10638 1 2574 1 536 1 12776 2 7774 2 11033 1 8040 50 6788 1 4910 3 1555 1 3126 1 6040 1 2214 1 9971 1 9838 1 5403 1 14738 5 4912 1 1169 1 14223 1 1560 1 8180 1 15162 1 14225 1 1291 1 13048 1 3432 1 414 2 1958 1 1055 1 1685 1 6671 1 13312 4 3945 1 5184 1 5671 1 7397 1 12522 1 5555 2 288 1 12524 1 10247 1 9186 1 11859 2 664 1 13319 1 12011 1 3438 1 3134 1 1836 1 1568 1 7660 1 2598 1 10119 3 4306 1 8545 1 1965 1 4429 1 7274 1 13323 1 5805 1 13583 2 13461 1 2869 1 16189 1 7788 1 11462 1 3136 1 6437 1 7789 1 14352 1 16191 1 13718 1 4928 1 1180 1 3816 1 6686 1 15447 1 13722 1 15301 3 6801 1 6564 1 12284 2 1436 1 3717 1 4441 1 7288 1 12937 1 9203 1 12534 1 9614 1 4110 3 15026 1 14626 1 13338 1 4829 2 1982 1 9482 1 13731 1 5953 1 13075 1 11064 4 9210 1 8684 1 813 1 4700 1 13079 2 14502 1 15312 1 4230 2 14105 1 2888 1 3972 8 15191 1 1328 1 11897 1 12953 1 4118 1 12163 1 7181 1 12037 1 15688 2 5442 1 3834 1 5580 1 10692 1 15192 1 7069 4 14257 1 4332 8 9884 1 3164 1 1992 2 2114 2 328 1 705 1 12167 2 14782 1 6830 1 12043 1 7574 2 9228 1 15472 1 9891 2 1459 1 8458 1 830 1 10157 1 3483 6 4957 2 5214 1 1595 1 2902 1 9506 2 10286 1 7195 2 3493 1 3036 1 6596 1 12568 1 2394 1 13505 1 1737 1 8214 1 8462 1 15334 1 15961 1 12181 2 13369 1 1096 1 3500 1 6476 1 15067 1 6602 1 2504 1 14140 1 3740 1 9103 1 9519 1 3341 1 7960 1 2404 1 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14448 1 5371 1 7368 1 9423 1 8693 1 15757 1 9496 1 8156 1 6339 1 706 1 9885 1 3983 2 15470 1 13086 1 15636 1 2117 1 5773 4 8883 1 16148 3 8751 1 14458 1 12822 1 2840 1 335 1 15473 1 10 1 9088 2 13293 1 1732 2 9232 1 14065 2 3485 1 5457 1 7447 1 469 1 5271 2 10840 2 4064 1 13683 1 11830 1 8828 1 641 1 5781 1 15701 1 13171 1 11631 1 1596 1 6283 2 10701 1 9094 1 14270 1 7765 1 3038 1 4250 1 8167 1 4659 1 905 1 7028 1 3793 1 10554 1 16459 1 10965 1 1738 1 13037 1 11838 1 8532 1 7711 1 9721 1 11776 1 9167 2 533 1 15217 1 3423 1 650 2 12775 1 8035 1 12185 1 8037 1 3501 2 7897 1 2404 1 5602 1 482 1 653 1 484 1 6480 1 6039 2 10970 1 10369 3 2214 2 1893 1 10442 1 3127 1 11380 1 14146 1 7904 3 5035 4 1357 1 14148 1 11386 3 6792 1 2986 1 5114 2 15500 3 15724 1 288 1 2019 3 4260 1 10310 1 112 1 16297 1 16471 1 4982 1 10453 1 10311 2 1490 1 8787 2 4263 2 12786 1 15973 1 1300 1 6864 1 11589 1 1964 1 5126 1 495 1 13193 1 7973 1 13583 1 3875 2 2466 1 2997 1 6494 2 5240 1 2873 2 7541 1 13645 1 5934 1 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10901 1 11282 2 3903 1 10018 2 14270 3 12620 2 6655 2 432 1 11081 1 safely 11966 1 9755 1 1170 1 3310 1 10824 1 573 1 10694 2 9650 1 9185 2 12692 1 8117 1 10277 1 5264 1 328 1 16078 1 1836 3 6080 1 8959 2 11820 1 11044 3 7239 1 6830 1 13746 1 4157 1 11081 1 5082 1 13784 1 337 1 9532 1 8517 1 4216 1 16356 1 6311 1 5271 1 1627 2 8246 1 3815 2 5709 1 4247 2 12795 1 10392 1 13171 1 15572 1 15769 5 12862 1 15907 3 11601 18 869 1 11366 1 10492 2 4623 1 3556 1 13626 1 2720 1 13398 1 93 1 4877 1 14691 1 15486 1 3558 1 10077 1 10076 1 779 1 623 1 12609 1 12775 1 14525 1 2730 2 11312 1 7743 1 13443 2 12578 1 7555 1 5689 1 2426 2 8292 1 11108 1 9550 1 2915 3 5437 1 12973 1 13118 1 16491 1 10304 1 10140 1 8223 1 15248 1 11646 1 4862 1 15546 1 8418 1 2214 1 13408 1 11784 1 1411 1 16494 1 13283 1 9587 1 6359 1 142 1 15579 2 14776 2 6010 1 5694 2 15083 1 1195 1 386 1 2826 1 magnetic_moment 15642 1 14816 1 11844 1 9667 1 8997 1 2568 1 9394 1 4882 1 6161 1 13814 1 air_defense 10868 1 12524 1 11636 1 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4309 2 9601 1 8997 1 9664 1 8491 8 233 1 3081 2 1507 6 685 1 13404 1 9937 3 13214 1 11605 30 3764 1 9075 3 10403 1 7178 1 10465 1 3088 1 3089 1 8942 5 14838 1 2691 1 14181 1 9081 2 7012 3 7563 1 3831 1 4511 4 15579 4 4633 1 12102 1 14504 1 9420 1 5578 1 2184 1 stressed 10113 2 13818 1 9186 1 6958 1 4335 1 4950 1 15634 1 15170 1 11693 2 4264 2 6168 2 1395 1 15399 1 5156 1 8512 1 13063 1 6367 1 10617 1 12268 1 1459 1 611 1 4529 1 10865 1 16123 8 4617 1 12466 1 10321 1 5494 3 10836 1 3483 1 16190 1 14322 1 11327 2 10359 1 8996 1 12399 1 12674 16 795 1 12569 1 11666 1 4993 3 6801 1 10070 1 7707 1 1075 1 5534 1 5718 1 7738 1 5505 1 6226 1 14243 1 62 1 1949 1 10076 1 4110 1 13214 1 6911 1 11606 24 4907 1 14598 1 8935 1 2945 1 15812 1 14010 4 3601 1 15459 4 11646 1 4359 1 3862 1 2950 1 16494 2 1291 2 1448 1 15002 11 1054 1 9554 1 6894 1 motmot 7100 1 3141 1 14717 1 11451 1 coral_necklace 5849 1 inappropriate 3833 1 4944 3 5443 1 15818 1 9819 3 16263 1 15430 4 14918 1 4179 1 545 1 13674 1 4489 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15579 1 7210 1 6830 1 elicit 13668 1 9819 1 15430 1 5119 1 12586 1 10758 1 704 1 8853 1 16472 1 16117 1 824 1 3354 1 15436 1 10899 1 2261 1 3282 1 8382 1 12890 1 1619 1 7413 2 14352 1 2001 1 12669 1 11403 1 6497 1 13428 1 6241 1 12856 1 16051 1 3788 1 12175 10 996 2 3816 1 14721 1 15 1 3646 1 200 1 10131 2 8864 1 9863 2 1211 1 4466 1 4933 1 448 1 529 1 13569 1 13797 1 4965 1 8361 1 11133 1 1710 1 11333 1 9613 1 7956 1 14626 1 5688 1 8391 1 5311 1 16033 1 695 1 4384 1 2885 1 1607 2 14838 1 11749 1 13765 1 14147 2 7012 1 14969 1 11686 1 11963 1 8946 2 12352 1 5317 1 11117 1 16144 1 boarding 1014 1 10322 2 4169 1 7148 1 14145 1 16356 1 12176 5 2306 1 8449 1 15701 1 6830 1 359 1 16044 1 6273 1 13461 1 5873 1 14525 1 16074 1 venerate 9509 1 11108 1 4623 1 6712 1 6794 1 85 1 920 1 4238 1 13552 1 2966 1 15844 1 10177 1 2937 1 4523 1 2865 1 2326 1 4705 1 13642 1 15637 1 1776 1 14941 1 13000 1 16169 1 9816 1 3582 1 6058 1 underhandedly 223 1 11399 1 14744 1 3724 1 37 1 9186 1 13795 1 703 1 4480 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1 271 1 8831 1 9635 1 16092 1 4352 1 3189 1 531 2 14663 1 8532 1 10295 1 10906 1 9775 1 11227 1 5029 1 9967 1 16169 1 12775 46 13374 1 3426 1 14525 1 11572 1 14408 1 2645 2 2011 1 6230 2 484 1 14600 1 14010 1 2136 1 12003 1 5727 1 11577 1 5403 1 8474 1 15423 1 6232 1 159 1 10376 2 13381 2 2797 1 851 1 7396 1 7596 8 2408 1 4865 1 9920 1 3349 1 14226 1 15353 2 7657 1 9735 1 7405 1 1490 1 10860 1 8119 1 6989 1 5347 1 13526 1 4813 7 11250 1 7665 1 5934 1 11593 1 14950 1 2027 1 14351 1 14160 1 15103 1 4928 1 5498 1 14548 1 12596 1 8384 1 8435 1 7799 1 11873 1 6178 1 6375 1 11601 1 7350 1 1369 1 9339 1 801 1 3077 2 12029 1 1370 1 11330 1 1707 3 15110 1 11479 1 60 2 14246 1 3296 1 2240 2 10077 2 12534 1 7425 1 12608 2 953 1 13919 1 7496 1 12537 1 10138 1 10750 1 4041 2 1014 2 2760 2 13076 1 10140 1 2546 1 3532 1 8560 1 8146 1 6634 1 4889 1 16495 1 142 2 8393 1 2953 1 1263 1 6263 1 4773 1 8946 1 3025 1 12620 1 386 1 arithmetic_progression 10133 1 9641 2 15444 2 11033 1 15864 1 6747 1 13076 2 536 1 12059 1 2490 1 opposed 13668 2 1024 1 252 1 11011 1 5374 1 5148 1 11817 1 13422 2 15759 1 9427 1 519 2 7374 1 3907 1 3676 1 6398 1 6903 1 14458 3 6655 1 1030 1 3102 1 5648 1 8630 1 766 1 265 3 13169 1 8161 1 12364 1 7135 1 13684 1 13432 1 15 1 13434 1 10770 1 13171 2 10491 1 9964 1 529 3 3923 1 11307 2 8637 2 12905 1 3926 2 8897 1 12907 1 1282 1 10775 1 13175 1 778 1 779 1 11309 1 15277 2 5176 2 536 1 7390 1 14992 1 8176 1 11843 1 13946 1 7649 3 15279 1 6667 3 14221 1 2581 1 16291 1 279 1 8180 2 15555 1 1055 4 2593 1 1834 1 3699 2 540 1 4675 1 14226 1 9186 1 8655 1 7156 2 12145 1 6168 1 2598 1 10506 1 5413 1 1570 1 14482 2 10797 1 293 1 44 1 10253 1 15293 2 4925 1 1306 1 14233 1 3139 1 8432 1 14234 1 4928 1 2604 1 4688 2 13722 1 15572 2 15301 1 11600 1 7169 2 7421 2 6180 3 10265 1 13968 1 6943 1 9999 1 9203 4 1317 1 8677 1 305 1 7929 1 2377 1 5688 1 12943 1 12410 1 3155 1 14773 1 14500 1 13973 1 5690 1 8942 1 15312 1 10271 1 13480 1 10823 1 1860 1 4706 1 12416 1 4707 2 3723 1 8946 1 15034 1 12686 1 6192 1 10018 2 3975 1 11612 3 16319 1 5442 1 5443 1 14507 1 574 1 1333 1 12692 1 9219 1 7184 1 3477 1 13745 1 4951 1 13490 2 2627 1 12960 1 10833 2 9762 1 5705 1 6712 1 830 1 3483 2 4957 1 11625 1 5214 1 15833 1 3729 2 2394 2 1596 1 4723 1 5713 1 15334 1 15065 1 7201 1 12439 1 12181 3 10559 1 2129 2 7712 1 5470 1 585 1 9518 1 11372 1 12971 1 6230 1 15853 1 8718 2 16104 1 12448 1 847 1 14527 1 11381 2 16344 1 9784 1 2407 1 7462 1 14018 1 4014 1 603 1 1901 1 1902 1 3201 1 7468 1 3202 1 16115 1 16117 2 15089 2 3513 1 1617 1 4490 1 6989 1 1909 1 15093 3 10867 1 5241 1 13531 1 10868 1 7729 3 866 1 13259 1 3209 1 2415 1 8486 1 6498 4 3210 1 1621 1 6499 1 14821 1 16356 1 10324 2 11406 1 10325 1 4493 2 2416 1 5745 1 8997 1 7486 1 4271 1 1635 1 3218 1 12735 2 8254 1 15878 1 15110 1 8739 1 3223 1 9546 3 4502 1 2682 1 5756 1 15118 1 15886 1 4041 1 10598 1 13011 1 1136 2 4771 1 14570 1 4511 1 629 1 11686 2 5766 1 145 1 11687 1 14047 1 5515 1 6011 1 387 1 14052 2 15630 1 6525 1 13551 2 13552 3 5519 1 13821 1 15634 1 4054 1 1146 1 4056 1 15132 1 9013 1 15636 1 12493 1 9015 1 1395 2 11147 1 8749 2 15637 1 4528 1 7239 1 14577 2 2195 1 4529 2 9021 1 10619 3 4531 1 9291 2 8517 2 6278 1 14857 1 5528 1 637 1 9826 1 9294 1 7518 1 7245 1 9830 1 906 5 15908 1 11163 1 10363 1 5534 1 6033 2 4537 2 8764 3 8529 1 4075 1 9036 1 6035 1 406 1 5027 1 10097 1 14864 2 15411 1 8769 3 11997 1 15155 1 12776 96 7256 3 16398 5 8292 4 7774 1 8040 3 5278 2 5279 1 16401 1 8534 1 9309 1 12003 1 6041 1 659 1 7260 1 160 1 924 1 5282 1 1685 1 16180 1 16182 3 3572 1 8049 1 8785 1 12011 1 10116 1 4093 1 9594 1 11459 1 15170 1 15436 2 2741 1 8309 1 4813 1 5805 1 7276 1 4307 1 1176 1 9598 1 8548 2 7789 1 13064 1 15441 1 4098 1 6311 1 3813 1 2747 1 11468 1 14617 1 15933 1 8550 3 12023 1 15447 1 10131 1 13863 1 6801 2 7286 1 5815 2 10933 1 8805 1 4823 2 5307 1 2474 1 9868 2 12534 1 4110 1 11480 1 9071 1 1187 1 3826 1 689 1 10685 1 14626 3 1715 1 14911 1 6323 1 13338 1 16436 1 692 1 15940 1 7810 1 13075 2 5311 1 14913 1 8559 1 13079 1 10144 1 15191 2 4118 1 3025 1 12037 1 14636 1 4120 1 15688 1 11491 1 7068 1 7069 4 3311 1 9882 1 14918 1 9357 2 12551 2 15467 1 9884 1 1992 1 704 1 12041 1 11212 1 15195 1 6080 1 14642 1 6081 3 10418 1 9891 1 11215 1 5841 1 5586 1 2263 1 15473 1 11495 1 14130 1 13099 1 3036 1 7309 1 3848 1 12568 1 14658 1 10428 2 10701 1 6349 1 201 1 4844 1 2277 1 4138 1 6835 3 8093 2 1737 1 11773 1 15961 1 8834 1 717 3 6351 1 7587 1 971 2 4857 1 8104 1 8102 1 9378 1 11512 2 213 2 11784 2 3863 1 6359 1 13380 1 12068 1 2797 1 13382 1 2016 1 7333 1 11517 1 982 1 219 1 7848 1 3636 1 9390 2 8596 1 7851 2 12848 1 14677 1 6108 3 16236 1 11252 1 2303 1 15235 1 227 1 16483 1 13395 1 2306 1 9663 1 16484 1 11803 1 10735 1 2815 1 16485 1 14956 2 5883 1 14959 1 3367 1 12341 1 9935 1 7863 1 8869 1 10461 1 5888 4 5361 1 3084 1 14697 1 11002 2 1014 2 16491 1 16492 2 14440 1 6890 1 10212 1 13408 1 4889 1 5890 2 16494 1 11282 1 12353 1 1267 1 12620 1 10218 1 6894 1 14704 1 4891 2 praetorian 11548 2 14192 2 12848 2 3396 2 6167 2 15933 2 11559 2 11916 2 10899 2 2978 2 14437 2 2986 2 east_anglia 10368 1 7749 1 5271 1 double_refraction 8180 1 hedgehog 11614 1 10867 1 12777 7 673 1 10029 1 6573 2 13531 1 10535 1 1457 1 5901 2 11636 1 10364 1 8595 2 14293 1 6759 1 3532 1 13836 1 11972 2 16411 1 1760 1 13608 1 464 1 1442 1 center_of_curvature 15945 1 14585 1 3676 1 3601 1 7788 1 7210 1 appease 3749 1 11517 1 16015 2 5208 1 2646 1 6566 1 7762 1 16116 1 16051 1 14088 1 6089 1 1154 1 13395 1 8203 1 2879 1 1715 1 12268 1 10395 1 3976 1 consultancy 4537 1 14060 1 6161 1 1787 1 3382 1 11133 1 stocks 6011 2 5896 1 4774 1 13609 1 14053 2 8153 1 9423 5 6204 1 1145 1 13820 1 6206 1 330 2 12041 1 9426 1 13745 1 9285 1 8750 1 7441 1 2438 1 10544 1 8458 1 2263 1 13681 1 2266 1 5648 1 4593 1 1035 1 11830 1 15058 1 1093 3 4250 1 3111 1 5785 1 529 1 13237 1 10430 1 3113 1 11920 1 4537 1 15910 1 12056 1 12181 1 9717 1 8529 1 585 1 13888 2 12129 1 14665 5 4196 1 9906 1 13839 1 1101 1 7390 1 14667 1 5543 1 10234 1 12778 31 2136 2 7148 1 4203 5 2214 1 6426 1 15077 5 101 1 2216 2 16405 1 10375 1 9728 1 14018 2 2797 1 5550 1 11181 1 284 1 9920 1 6165 1 3436 1 15972 1 10453 1 14533 1 39 1 8238 2 9116 1 10456 1 11395 2 9797 1 2147 1 6925 1 4925 2 11400 1 673 2 46 1 6111 1 6498 1 12397 1 9601 1 12992 1 9400 1 16051 1 14353 1 790 2 1180 1 3815 2 15933 1 10068 2 16192 3 2159 1 10200 1 9665 2 1004 1 8319 1 448 1 11735 2 1705 1 14691 1 11133 1 5686 1 8137 1 2608 1 14693 1 8072 1 508 1 11339 5 8391 1 1132 1 13011 1 7744 1 8501 1 11684 1 16072 2 11420 1 11686 1 15519 1 15520 1 3025 1 320 1 12620 2 purify 14798 1 13034 1 9608 1 5331 1 573 1 10825 1 12231 1 12142 1 6203 1 4537 1 12181 1 1475 1 10775 1 4263 1 12848 1 10906 1 778 1 10907 1 9413 1 3319 1 3223 2 5041 1 8100 1 15263 1 15953 1 10899 1 15696 1 5756 1 2882 1 7226 1 3766 1 536 1 6038 1 10421 1 10156 1 11002 1 5436 1 3666 1 12779 7 4700 1 2581 1 9644 1 11327 1 279 1 14236 1 16344 1 12764 1 7119 1 5139 1 629 1 2306 1 8449 1 8021 1 9179 1 398 1 6894 1 bris 2190 1 758 2 8980 1 13115 1 10661 1 10380 2 delicatessen 11397 1 12323 1 3133 1 4365 2 67 1 8750 1 supple 6011 1 8131 1 13299 1 1745 1 11485 1 16191 1 1064 1 1957 2 7636 1 12978 1 9228 1 11612 1 8769 1 1588 1 to_the_full 4659 1 7367 1 8673 1 7766 1 2337 1 2300 1 5569 1 8361 3 14693 1 16234 2 10681 1 3118 1 6830 1 4907 1 5888 1 5190 1 10544 1 8193 1 2873 3 11943 1 16436 2 3086 2 2945 1 12410 1 9705 1 12383 1 1053 1 11529 1 11698 1 10395 1 10218 1 4138 1 13581 1 evaporative_cooler 12345 1 6389 1 chili_sauce 14859 1 9162 2 9769 1 16017 1 15618 1 13995 1 3112 1 15864 1 5262 1 13999 1 10742 1 10924 3 67 1 3983 1 11280 1 12780 5 15434 1 14018 1 4579 1 740 3 5972 1 7340 1 2873 1 6452 1 foothold 1978 1 6592 1 2128 1 2613 1 972 1 481 1 morphophonemics 13648 1 pavlovian 4660 1 2281 1 genus_armeria 12786 1 6977 1 4143 1 14196 1 7697 1 8328 1 1893 1 4698 1 workbasket 12554 1 11611 1 14956 1 moscow 11517 2 9527 1 10824 1 5038 1 10825 2 11011 1 884 1 1941 1 13821 2 15089 1 3134 1 1228 2 7605 1 11619 1 5012 1 12302 1 9764 1 5561 1 2563 1 10874 1 2781 1 1813 1 15406 1 1844 1 267 1 13467 1 9861 1 1368 1 6801 1 7197 1 1406 1 10492 1 6059 1 3686 1 7110 1 3113 2 5432 1 14555 1 5948 1 9203 1 6035 2 6509 1 3596 1 11743 1 12970 1 3766 1 9748 2 536 1 10851 1 11108 2 1014 2 6230 1 8472 1 5228 2 13079 1 3972 1 14969 1 15225 1 12781 13 15688 1 2488 1 oak_apple 6389 1 11181 1 172 2 buckwheat 7015 1 15618 1 9642 1 14265 1 13422 1 6035 1 13720 1 3439 1 3354 1 9064 1 16358 1 6535 1 1596 1 12782 6 1019 1 3781 1 5222 2 5361 1 11022 2 3403 1 15770 1 13712 1 dactylic 2961 1 16151 1 2696 1 7703 1 6911 1 9546 1 5010 1 6504 1 938 1 15462 1 exigency 1053 1 13702 1 1674 1 10081 1 menuhin 4529 1 10820 1 4762 1 3872 1 5988 1 12115 1 989 1 5240 1 chatter 1204 2 10071 1 13927 1 15279 1 13865 1 9983 1 16165 1 4808 1 2054 1 3676 2 6747 2 9044 1 14 1 3988 2 9554 1 soapfish 2089 1 black_nightshade 6442 1 14712 1 10704 1 14159 1 13583 1 11620 1 8581 1 canonized 9305 1 5177 1 5345 1 7729 1 12048 1 11708 1 6150 1 8892 1 12383 1 62 1 14427 1 9116 1 12478 2 12927 1 3858 1 family_potamogetonaceae 1845 1 3555 1 2436 1 9644 1 1659 1 8866 1 10746 1 11228 1 coppery 12783 5 7657 1 16459 1 3524 1 10310 2 12075 1 12109 1 12326 1 15973 1 6449 1 1200 1 2436 1 14196 2 5054 1 6965 1 4954 1 9601 1 13281 1 14311 1 3760 1 9587 1 8021 1 2089 1 2797 1 7332 1 12519 1 4565 1 1403 1 11474 2 tray 12521 1 10244 1 5116 1 14532 1 12784 15 9819 2 12041 1 13422 1 6080 1 6238 1 8571 1 10833 1 5739 1 7541 1 12822 1 14514 1 789 2 15101 1 8246 2 11827 1 7730 1 12795 2 173 1 447 1 1596 3 9467 1 7314 1 7169 1 9863 1 5502 1 8167 1 13034 1 400 1 3556 1 8897 1 4350 1 16092 3 5173 2 5307 1 7991 1 2680 1 3082 1 1547 1 14004 1 3222 1 6352 1 972 1 7114 1 12578 1 10750 1 1014 2 15119 1 7230 1 16101 1 5311 1 13700 1 7009 1 14838 1 8618 1 11896 1 6518 1 2953 1 13343 1 5003 1 10018 1 12876 1 10050 1 2986 1 853 1 short_aria winter_sweet 1709 1 13086 1 14196 1 11964 1 black_bread 1560 1 7015 1 768 1 8458 1 3436 1 12008 1 8886 1 4756 1 36 1 5783 1 15433 1 10142 1 7350 1 stoned 4169 1 4401 1 4626 1 1750 1 15729 1 2214 1 14507 1 13684 1 9833 1 15501 1 flying_phalanger 12075 1 jakob_boehme 920 1 5884 1 13160 1 14236 1 family_geometridae 7541 1 3136 1 13609 1 16056 1 626 1 3773 1 12044 1 16470 1 mullidae 1457 1 newborn_baby 15719 1 13668 1 4332 1 15235 1 12102 1 14746 1 1667 1 13150 1 2898 1 11691 1 9546 1 7882 1 9599 1 genus_calandrinia jovial 288 1 11536 1 13399 1 13797 1 14555 1 733 3 7587 2 15487 1 7239 2 782 1 13858 1 9926 1 7230 1 4169 1 7392 1 14082 2 13027 2 3229 1 9400 1 15077 1 595 1 11749 1 8265 1 1055 1 lamplit 9087 1 2613 1 15633 1 14373 1 departure 5894 1 15993 1 573 1 14114 1 5581 1 324 1 13743 1 12041 1 13674 1 2326 1 13490 1 14057 1 6830 1 7629 1 13618 1 3546 1 6713 1 9021 1 15765 1 14461 1 11495 1 14323 1 15056 1 152 2 14585 2 2443 1 6220 1 4659 1 1737 1 7200 1 13689 1 10709 1 9377 1 535 1 11713 5 12971 1 2646 1 8039 1 1478 1 278 1 12383 2 1169 1 3747 1 8180 1 6043 1 15720 2 163 1 7776 1 6853 1 14285 1 9920 1 15229 1 7048 1 219 1 12845 1 12011 1 2526 1 15170 1 11728 1 15729 1 12787 24 6735 1 8055 1 5873 1 14612 1 9659 1 9989 1 7607 1 16356 1 14236 1 3815 1 15570 1 14954 1 373 1 15933 1 12996 1 937 1 15447 1 3007 1 13264 1 4495 1 2036 2 748 1 3452 1 4164 1 1004 1 3145 1 10812 1 4760 1 9614 1 8072 1 9349 2 11742 1 13214 1 10335 1 5688 1 11481 1 10522 1 7226 1 10337 1 13276 1 2546 1 15386 1 2547 1 6890 1 3158 1 11749 1 6389 1 11547 2 15034 1 12953 1 10147 1 1656 1 15946 1 15688 1 14506 1 trophy 7413 1 3113 1 14198 1 16008 1 8582 1 16356 1 8532 1 7636 1 2765 1 15896 1 11278 1 4143 1 11413 1 1772 1 8778 1 13461 1 3703 1 13264 1 8829 1 12228 1 4464 1 13002 1 9439 1 pus 5442 1 4944 1 5733 1 7754 1 3027 1 7368 1 8541 5 12981 1 15467 1 10310 2 10247 1 8378 1 15636 1 6828 2 4951 3 8309 1 12788 30 11213 1 11187 1 12889 1 14458 1 15401 1 5240 1 2873 2 16416 1 11189 1 4926 2 1364 3 3992 1 1698 3 15736 1 1812 1 9707 1 13429 2 7636 1 229 3 4064 1 13752 1 12308 1 10326 1 1242 1 4933 2 12029 1 3793 1 9097 1 3145 1 2909 3 12472 1 5720 1 1925 1 11308 1 12059 1 4031 1 8467 1 2284 1 7836 2 3223 1 16026 2 10300 1 14911 1 6419 1 14992 1 4319 1 4476 1 13475 1 5436 1 6293 1 12192 1 1133 1 10305 1 4257 1 9444 1 7360 1 9912 1 2764 1 10975 1 8180 1 4083 1 11238 1 3386 2 1357 1 5766 2 5694 2 3346 1 10018 1 10654 1 6580 1 13223 2 3571 2 2986 3 11854 1 distinctiveness 8180 1 11147 1 4990 1 14201 1 5154 1 15329 1 15847 1 3975 1 16085 1 goosbumps 6525 1 7387 1 16195 1 2214 1 227 1 genus_fothergilla 13900 1 14173 1 15757 1 16329 1 1893 1 3258 1 13086 1 12786 1 5017 1 15826 1 14712 1 6965 1 9216 1 ametropia 897 1 unilaterally 15330 1 15933 1 contract_in 4141 1 2300 1 8529 1 2420 2 15089 1 4370 1 6473 1 6249 1 3082 1 8749 1 13490 1 2624 1 13214 1 13575 1 13903 1 7239 1 1510 1 15667 1 11339 1 12789 6 9629 2 14667 1 6713 1 11033 1 11453 1 484 1 85 1 9764 1 10106 1 16355 1 11784 1 15330 2 2030 2 11405 1 14225 1 5860 1 11112 1 4437 1 2694 1 13030 1 14892 1 16484 3 11006 1 4118 3 9860 1 12177 1 15301 1 7733 1 11503 1 6703 1 conversion 15254 1 7504 1 13668 1 12299 1 10828 1 9219 1 884 1 6267 1 12555 3 13674 1 4052 1 13676 1 9427 1 12626 1 5641 1 8083 1 3907 1 7574 1 13228 1 16000 1 1086 1 10356 1 9429 1 8204 1 10421 1 8458 1 10420 1 10485 1 3679 1 10956 1 3684 1 1667 1 7518 1 5390 1 3327 2 13434 1 14068 1 2056 1 4464 1 13938 1 5329 1 10492 2 9576 1 5718 1 6033 1 11989 4 12059 1 778 1 779 1 15846 1 14864 1 2504 1 4907 1 14941 1 11232 1 8841 1 11999 1 10782 5 15714 1 4801 2 5228 2 3744 1 2581 1 9912 1 12448 2 7042 1 8180 2 12978 1 9107 1 924 1 8590 2 10377 1 8723 1 9253 1 7262 1 7968 2 13312 1 7969 1 6164 1 14226 1 1903 1 16041 1 10453 1 10116 1 6552 1 13251 1 10914 1 12145 2 6168 1 2865 1 1229 3 6989 1 667 1 15729 1 8309 1 13192 1 11396 1 120 1 6367 1 15795 1 10989 3 121 1 12790 36 5240 1 6678 1 12791 1 13713 1 13064 1 16047 1 4755 1 15671 1 15441 1 11327 1 5047 1 501 1 2365 2 5680 1 298 1 11409 1 8997 1 5132 1 15875 1 3762 1 13965 1 300 1 11475 1 1004 2 9341 1 5357 2 10333 1 11197 1 8869 1 13402 3 9203 1 14243 1 15880 1 8443 1 5434 1 3223 1 1510 1 13599 1 9004 1 11887 2 811 1 2478 1 4039 1 3656 2 12410 1 4041 1 11002 1 13973 1 8939 2 13150 1 3830 1 4700 1 12809 1 141 1 16494 1 10342 1 7012 1 14969 3 11282 1 13665 2 14188 3 6192 1 12353 2 16143 1 14636 2 8563 1 12228 2 6894 1 11286 2 5893 1 15688 1 shrimpfish 11670 1 3498 1 9932 1 13895 1 4187 1 2089 2 9635 1 7244 1 6535 1 8529 1 13718 1 13935 1 cultivated_land 15820 1 1487 1 13967 1 13708 1 5718 1 1613 1 4093 1 12786 1 1799 1 2062 1 12626 1 2436 1 3354 3 3907 1 5487 2 1510 1 14525 1 293 1 6369 1 12790 1 8702 1 726 1 12791 8 8886 1 4041 1 10956 1 15267 1 85 1 11327 1 3507 1 5422 1 1560 1 16314 1 13309 1 3289 1 10942 1 5680 1 15933 1 11017 2 14504 1 447 1 12518 1 13434 2 7731 1 8997 1 471 1 16231 1 14830 1 14972 1 9920 1 2111 1 intermix 6780 1 6480 1 3510 1 5968 1 16021 1 14493 1 227 1 167 1 3760 1 2598 1 16196 1 8021 1 4762 1 14551 1 3875 1 14047 1 5756 1 9421 2 14911 1 293 1 oneness 11002 1 14161 1 15469 1 3982 1 5073 1 13160 1 16035 1 10033 1 2974 1 2737 1 1602 1 1395 2 10144 1 12506 1 990 1 chub 15982 2 2306 1 2089 2 1596 1 3633 1 13935 1 krutch 12837 1 15885 1 529 1 13754 1 11940 1 alveolar_consonant 13482 1 overleaf 8998 1 10131 1 14864 1 john_dryden 7835 1 5694 1 391 1 5600 1 15088 1 16303 1 gold_foil 7120 1 indebted 8167 1 16455 1 13954 1 14622 1 3113 1 3185 1 4616 1 5026 1 7302 1 2865 1 8363 1 15846 1 7740 1 11742 1 10171 1 4502 1 8645 1 1604 1 7114 1 9629 1 741 1 6530 1 9021 1 6102 1 14601 1 9704 1 14322 1 11086 1 16191 1 4562 1 12083 1 12793 5 5500 1 9728 1 2274 1 15933 4 12953 1 15877 1 6792 1 2016 1 in_restraint 7421 1 4669 1 12643 1 5534 1 6498 1 3567 1 3862 1 659 1 3675 1 595 1 4705 1 7462 1 10602 1 5087 1 510 1 10697 1 assessment 8047 1 620 1 10608 1 13552 1 10137 1 62 1 11693 1 12721 1 623 1 10097 2 13253 1 5599 2 6064 1 13576 1 2601 5 8700 1 7441 1 2508 1 2838 1 6530 1 7716 1 1460 2 2534 1 14440 1 16329 1 7899 2 14322 1 1309 1 15422 1 1090 1 1136 1 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5228 1 14702 1 4279 1 489 1 7653 1 12619 1 9918 1 12353 1 925 1 7596 1 2016 1 1022 1 14604 1 integer 15228 1 420 1 5261 1 5283 3 1565 2 9387 1 543 4 9733 2 13772 1 5120 1 15430 1 6266 1 6732 1 15230 2 1940 1 1490 1 12848 1 15636 1 4183 1 2624 1 10664 2 10543 1 11396 1 9394 1 3401 4 2705 2 3280 1 5079 1 6494 1 2227 1 9021 1 4956 9 16328 4 16417 1 263 14 4531 1 2535 1 12669 1 10324 1 8246 2 16052 1 152 1 12053 1 15444 4 15105 1 12638 2 3684 1 6747 1 10770 1 14658 1 5089 2 7733 2 9337 3 10330 1 1635 2 10492 1 11099 1 15241 4 2720 1 5022 1 10741 4 12342 2 12868 73 10333 1 4822 2 1600 1 8930 2 4272 1 15712 4 4907 2 6975 1 2377 3 7006 1 13598 2 8769 1 11716 2 12410 4 1014 1 14009 1 14010 3 879 1 5279 7 11484 2 1324 1 13736 2 5033 1 10406 1 16494 1 6328 1 11896 3 9251 8 12543 1 10410 3 163 1 319 4 8005 1 2984 1 1389 1 12228 1 6792 3 7934 1 13312 2 reconstructive 9930 1 14802 1 16391 1 5278 2 14 1 14429 1 1919 1 13711 1 11528 1 bastnasite 10965 1 8582 1 5742 3 6498 3 11327 2 3997 1 4263 1 5868 3 16352 2 8666 1 5764 1 5711 1 3223 1 7922 1 7674 1 4185 1 9438 3 theodore_roosevelt 1737 1 7069 1 10750 1 11512 1 13079 1 12181 1 9793 1 11175 1 4172 1 5927 1 1517 1 62 1 14455 1 3881 1 11147 1 8092 1 648 1 15920 1 3277 1 4534 1 5168 1 11939 1 5508 1 7067 1 great_salt_lake 7421 1 15532 1 11002 1 5228 1 884 1 13821 1 16094 2 14584 1 10477 1 10392 1 13434 1 11123 1 13312 1 15688 2 get_down 3284 1 6338 1 2130 1 9259 1 16494 1 1642 1 10215 1 15191 1 11017 1 13412 1 7464 1 13063 1 8084 1 16144 1 alba 13494 1 8829 1 10462 1 5750 1 16388 1 captain_bligh 13252 1 1709 1 5396 1 1560 1 8458 1 7904 1 5347 1 211 1 9635 1 9328 1 sprinkle 9714 1 12508 1 3350 1 10136 1 9219 1 5772 1 8628 1 6506 1 12167 1 2412 1 13370 1 6080 1 7739 1 6866 1 6509 1 12869 6 3703 1 5542 1 4211 1 8292 1 15267 1 5032 1 10389 1 15672 1 13720 1 3467 1 15933 1 2953 1 9490 1 3520 1 14167 1 4515 1 16486 1 voluntarily 7969 1 15350 1 8251 1 6179 1 16349 1 5785 1 5331 1 12732 1 6318 1 4262 1 4808 1 8870 1 15962 1 6167 1 9116 1 3277 1 6170 1 8055 1 12870 11 6053 1 12943 1 7760 1 4212 1 10156 1 9306 1 6230 2 9006 1 3992 1 2734 1 14716 1 11945 1 3087 1 13150 1 3830 1 7189 1 16174 1 6771 1 2214 1 13408 1 13341 1 11662 1 12383 1 7133 1 11204 1 10325 1 2765 1 14237 1 141 1 10034 1 14823 1 14969 1 15933 1 5087 1 126 1 1916 1 11631 1 1861 1 6147 2 2036 1 16210 1 14956 1 selling_price 15572 2 9716 1 61 1 304 1 6606 2 12300 2 11405 1 alias 14012 1 10237 1 9021 1 1014 1 4737 2 3189 1 destabilize 10325 1 9203 2 641 1 11913 1 4847 2 12620 1 99 2 11714 1 range_of_mountains 4031 1 14585 1 2680 1 12123 1 2164 1 11331 1 15518 1 6281 2 15021 1 7850 1 15573 1 13063 1 2214 1 3677 1 8311 1 2490 1 4829 1 open_fireplace 10755 1 5052 1 9031 1 9667 1 6244 1 grandson 7398 1 8302 1 4155 1 7850 1 12845 1 3803 1 13552 1 8655 1 9259 1 16268 1 3986 1 10119 1 8663 1 7789 1 151 1 3483 1 8631 1 6715 1 5847 1 15139 1 9664 1 11917 1 10701 1 14094 1 15574 2 1737 1 13035 2 1370 1 2207 1 10560 1 13369 1 9904 1 13801 1 1641 1 8175 1 5820 1 11229 1 1982 1 16398 1 12377 1 12871 9 2856 1 4861 2 11512 2 813 1 16174 1 14838 1 2214 1 11784 1 16494 1 11282 1 6389 1 2921 1 15688 1 ch3br 9219 1 10421 1 12122 1 6487 1 13967 1 lira 10325 1 585 1 11229 1 7851 1 14181 1 1221 1 worsley 6843 1 11002 1 4202 1 16063 1 5536 1 14122 1 hudson_bay 7015 1 11652 1 505 1 12284 1 4427 2 4004 2 3164 1 9186 2 16321 1 10539 3 859 1 3438 1 6097 1 11147 1 5347 1 9285 1 10335 1 4781 1 13089 1 10635 1 1231 1 8512 1 293 1 4829 1 13340 1 14650 1 3483 1 12872 7 2563 1 6930 1 8246 1 1558 1 757 1 14281 1 159 1 6749 1 1402 1 1685 1 7349 1 13312 2 spoonful 15769 1 2418 1 12708 1 pacific_coast 7015 1 884 2 5264 1 14056 1 1942 2 2436 1 11147 3 14712 1 9285 1 14196 7 8512 3 2560 1 4529 2 763 1 2330 1 8012 1 15404 2 9291 3 11980 1 9704 1 16157 1 2563 1 14065 3 5387 2 13935 2 8021 5 5529 1 1667 1 1818 1 6535 1 10094 2 2971 2 7142 1 5173 1 3258 8 6289 1 6035 1 3421 1 6785 1 14216 3 6419 3 5179 5 15547 1 7528 1 1560 2 13048 2 2587 1 7904 4 8044 1 8537 1 417 1 1685 1 5798 1 3699 1 6430 1 5671 1 3436 1 5555 2 13314 1 11184 1 9186 1 1567 1 11859 1 14607 1 3134 1 2598 1 13583 1 4920 1 14091 1 293 3 2873 3 10253 1 5295 3 3444 4 14352 1 14092 1 15672 1 1428 1 7415 1 5679 1 8435 1 447 1 16055 2 2089 7 10515 1 15573 3 179 1 9996 1 7547 1 14623 2 13967 1 2372 1 13728 2 11602 1 11196 1 10400 1 13968 1 8675 1 12939 1 2881 1 4829 1 15684 1 9209 1 311 2 2613 1 16314 12 16070 1 13343 1 14107 1 9216 1 11614 1 7568 1 1865 1 3164 2 4335 1 11349 1 705 1 8698 1 5837 1 329 1 16078 2 13086 1 15827 1 15323 1 3320 1 15046 1 8573 2 12822 3 5705 1 8702 2 8458 1 13356 1 2263 1 5455 1 8343 1 3992 2 3483 1 10158 1 1596 2 16453 1 11917 1 10701 1 2277 1 8581 4 9372 1 16459 2 15840 4 10704 2 9635 1 4350 1 3189 1 14137 1 5469 1 12705 1 11777 1 5722 1 7321 1 9103 3 211 1 5228 1 7457 1 13893 1 16465 1 12064 1 14526 1 1104 2 6727 1 1356 1 8983 1 7846 1 2797 1 15082 1 8230 1 6487 2 15971 1 6489 1 9255 1 1487 2 3750 1 10983 1 14149 1 13774 1 13900 1 9922 1 1755 1 3869 1 10310 2 11389 1 12326 2 12847 1 4263 6 15973 1 14027 1 5126 3 5347 1 7853 1 11397 1 14539 1 2806 1 2303 1 5128 1 1913 2 14543 1 10321 2 225 1 6498 1 16124 1 7103 4 12084 1 3760 4 10326 1 4994 1 999 1 8128 1 15611 1 7350 24 9406 1 3524 1 3219 1 2312 1 10205 1 10335 1 6754 1 8259 2 5508 1 3084 1 10747 1 15249 1 7360 2 12873 72 6128 1 14311 2 757 1 9945 1 14312 1 6389 1 6638 2 9148 1 12352 1 16373 2 7014 1 teddybear 735 1 758 1 labial 15907 1 13718 1 3348 1 fatness 14077 1 14979 1 12477 1 3992 1 85 1 15467 1 11831 1 7585 1 15089 1 bister 9206 1 genus_rynchops 1261 1 inge 9405 1 5671 1 1565 1 3636 1 1488 1 13777 1 8361 1 3064 1 1836 1 9203 1 8069 1 14609 1 10476 1 10007 3 8141 1 2466 1 2149 3 2426 1 2945 1 14352 1 9704 1 14838 1 2613 1 13606 1 16051 1 10372 4 10325 1 8209 1 12226 1 12279 1 15572 1 15816 1 play_down 15677 1 12085 1 10965 1 16117 1 11653 1 8579 1 old_world_crayfish 1457 1 postponement 15049 1 9324 1 945 1 3696 1 7293 1 11109 1 14814 1 4616 1 13338 1 12383 1 cricket 6898 1 15467 1 252 1 3 1 4950 1 12110 2 5267 1 7238 1 2190 1 11147 1 8749 3 13490 1 12629 1 465 1 2330 2 5900 1 5778 1 14462 1 13496 1 5458 1 3617 1 14794 1 8021 2 15701 1 11698 1 16 2 14270 1 12643 1 5331 1 3734 1 2910 3 8582 1 10556 1 1600 1 2062 1 2063 1 10299 1 14405 1 9908 1 7320 1 9172 1 6419 1 14994 1 3342 2 2915 2 10642 1 6230 2 8108 1 12973 2 14010 1 2858 1 11646 2 14014 1 4803 3 10570 1 12583 1 12260 2 8299 1 10983 1 9922 1 5186 1 2802 6 1300 2 11728 1 4678 1 10506 1 13583 1 4920 1 1968 1 7856 1 12666 1 2873 1 7410 2 4925 2 5493 1 1307 1 7789 1 14160 1 8246 1 11406 1 4102 1 12594 1 996 1 8385 1 15445 1 10131 2 6801 2 1635 1 4933 1 8439 2 16304 1 5996 4 11602 1 3371 1 14690 1 8254 1 11878 1 11133 1 8675 1 9612 1 4762 4 8322 1 14626 1 6123 5 7116 1 15940 4 5758 1 15118 1 5311 1 8875 1 5362 1 7744 1 13736 1 7010 1 13082 1 16494 1 15032 1 9685 1 3831 1 8393 3 12874 37 11282 4 14776 2 12687 1 7751 1 12353 1 14047 1 14636 1 7014 1 5957 1 11287 1 venture 4391 2 7367 1 15318 1 9423 1 6203 1 12693 2 1145 9 12815 1 13674 1 11906 1 13352 2 7302 1 14319 1 11618 1 11147 1 6081 1 13745 2 13746 1 6707 1 15829 1 6655 2 2328 1 10544 3 9629 1 5840 1 2490 2 16217 1 10483 1 15136 1 12962 1 3788 1 5388 2 6774 1 6773 1 338 2 4247 2 16387 1 15 1 8408 1 7707 1 10492 1 7451 1 2906 1 1211 1 4660 1 11703 1 10363 1 18 2 12645 1 2337 1 5173 1 8363 1 13575 1 4907 1 8645 4 3932 2 11106 1 7390 8 5543 1 6976 1 15419 1 484 1 12249 1 4203 1 2581 1 6294 1 15077 1 6359 2 14529 1 1750 1 1055 1 15784 1 107 2 6791 1 2016 1 11858 1 4426 1 13900 1 3202 1 10115 1 4156 2 4616 1 14417 1 12786 1 4678 1 10727 1 13711 1 12464 2 14028 1 7161 1 4680 1 6994 2 7413 1 12397 1 8314 2 12995 1 11662 1 14547 1 4928 1 9739 1 1180 2 125 1 9334 2 7796 1 13722 1 10879 1 1368 1 14094 1 1635 1 12471 1 6116 1 9405 1 4933 1 3145 3 10333 1 10593 1 5307 1 8137 1 62 1 622 1 4380 1 5361 1 7496 1 3659 3 8558 2 1014 1 11002 2 4169 1 5000 1 10211 1 813 1 16313 1 10012 2 14838 2 2691 2 13735 1 15312 1 2765 2 14775 1 6010 2 12416 1 12875 32 14044 1 6071 1 10414 1 15756 1 electric_catfish problematic 12034 1 5922 1 14041 1 10809 1 7649 2 4099 1 15832 1 7154 1 14838 1 16051 1 15997 1 1053 1 7593 1 15681 1 15976 1 12268 1 14636 1 2873 1 gift_horse 7099 1 12457 1 4495 1 13718 1 1595 1 2546 1 6058 1 13367 1 flattering 8167 1 10492 1 12784 1 14412 1 10333 1 8134 1 12041 1 2865 1 7993 1 3082 1 7211 1 13490 1 13146 1 15953 1 1099 1 9270 1 7667 1 4860 1 763 1 8992 1 10340 1 4101 1 1878 1 14236 1 6145 1 443 1 7730 2 3790 1 1209 1 12876 7 in_a_nutshell 1053 3 9955 3 6520 3 8055 3 529 3 5403 3 5967 3 1490 3 bivariate 10698 1 15444 1 copulative_conjunction 945 2 2928 1 1395 1 3792 1 5166 1 inspection 6011 1 4774 1 6958 1 4335 1 13552 1 14851 1 12041 1 15399 1 8749 1 13745 1 9889 1 7509 1 11298 1 4130 1 8344 1 5165 1 7022 1 12898 1 14584 1 8348 1 14585 4 5458 1 3617 1 12638 1 13684 1 2199 1 5590 2 12368 1 10290 1 4466 1 10628 1 14328 1 15910 1 4192 2 532 2 7387 1 16223 1 1476 1 15339 1 3859 1 7455 1 10640 1 1165 1 15073 1 11927 1 13698 1 10304 2 11034 1 14602 1 7899 1 7148 1 16104 1 12916 1 4912 1 8180 1 10570 1 4204 1 16405 1 8181 1 13181 1 2220 1 11939 1 8851 1 284 1 2349 1 15084 2 13581 1 8726 2 4017 1 16182 1 2862 4 4155 1 7210 1 15089 1 10056 1 13190 1 9394 1 5988 2 1968 2 15797 3 294 1 2081 1 5621 1 1915 1 3815 1 11406 1 12020 1 8800 1 1574 1 15609 1 9664 2 13863 1 7170 1 1183 1 11330 1 14035 1 3820 1 12734 1 7173 1 14555 1 8137 1 7992 1 62 1 508 1 1378 2 3766 3 6633 2 15246 1 1014 2 5062 1 7361 1 4279 1 5002 1 16495 1 4118 1 6192 1 1656 1 12877 22 by_fits_and_starts 1656 1 13221 1 15933 1 fuel_line 14071 1 14974 1 5032 1 556 1 7346 1 8628 1 semantic_role 12585 1 4256 1 9021 1 7428 1 2646 1 1014 1 7163 3 3113 1 8907 2 3759 4 3776 7 5854 2 15790 1 10822 1 1091 1 7060 1 9116 1 10424 1 3223 1 13467 1 16315 7 2755 2 13273 1 11553 3 9978 5 12620 3 2159 2 2490 1 12878 8 syringe 15310 1 12879 6 6557 1 5016 1 14485 1 5902 2 9219 1 7523 1 2581 1 10389 2 10023 1 2912 1 7505 1 10128 1 14892 1 6352 1 14167 1 6161 1 4147 1 phase_i 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8512 2 4185 1 2560 3 15763 1 16003 1 8279 1 8012 4 9021 1 5778 2 12495 1 3678 1 15404 1 16004 2 12501 1 4062 1 8021 1 6146 2 13434 1 1818 5 8408 1 7384 1 2970 1 16164 1 3253 1 10628 1 12123 3 12644 1 8171 1 3687 1 6287 1 11449 1 12126 1 3931 1 5029 1 16028 1 11171 1 14216 2 4296 1 14076 1 7255 1 2578 1 9971 1 9973 1 11578 1 10238 1 159 1 2590 1 3699 1 1687 1 9731 1 15559 1 3436 1 5555 1 9186 3 14606 2 4919 1 3134 2 1300 1 11043 1 12395 1 4678 2 16411 1 4812 1 13583 1 10920 1 293 1 13857 2 3136 1 11189 1 3444 3 673 1 13462 1 2364 1 7415 1 6686 2 10805 1 447 1 2089 1 7799 1 12401 1 4567 1 11474 1 15675 1 7421 2 9066 1 14623 1 5567 1 3819 1 12152 1 7288 1 1708 1 8675 1 6568 1 12031 1 5054 1 10935 1 809 1 2244 2 9079 1 3303 1 185 1 14374 1 14501 1 311 1 2613 5 698 1 11070 1 3307 1 9216 1 6820 3 8332 1 14507 1 4238 1 14779 1 16078 2 707 1 10830 1 8699 1 7574 2 3320 3 6707 1 6965 1 7441 1 3988 1 1588 1 8702 1 13356 1 2263 11 5455 2 10956 1 3992 4 9893 1 11217 1 11361 1 3491 1 15058 1 471 1 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1 9664 2 16485 2 11771 1 4466 1 1406 1 13536 20 10492 1 12123 1 15241 1 1820 1 9344 2 16251 2 2677 2 8805 2 5686 1 4143 1 12705 1 9614 1 3376 2 6292 1 11273 1 7226 1 12578 1 8106 1 12536 1 15940 1 14994 2 1014 1 6665 1 3663 1 8040 2 3742 3 2579 2 11034 1 1166 1 5228 2 5606 1 13079 1 975 1 9212 1 141 1 12293 1 16494 2 10977 1 14225 1 4508 1 7012 2 10144 1 10343 1 9949 2 14188 3 1685 1 1900 1 4365 2 11612 2 14946 1 15688 1 formulaic 2617 1 7829 1 3106 1 5555 1 3393 1 8523 1 gujarati 421 1 5347 1 13144 1 9186 1 884 1 14701 1 10070 1 low_blow 2122 1 12818 1 10779 1 3679 1 15494 1 applesauce 3798 1 6232 2 5671 1 3301 1 9081 2 9680 1 13649 1 4579 1 1621 1 6890 1 1789 1 8869 1 9768 1 comfortingly 2814 1 4360 1 11002 1 9333 1 7932 1 umbra 7944 1 9672 1 5347 1 2789 1 persist 10274 1 11289 1 9387 1 3636 2 10310 1 4238 1 10985 1 9361 2 1963 1 6167 2 3280 1 12270 1 437 1 14650 1 11495 1 7477 1 1063 3 469 1 15058 1 12795 2 6969 1 15299 1 15445 1 8523 2 9665 1 9714 1 7736 1 16459 1 3113 1 13537 15 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15935 1 9203 1 2476 1 8073 1 5688 1 1982 1 16436 1 9079 3 9210 1 7178 1 1855 1 813 1 4701 1 12949 3 312 1 10526 1 13736 2 11896 1 5831 1 15687 1 1328 1 7181 2 7435 1 5957 2 5442 1 4332 1 5072 1 11349 2 9221 1 10540 3 6080 1 333 1 12959 1 2490 1 13617 1 8458 2 11760 1 14650 1 7074 1 16330 2 15590 1 10546 1 1595 1 13097 2 6089 1 7195 2 1735 1 6596 1 12568 1 1208 1 10701 1 3329 1 4728 1 10430 1 9243 1 13367 1 15065 1 2008 1 3189 5 3623 1 14274 1 2009 1 5099 1 15339 1 1743 1 8108 1 14010 1 211 1 7457 1 5228 1 14811 1 11784 1 11381 1 595 1 6105 1 11383 9 1357 1 14019 1 10979 1 1486 1 9390 1 14948 1 13250 1 4488 1 14818 1 4264 1 9116 3 9654 1 1907 2 5988 1 12590 1 5739 2 741 1 7729 4 14160 1 6498 6 125 1 1773 1 126 1 11666 1 8490 1 4621 1 12471 1 1246 1 4761 6 5505 3 13538 62 9937 1 10076 1 6630 1 4762 5 10335 1 7492 1 7006 1 3084 1 4998 1 11002 2 7230 1 1134 1 2546 1 4767 1 6890 1 14838 1 2948 1 13012 1 11278 1 16494 1 4045 1 11282 19 2953 1 15752 1 6391 1 8505 1 soda_water 4709 1 1142 1 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15317 2 personage 7532 1 8673 1 15398 1 4179 1 4369 1 2723 1 11905 1 14415 1 12145 1 7374 1 9285 1 1476 1 10418 1 16081 1 1099 1 12244 1 4129 1 2836 1 4211 1 11081 1 10058 1 9502 1 2945 1 14887 1 7789 1 15853 1 524 1 5214 1 6929 1 1987 1 16159 1 4101 1 4219 1 14587 1 2814 1 11789 1 orthophosphoric_acid 5442 1 14226 1 15430 1 16438 1 tutelary 6520 1 3308 1 8408 1 2326 1 12639 1 14851 1 11636 1 collodion 14679 1 12831 1 4856 1 13706 1 curet 12630 1 13034 2 12980 1 11857 1 10339 1 15725 1 8110 1 12539 1 12856 1 11838 1 12059 1 10755 1 9116 2 5681 1 4951 1 3068 1 13855 1 11712 1 9599 1 14992 1 recite 7566 1 4237 1 10895 2 3672 1 6200 1 1024 1 3242 1 3776 1 1523 1 759 1 4950 1 11755 1 2116 1 1395 2 10279 1 11147 1 8083 1 9285 2 1997 1 10544 1 16216 2 3677 1 7827 2 9955 1 2332 1 6713 2 9021 1 9705 1 12894 1 8892 3 15959 1 5528 1 641 1 6596 1 15702 1 10772 1 2849 1 14271 1 12571 2 13104 1 7027 1 7582 1 5170 1 15704 2 6779 1 3041 2 5717 2 8897 1 8529 1 12372 1 531 1 1099 1 9778 2 6230 1 12003 1 12255 1 10570 1 14813 1 14147 1 1750 1 4086 1 15225 1 2797 1 1056 2 13850 1 1565 1 6856 1 14023 1 10503 1 16043 1 12011 2 10248 1 4093 1 16410 1 2805 1 432 3 14232 7 1176 1 932 1 6241 1 5244 1 12085 1 11130 1 1238 2 12020 1 9264 1 9860 1 1072 4 10131 1 14829 1 15450 1 9340 1 9609 1 14034 1 129 1 9477 1 14555 3 4761 1 6630 1 8070 1 15880 2 15026 1 13543 35 3527 5 1250 2 10522 1 811 2 13602 1 14307 1 15459 1 4169 1 9682 1 11065 2 10525 1 12159 2 141 1 9354 1 14251 2 5366 1 15032 1 7122 1 10943 1 1656 1 6894 1 tributaries 5442 1 9372 1 802 1 9241 1 3310 1 14802 1 13367 1 8822 4 884 1 3164 3 2300 1 3258 2 6226 1 11726 2 1963 2 6614 1 5469 1 11147 1 14782 1 10477 1 6841 1 11228 1 14865 1 13542 12 7868 1 8884 1 3580 1 5616 1 13063 1 362 1 4296 1 6323 2 4829 2 16311 2 6721 1 14714 1 3992 2 14650 2 5161 1 5179 1 10305 3 8993 1 7344 1 7872 1 1104 1 3384 1 3412 1 14954 2 2605 1 2917 1 1596 1 10131 2 8128 1 5180 1 9384 1 9791 1 frivolous 10415 1 11457 3 4332 1 12284 1 10071 1 9982 1 10247 1 4406 1 13674 1 6553 1 1784 3 7880 1 8031 1 13490 1 14002 1 8570 1 13544 19 8192 1 15829 1 6323 1 4198 1 6530 1 9021 2 9430 1 1148 2 7230 1 12429 1 14515 1 11485 2 16104 1 3862 1 4545 1 1595 1 9400 1 4912 3 10324 1 8180 1 11408 1 7581 1 4706 3 4707 3 2918 1 13863 1 6853 2 8914 3 2184 1 2111 4 ice_field 1845 1 1252 1 1770 1 9477 1 972 1 15028 1 bookmaker 5861 1 9412 1 15555 1 12323 1 7800 1 12798 1 5187 1 5271 1 5926 1 chimney 4456 2 12229 1 16263 1 7368 1 1660 1 1992 1 7937 1 3676 6 10478 1 7239 1 3320 1 6398 1 6966 1 10421 1 8702 1 9021 1 14980 4 15135 1 10836 1 1148 1 8205 1 2121 1 4957 1 9704 2 11302 4 4064 1 2904 1 15205 3 8212 1 16331 1 2636 1 10491 1 4464 3 9714 1 3556 1 4350 1 10559 1 718 1 15339 1 6416 1 4145 1 3501 1 536 1 14598 1 8292 1 4200 1 11575 1 11513 1 213 4 1290 1 9784 1 488 1 6849 1 8476 1 11939 1 2986 1 4205 1 6731 1 3868 1 11042 2 14533 1 3132 2 2222 1 11794 1 7210 1 1836 2 2527 1 15290 1 7972 2 11394 2 359 1 13461 1 13583 2 3875 1 4921 1 863 1 2466 1 6053 1 5618 1 4433 2 10867 1 4925 1 9459 1 14888 2 11401 1 7344 1 11946 1 676 1 12147 1 442 7 11406 1 5681 1 10926 1 10675 1 14429 1 999 1 6801 1 9607 1 13002 4 6377 1 557 2 3145 1 14555 1 6506 2 945 1 13801 1 6753 1 9413 1 9074 3 13008 1 13656 1 877 1 13545 28 2945 1 10751 5 15030 1 16491 1 4169 2 4228 1 3227 1 8143 1 14838 1 9485 1 455 1 7010 1 1651 1 5002 1 14183 2 8449 1 11896 1 12487 2 8394 1 15518 1 4327 3 10018 1 15992 2 13286 1 14506 1 tunis 5183 1 9934 1 12123 1 9210 1 5228 1 884 1 13079 1 665 1 328 1 7012 1 2005 1 11924 1 9748 1 1982 1 isaac 4283 1 542 2 2143 1 8624 13 6984 1 6732 4 13417 2 2526 2 9821 1 7372 1 2598 1 2326 13 15729 1 8083 1 15637 2 1871 2 6137 1 8055 1 6556 1 11215 2 12360 1 13712 1 12726 4 8991 8 866 1 8575 1 8124 1 5214 1 10125 1 13826 1 1063 1 9061 1 14585 2 13877 3 12795 2 13434 1 11533 1 10361 1 939 5 905 7 10363 2 58 1 10997 1 9298 2 13801 1 9937 2 7061 2 13371 1 15339 2 11743 2 9906 2 14941 3 1510 1 590 1 11886 1 3531 2 13474 1 15118 1 13546 17 13759 1 6890 1 8648 1 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11346 1 14189 1 14972 1 9278 2 ischium 9639 1 4591 1 5232 1 6679 1 1131 1 seriousness 1635 1 1920 1 12643 1 9474 1 15821 1 1024 1 8153 1 16377 1 9635 1 13653 11 4875 1 5581 1 8361 1 584 1 11728 2 3676 1 8659 2 3778 1 13575 1 8101 2 11607 1 8192 3 7006 1 3084 3 11108 1 2945 2 15135 1 1363 1 12249 2 15268 1 14820 1 5439 1 11485 2 13428 1 7042 1 1595 1 3813 2 4889 1 13720 1 11749 1 4705 1 2054 1 1466 3 4247 2 6043 1 9044 1 11385 1 1916 1 15271 1 10068 1 3647 1 2111 1 apse 6192 1 14068 1 3986 1 pains 1331 2 12298 1 13158 1 758 1 884 1 2697 1 13821 1 11292 1 7574 1 6655 1 7509 1 2329 1 10698 1 5778 1 5966 1 6592 1 9704 2 8517 1 7189 2 7191 1 14066 1 1595 1 12305 1 7636 1 3917 1 3618 1 1736 1 10360 1 12178 1 1881 1 11985 1 14798 1 11703 1 5171 1 11986 2 11709 2 5853 4 4964 1 12439 2 12318 1 7770 1 9775 1 11227 1 7836 1 12707 1 11104 1 16026 1 15916 1 4146 1 12186 1 11108 1 5103 1 15157 1 3741 2 8110 1 14012 1 1607 1 2214 1 1893 1 8179 1 5664 1 4082 1 2346 3 8226 1 8180 5 6359 1 16036 1 2797 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7864 1 5632 1 5028 1 14525 1 7869 1 6255 2 15812 1 14773 1 10642 1 8040 1 14600 1 3303 1 1893 1 7063 1 6789 1 11282 2 11489 1 13286 1 4581 1 3059 1 6264 1 journalistically 10325 1 11011 1 954 1 scriber 7421 1 7387 1 3158 1 13209 1 1721 1 7912 1 2425 1 7494 1 idolatrously 10196 1 diacritic 11428 2 1395 2 15898 1 15459 1 16471 2 8596 1 14658 2 12727 2 6241 2 13552 2 717 2 mawkish 14855 1 4998 1 11155 1 unfeigned 11400 1 8081 1 3741 1 5901 1 13490 1 15601 1 9573 1 7733 1 3115 1 upward 14314 1 11429 1 7366 1 11008 1 15254 1 12229 2 13158 1 7368 2 6898 1 252 2 10758 1 7016 1 10221 1 9282 1 9557 1 15633 1 3395 2 4182 1 15759 3 3779 1 15900 1 4185 1 2329 1 6656 2 8012 4 9021 1 5900 1 3678 3 5901 1 8013 1 4531 1 14461 1 10226 1 9705 2 14718 1 14199 1 13169 3 4062 1 6145 2 4401 1 4064 1 13029 1 13432 1 1815 1 7138 1 14 1 13434 3 10771 1 11158 1 11984 1 4659 5 7642 1 10492 1 10628 1 16278 2 7766 1 3113 1 11307 1 6152 1 14212 1 7035 1 5029 1 10779 1 11570 1 9437 1 15917 1 536 1 13443 1 16398 1 8292 2 918 1 12000 1 5178 2 657 1 9971 5 1957 1 8180 2 13309 1 8911 1 16405 1 7260 1 5281 1 7776 1 5180 2 11582 1 6161 1 4205 2 2349 1 12138 1 8541 1 11724 1 5926 1 12786 2 1568 1 10250 1 7661 1 7159 1 2598 1 8309 1 8545 1 11185 1 5805 1 6171 2 13193 1 13461 2 15564 1 5807 1 13063 1 4921 1 13711 1 2997 1 2466 2 293 1 7541 2 12270 1 3136 2 673 1 11189 3 4308 1 7055 1 12669 1 10511 2 5810 1 11595 1 14616 1 7793 1 2747 1 14236 1 15672 1 4102 1 442 2 9464 1 15570 1 7796 1 12795 1 8434 1 5425 1 2089 1 13467 1 15018 1 15447 1 10131 1 3591 1 8438 1 7421 1 9470 2 6059 1 1183 2 8554 1 1436 1 11602 1 10136 2 14764 2 3013 1 6448 2 7682 1 3958 1 9203 1 3150 1 62 3 1851 1 9869 1 13214 1 14101 2 9077 1 15028 1 4698 1 2477 1 12034 1 11063 1 11200 1 8325 1 3830 1 15311 1 6574 1 2613 2 3970 1 8944 1 13736 1 455 1 14634 1 13081 1 3158 1 12036 1 14106 1 16441 1 12543 3 6954 1 6070 1 7814 1 10018 2 11489 2 11612 1 7436 1 12295 1 14506 1 5579 1 9494 1 4944 2 5443 1 5959 2 6823 1 11901 1 573 1 16320 1 71 1 4335 4 16321 1 329 4 15470 1 2897 1 15041 1 6828 1 15827 1 14118 1 15043 2 7574 1 3320 1 10418 1 7697 1 5449 1 15472 1 80 1 14121 1 7442 3 10280 1 7304 1 4590 1 5705 1 5966 1 16328 1 8205 1 14650 3 7189 1 9368 1 7581 1 15701 1 4248 1 12569 6 7313 1 14270 1 9238 1 2277 1 9899 3 3038 1 1211 1 15838 1 9096 1 7583 2 16459 1 10430 1 8708 2 8582 1 10556 2 10706 1 13368 1 7585 1 2131 1 2132 1 13111 2 4143 1 718 2 10847 1 15339 1 16096 7 7837 2 14525 1 7321 3 2914 1 12578 1 1218 1 12322 1 4256 1 11233 1 10303 1 14010 1 3743 1 11513 1 8980 1 3195 1 11646 1 213 1 8369 2 1893 6 1104 2 11928 1 3507 1 595 1 14281 3 10570 1 977 2 13895 1 2797 1 11935 1 599 2 13768 3 6853 1 14286 1 8373 1 12718 4 11119 3 5733 1 8592 6 5610 1 13900 1 12586 1 3636 1 732 2 12981 2 4155 1 2802 1 16116 1 9259 2 7210 1 7471 2 2529 2 3065 1 10864 1 11941 1 3875 1 6367 1 7856 1 2532 1 11943 1 5618 2 2303 1 5492 1 13531 1 866 2 14424 1 8243 1 9532 8 10587 1 6499 2 15103 1 1624 1 11946 1 227 1 9399 1 11406 1 15237 1 13395 1 5500 5 2306 1 503 1 14954 1 5745 1 14826 1 12998 1 7731 1 999 1 4874 1 7486 2 15877 1 15611 1 14687 2 13397 3 1370 1 5135 1 6502 1 12218 1 236 1 7218 2 8868 5 5246 3 15745 3 4031 1 8138 1 13404 1 2680 1 6629 2 876 2 15381 3 12740 2 16253 1 10743 1 3223 1 3376 1 12608 1 4882 1 8139 1 13656 94 2943 1 5756 1 512 1 13545 1 2426 1 1512 1 11956 1 15118 1 10597 1 1014 1 15887 2 10751 2 4169 7 4384 1 15249 1 7360 1 10340 1 16258 1 14838 1 16494 2 11684 1 12225 6 7120 1 1521 1 6638 2 11686 2 15753 1 9421 2 11964 1 14972 1 13286 1 6894 3 2826 3 1022 2 disregarding 4466 1 2122 1 12818 1 10779 1 3679 1 15494 1 fluoroscope 2862 1 12353 1 12020 1 scrap 6011 2 400 1 9164 1 5555 1 12140 1 15398 1 11735 2 14461 2 9423 1 3001 1 2030 1 12041 1 8787 1 8870 2 7260 1 13371 1 7239 7 6830 1 13089 1 8946 1 14047 2 2960 1 13657 7 extenuating 1920 1 5813 1 5188 1 7170 1 stipend 7969 1 10995 1 14342 1 5345 1 1055 1 2927 1 13482 1 horehound 11593 1 3760 1 11956 1 9057 1 5126 1 7765 1 scut_work 5082 1 323 1 15032 1 9044 1 4886 1 2170 1 6237 1 ravelling 2335 1 1340 1 11349 1 4358 1 form_genus 7421 1 16446 1 12643 1 3200 7 15354 1 14639 5 732 1 5173 3 11541 1 14212 1 2062 1 7158 3 12609 1 10831 1 9638 1 5449 2 6367 1 2836 1 14962 1 892 1 12270 1 5295 1 3992 1 6888 1 2152 1 13658 9 9308 2 15736 1 9644 1 12308 1 5849 4 144 1 5088 1 15301 2 14972 1 meddle 11909 1 14719 3 1145 1 7031 1 flipper 7541 1 6324 1 3136 1 3555 2 3964 2 6907 1 8595 1 1104 1 1534 3 5136 1 16207 1 12006 1 10475 1 1300 1 4664 1 13434 1 14490 1 11346 1 12353 1 11414 1 11612 2 8802 1 12017 1 11474 1 10991 1 9590 1 tranquilize 3537 1 12731 1 6145 1 11684 1 996 1 7824 1 5307 1 5527 1 179 1 germanium 8076 1 9097 2 1088 2 15038 1 14726 2 10559 1 11308 1 15470 1 15636 1 8449 1 6910 1 5390 1 9179 1 5886 1 9467 1 2539 1 4185 1 2043 1 4464 1 1929 1 feat 1330 2 7365 1 3350 1 16350 2 5518 3 7267 1 9185 1 2300 1 1961 1 9259 1 14534 1 11728 1 9116 4 13190 1 9151 1 13746 1 7539 1 14060 1 11215 1 13930 1 14121 1 990 1 2490 1 6530 2 5128 1 7729 1 3483 1 4531 3 13260 1 7191 1 7279 1 15175 1 6311 1 9400 2 3815 1 6685 1 3617 1 4247 1 9264 1 9860 1 13171 1 14797 1 1540 1 9899 1 9665 1 8093 1 5883 1 5750 1 11876 2 6032 2 11133 1 14274 1 4143 1 14494 1 10076 1 12534 1 5053 1 3082 1 915 1 15114 1 6228 1 3016 1 3527 2 11677 1 1510 1 2914 1 11744 1 5400 1 7496 1 11108 1 11679 1 6230 1 16065 1 14010 1 2481 1 13276 1 5178 1 4228 1 13637 1 14869 1 13659 14 1167 1 14838 1 6361 1 15034 1 12519 1 3238 2 vaporized 14071 1 2988 1 6957 1 10226 1 1757 1 9219 1 9533 1 4142 1 501 1 11861 1 11684 1 6080 1 15636 1 15827 1 6554 1 7393 1 1009 1 15875 1 13264 1 2594 1 15316 1 14956 1 alternative 2384 1 1330 1 6011 1 5443 1 7366 2 8950 2 8879 1 9819 1 6336 1 8153 1 11552 2 704 1 2114 1 13552 1 12556 1 3167 1 9558 1 14978 1 12041 1 13674 1 9886 1 15636 1 7757 1 7374 1 11820 1 8083 7 6081 1 2627 3 2558 1 7759 1 5964 1 15046 1 15472 1 3101 1 10544 2 11694 1 11081 2 4061 1 4529 2 6530 1 12362 1 3404 6 7376 1 3405 1 15052 1 14461 1 3996 1 2563 1 12563 1 11087 1 6532 1 12898 1 10358 1 3487 1 4900 1 11090 1 7135 1 8020 1 5272 2 13878 1 5849 1 6969 1 13991 1 6833 1 3792 1 12570 1 8408 2 9714 1 14071 2 4659 1 8167 2 10363 1 13754 1 13626 1 8834 1 6781 1 3926 1 6286 1 13574 1 6473 2 1949 1 6035 1 12441 1 4076 1 838 1 4907 1 10043 1 3933 1 8769 1 536 2 13444 1 2509 1 6230 1 14010 1 5179 1 12133 1 7649 2 12711 2 12381 3 14280 1 8844 6 3568 2 1749 2 12006 1 5608 2 848 1 1053 1 16177 1 13765 1 15553 1 16036 1 7593 1 7393 1 2918 1 1562 2 11939 1 14673 1 5183 1 8726 1 12718 1 1903 1 10658 1 5120 1 13128 2 6732 3 2862 1 6733 2 13320 1 10380 1 12011 1 15790 1 6433 1 15170 1 2301 1 5346 1 2598 1 4021 2 1693 2 9189 1 1907 2 6108 1 15667 1 16475 1 5873 1 3582 2 6172 1 4307 1 13712 2 4925 2 5493 1 4680 1 1362 1 7166 1 7413 1 16419 1 8796 1 6498 1 1915 1 1236 1 8246 1 6442 1 15871 1 12596 1 14297 1 8997 1 5245 1 12727 1 10131 1 6445 1 4689 2 9337 2 12401 1 13209 1 3077 2 3143 1 448 1 1183 1 6566 1 1185 1 12342 1 6318 1 15305 1 15615 1 8870 2 62 1 13214 1 950 1 10685 1 3596 1 13273 1 1510 1 11414 1 5573 1 12033 1 5361 10 1931 1 2426 1 11956 1 8682 1 10750 1 14913 1 11485 1 3900 1 13660 57 2950 1 16141 1 14840 1 6516 1 14251 3 4706 1 11686 1 8946 2 320 1 9421 1 12620 3 13482 1 11347 2 7623 1 12107 1 momentaneous 4680 1 9021 1 5621 1 4530 1 3997 1 5063 1 11461 1 14585 1 12036 1 4076 1 3091 1 1968 1 7169 1 1564 1 vehicle_traffic 14507 1 6509 1 16008 1 520 1 2195 1 8869 1 14956 1 festoon 653 2 1744 1 15541 1 10310 1 13054 1 14606 1 12003 2 10671 1 3812 1 13970 1 5231 1 15242 1 7864 2 4409 1 3004 1 1583 1 1667 1 988 1 12958 1 447 1 15753 1 14196 1 4185 1 14525 2 11437 1 9103 1 883 1 9278 1 7984 1 fearfully 11108 1 5778 1 13181 1 15537 1 14228 1 recreant 12410 1 12643 1 927 1 7970 1 11646 1 3926 1 14055 1 8710 1 15306 1 10076 1 7121 1 9168 1 15191 1 8072 1 13371 1 magnetism 1565 1 4867 1 15692 1 15130 1 4261 1 2523 1 16321 1 8050 1 8628 1 5412 3 5674 1 12145 1 6080 1 13460 1 6270 1 3676 2 1907 1 13089 1 670 1 5584 1 12268 1 2467 1 9429 1 14855 1 5586 1 3404 1 12726 1 3138 1 13560 1 7729 1 13931 2 12561 1 3483 1 13260 2 6499 2 1947 2 14236 1 6244 1 1667 1 9711 2 4001 1 8997 1 4723 1 15875 2 14956 2 7420 1 4466 1 4933 1 11163 5 13004 2 16392 1 14831 1 10816 1 10559 2 15744 1 11133 2 15410 1 62 1 1710 1 10495 4 6910 1 3223 1 14436 2 13273 1 12156 1 4502 1 15964 1 13943 1 9640 2 5103 1 11999 1 8108 3 6257 2 7178 1 8718 1 11035 1 15656 1 7009 1 3630 1 14738 2 13661 19 10271 1 2139 1 10143 2 9728 2 11686 1 1055 1 2516 1 13482 1 13814 1 photomechanical 12856 1 12020 1 3556 1 substantially 10492 1 5733 2 11707 1 15692 1 1437 1 12800 2 13801 1 2598 1 585 2 9071 2 7274 1 11777 2 14679 1 7958 1 12268 2 11339 2 2662 1 2945 2 12631 4 14544 1 11464 2 16491 2 11084 2 13512 1 6498 1 14323 1 12995 2 3862 2 13662 7 11406 3 15406 2 4707 1 4464 1 sea_turtle 10253 1 12123 1 7657 1 11314 1 3566 1 9291 1 9635 2 10790 1 204 1 12815 1 3134 1 2373 1 15434 1 2306 1 8824 1 3320 1 1596 1 7868 1 4185 1 2882 1 9384 2 9206 1 375 1 7350 1 temperament 8592 1 6336 1 7049 1 5836 2 12848 1 12145 1 1395 1 4021 2 1028 1 1998 1 15762 1 8751 1 7441 1 4158 1 4529 2 4590 1 12466 1 11083 1 15799 1 12272 1 7669 1 8662 1 11358 1 5708 2 14323 1 13912 1 1627 1 12212 1 3491 1 7703 1 12795 1 16387 1 15984 1 9539 1 14658 1 7545 1 179 2 8438 1 12643 2 1004 1 9609 3 3145 1 3218 1 13626 2 5534 1 7316 1 3594 1 8867 1 4761 1 14274 1 7992 1 4695 1 8901 1 12534 1 240 2 5309 1 5913 2 12578 1 7227 1 11781 1 12380 1 5000 1 11485 1 14442 1 14181 1 8910 1 13663 16 594 1 4889 1 14106 1 3534 1 4707 4 1055 1 8849 1 7595 2 5694 2 1140 1 11939 1 6488 1 small_ship ventriloquist 10253 1 6596 2 9506 1 4200 1 573 1 12268 1 3831 1 11928 1 11157 2 plumelike 11431 1 14623 1 10310 1 1925 1 5173 1 12815 1 8786 1 10632 1 13821 1 4263 1 15695 1 3134 1 10906 1 12968 1 4143 1 2436 1 3354 1 6965 1 3501 1 2244 1 8702 2 8458 1 2263 1 9680 1 16301 1 1732 1 7360 1 3814 1 16314 1 1560 1 3616 1 3760 1 13664 5 7904 1 14297 1 447 1 12518 2 15082 1 556 1 10701 1 1685 1 15181 1 15574 1 9278 1 7350 1 bound_up 12943 1 12537 1 10182 2 12668 1 12693 1 15377 1 12848 1 8775 1 6774 1 12721 1 11895 1 8758 1 10978 1 998 1 16055 1 5688 1 13712 1 mohs_scale 4944 1 3939 1 9451 1 16236 1 13626 1 2045 1 15089 1 stockroom 10223 1 13895 1 8769 1 4248 1 polysaccharide 14449 1 6740 1 918 1 3113 1 2229 1 14259 1 11327 1 7902 1 768 1 14251 1 9728 1 4951 1 15251 1 3386 1 2335 1 16276 1 5694 1 6535 1 9546 1 1562 1 11425 1 10765 1 1415 1 newly 5442 1 5959 1 4178 1 10606 1 9086 1 12229 3 9819 1 8153 2 9423 3 6525 1 704 1 8156 1 13350 1 1145 1 3167 1 8453 1 13422 1 3396 1 8881 1 16268 1 6134 3 3676 1 256 2 6830 1 13089 3 3320 1 333 1 3841 1 14121 1 14514 1 4590 1 13356 1 2196 2 10619 1 1088 1 960 1 2001 3 15267 1 14199 1 12898 1 10161 1 12566 2 5849 1 8021 2 12568 1 12569 3 13104 2 9714 3 5329 1 9830 1 7451 1 12643 1 12123 1 5785 1 14328 1 10554 1 644 1 2720 1 15482 1 7316 1 2448 1 11026 1 4537 1 8639 3 5173 1 4662 1 12059 1 4143 1 8288 1 15846 2 14804 1 13888 1 8468 1 9377 1 1099 1 6720 4 1604 1 8769 1 4669 1 14994 1 12246 3 11233 1 8040 1 4203 2 16104 1 12003 2 9971 1 12255 1 9914 2 16344 2 14671 2 4975 1 13380 1 12715 1 3802 2 2920 1 14873 1 3699 1 3348 2 10721 4 9648 1 7531 1 4017 1 7657 1 10983 1 8477 1 12393 1 12458 1 15925 1 6432 1 16116 1 12848 1 15089 2 5927 1 8479 1 3354 3 737 1 1908 1 9394 3 3515 1 3703 1 15667 1 9856 3 3582 1 2927 1 5618 1 12270 2 6739 1 5295 1 6557 1 11400 1 3072 2 12209 1 12792 1 13392 1 11049 1 13718 1 16051 1 10324 1 6441 1 9662 1 12595 1 7544 1 2306 1 5745 3 5298 3 16358 2 447 1 3520 1 8997 1 10131 1 13264 1 1504 1 179 1 7350 1 801 1 12731 1 1075 1 1005 2 12736 1 10333 1 15110 1 9542 1 11133 1 11676 3 7864 1 3295 3 13801 1 3652 1 11333 1 13971 3 3653 1 8322 1 12480 1 1646 1 1510 1 4503 1 5888 1 8496 1 953 1 11339 1 10402 1 9206 1 9811 1 16436 1 1189 1 244 1 10750 1 7230 1 16491 1 813 1 14374 1 7744 1 14838 1 16494 1 10754 1 14251 1 12810 2 12160 1 68 5 6700 1 4707 1 15519 2 5003 1 13666 41 11489 1 1656 1 6194 3 2826 1 6894 2 probabilism 16455 1 14307 1 mandibular_notch 15889 1 11474 1 arcadia 9420 1 stamen 11431 1 3313 1 8822 2 9115 1 4155 1 14607 3 3392 2 4263 1 15759 2 9014 1 12697 2 4870 1 11437 1 8572 2 13712 2 5618 1 12822 1 4268 1 15830 1 14542 1 12892 2 9156 1 15267 1 3993 4 1732 1 6466 1 84 1 1621 1 1624 2 1236 1 897 1 15297 1 4102 1 1428 2 3760 1 15299 1 2394 1 999 1 11021 1 7384 2 11443 1 6801 3 9665 1 8093 1 12643 1 14801 1 2850 2 91 1 8171 1 7452 1 9635 1 2568 1 13968 1 10742 1 3258 1 14212 4 11413 1 6692 1 5756 1 14370 1 7085 2 12578 4 15028 1 4698 3 13216 1 1932 1 6977 1 11956 2 11063 1 1649 1 15219 1 14010 1 13076 1 10465 1 9444 1 9945 1 16405 1 318 1 10018 1 13768 4 14047 2 7333 1 9047 1 11427 2 13581 2 13667 15 sio2 14226 1 14124 1 8458 1 2642 1 13859 1 732 1 7218 1 11327 1 4830 1 cheater 12145 1 3676 1 7484 1 10067 1 10211 1 13551 1 2114 1 6241 1 7254 1 13476 1 nonappointive 15225 1 12757 1 14626 1 2986 1 rubber 6011 1 387 1 758 1 13669 98 2553 1 10473 1 12109 1 254 1 12622 2 10221 4 3393 2 8628 1 14710 1 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2 9532 1 4957 2 5000 2 10742 1 7658 1 15269 1 13038 2 3352 1 13736 1 3795 1 11406 2 3831 1 3251 1 68 2 6210 1 3501 2 pigeon_pea 9635 1 5106 1 15267 1 13706 1 olfactory_nerve 1836 1 7496 1 3065 1 1660 1 4951 1 812 3 14203 1 6520 1 6211 1 16396 1 6318 1 14759 1 9618 1 hitchcock 7387 1 4499 1 227 1 mexican 4393 1 5262 2 13551 1 13672 78 2698 1 10221 1 13821 2 5 1 7757 1 11147 1 9285 1 16148 1 9563 1 14196 1 2437 1 8512 3 4185 2 14458 1 12361 1 13681 2 8886 2 14715 1 10 2 151 1 523 1 15267 1 9291 1 15136 1 8015 1 897 1 10487 1 14584 2 15767 1 5783 1 2335 1 13434 1 6535 1 6284 1 14859 1 11162 1 6778 1 6777 1 3793 2 3112 1 2718 1 8026 1 11564 1 6151 3 5173 1 3258 5 8765 1 6289 1 11449 1 5791 3 15916 1 8768 1 11312 1 16398 1 3123 1 12246 3 917 3 11033 1 7257 1 6666 1 11035 1 7900 1 8179 1 1560 4 4803 1 13845 3 7260 1 5035 1 7904 1 5549 1 924 1 15920 7 1685 1 13706 1 5671 3 9186 2 11859 1 5556 2 15789 1 4676 1 10792 1 1836 1 3134 2 4809 1 6167 1 7158 1 5188 1 5929 1 15171 1 14230 1 13191 1 293 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1 5705 1 14514 1 16153 1 5901 2 5967 1 5324 1 3992 1 151 2 13682 46 1534 2 13935 12 7134 1 834 2 2124 3 8021 1 5849 1 3495 2 8523 1 6282 1 12569 1 1596 3 6535 2 8581 6 3555 6 4468 1 12643 2 10965 1 10364 2 9635 1 5853 1 3114 1 8529 1 4352 1 1041 1 4143 1 4664 1 968 1 11228 5 6785 1 2402 2 5913 1 14216 2 12577 1 14077 1 9041 1 2857 1 8472 1 3744 1 16465 1 2581 1 5338 1 1104 5 6232 1 13515 1 7903 1 16405 1 9841 1 5180 1 13382 2 4365 1 3269 1 13312 1 15971 1 12718 1 12522 1 12075 2 6862 1 15288 1 1963 1 1836 1 12785 2 2529 1 1173 2 7273 11 16411 1 5041 1 13904 1 5988 4 7974 1 2873 1 6494 2 15011 2 7541 3 10253 1 10867 2 3072 1 225 2 8663 1 2229 1 8605 1 3812 4 9464 1 16126 1 7542 1 11259 1 5355 1 9064 1 14428 1 2089 2 9860 1 8128 1 8802 6 11474 1 4758 3 375 1 7421 2 14623 1 16056 1 13004 3 11196 1 15576 1 12342 7 3650 1 9477 1 9069 3 5136 1 2312 1 11541 3 8675 1 8555 1 1643 4 16196 1 3765 1 7552 1 11414 1 14911 1 10746 1 3084 2 9207 2 16311 1 11274 1 15028 1 11956 1 14439 1 12096 1 7230 1 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11787 1 2986 1 15688 2 13312 2 9845 1 ladybug 6442 1 4522 1 7457 1 3964 1 16350 1 311 1 3258 1 13609 1 13725 7 1963 1 1755 1 eurasia 8506 1 11431 1 8879 1 15757 1 884 7 13821 1 885 1 11292 9 1799 1 15759 1 255 1 15261 1 2436 8 9697 1 1943 1 6396 2 13290 1 14196 3 8512 1 14060 1 4185 3 15763 1 6656 2 8012 1 8886 1 14980 2 15404 2 16004 3 1807 1 16157 1 9706 1 897 1 8021 9 4402 1 10094 1 2970 2 11162 1 12508 1 6409 1 3258 2 10775 1 5910 1 12648 1 8642 1 5028 1 15651 1 9172 1 6419 1 14076 1 14077 1 8040 1 12380 1 2069 1 10784 1 2735 1 9973 1 15281 1 1558 1 3128 1 13309 1 159 2 1683 1 14338 2 14603 1 6430 1 3269 1 13312 1 9590 2 12521 1 5555 5 12263 1 12524 2 15432 1 9186 1 10790 1 14606 2 7154 1 5556 1 8786 1 15288 1 12786 1 3135 1 4678 1 10918 1 13583 2 2869 1 293 7 7410 1 6926 1 7541 1 15796 1 7054 1 16417 2 6681 1 4099 1 6560 1 13718 1 10128 1 6686 1 8434 1 15299 1 10392 1 447 1 2089 4 7798 1 6178 1 800 1 4933 1 10263 1 14623 1 12284 1 13726 73 13967 1 13968 1 12799 1 1709 1 6568 1 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10473 2 11349 1 3608 1 5264 1 3395 3 13422 1 14782 2 3840 1 9285 1 7239 1 6830 1 13491 1 9953 2 4185 1 11081 2 8 1 14980 3 1088 3 15052 1 3992 2 15473 1 9291 1 9704 1 85 2 5214 1 8517 1 13935 1 7636 1 2846 1 13171 2 1596 1 14270 1 14934 1 13567 1 11771 1 14071 2 6907 1 12643 3 5718 3 4904 1 7585 1 6096 1 13440 1 2853 1 4143 1 11102 1 779 1 13371 1 11029 3 11777 1 11228 1 8645 1 9723 1 6419 1 7254 1 4541 1 7256 1 1829 1 6230 1 2579 1 11375 1 5179 25 15548 1 15547 1 9248 1 1893 1 16104 1 10569 1 6918 1 2797 1 13518 1 9109 1 4365 2 9384 4 2296 1 9920 1 13706 3 5183 1 11457 3 7657 2 7154 1 10312 1 1300 1 15358 1 5487 1 7853 1 12723 1 120 1 15975 1 6555 1 5616 1 293 2 4815 1 10321 1 11464 1 9532 1 13392 3 6621 4 1770 1 370 1 227 1 12084 5 3813 1 1773 1 8386 1 2089 12 16358 3 6444 2 5134 1 10131 3 3647 1 6801 1 9665 1 8250 1 7350 1 10263 1 10995 1 14239 1 6502 3 13728 53 12936 2 1437 1 10742 1 9998 1 5505 1 9999 1 9479 1 2545 2 7868 1 6322 1 4698 2 4829 1 13867 1 4040 1 6124 1 3463 1 8325 1 2546 2 185 1 12873 2 16314 2 11683 1 9489 1 7120 3 16442 1 6638 2 2043 1 16318 1 5767 1 7503 1 7014 4 sense_of_taste 1331 1 3072 2 12378 1 3882 1 13727 6 2489 1 6411 3 4099 1 269 1 8531 1 15089 1 prothorax 15651 1 9186 1 7744 1 7350 1 intransitively 10073 1 10866 1 3776 2 5307 1 liberia 101 1 9415 1 768 1 484 1 10335 1 6830 1 1755 1 8997 1 5228 1 9420 1 15688 1 13312 1 opponenet 4401 1 9918 1 3477 1 4389 1 10779 1 14725 1 14113 1 obeche 5295 1 9210 1 conditional_sale 7256 1 8195 1 11033 1 7587 1 14530 2 4118 1 15308 1 8981 1 16372 1 9920 1 13581 1 lavage 12879 1 11760 1 14485 1 151 1 1769 1 12059 2 3362 1 10401 1 6352 1 14167 1 4907 1 11080 1 14047 2 13297 1 14471 1 prepackaged 1596 1 6339 1 3859 1 13431 1 4093 1 81 1 festive 10492 1 15397 1 8784 1 4092 1 2802 1 13674 1 14319 1 9285 1 1187 1 10299 1 4412 1 4023 1 9206 1 14855 1 10750 1 3483 1 13027 2 14082 1 10840 1 171 2 297 1 5129 1 11749 1 3972 1 4231 1 13265 1 class_myxomycetes neurofibroma 12884 1 7636 1 12318 1 8180 1 3894 1 4698 1 sweet_lemon 13422 1 3793 1 9057 1 7904 1 10959 1 3819 1 5972 1 12590 1 4969 1 12809 1 13394 1 3983 1 street_corner 3643 1 12240 1 papuan 4877 2 12399 2 13648 2 1755 2 entertain 2638 1 4568 1 7802 1 7367 1 14113 1 4774 1 91 1 3113 1 11792 1 15788 1 15089 1 4370 1 7302 1 8070 2 4697 1 3320 2 11186 1 5805 1 13729 10 5888 1 11250 1 3859 1 11339 1 10989 1 1499 1 5082 1 1014 1 484 1 11034 1 8325 1 8314 1 8517 1 5214 2 14810 1 368 2 14220 1 8942 1 14463 1 1065 1 12581 1 8246 1 15015 1 14236 1 10325 1 1036 2 16484 1 2905 1 13171 1 1208 2 11666 1 6161 1 12071 2 8914 1 hard_up 13228 1 11033 1 16104 1 10572 1 15459 1 genus_phacelia coexist 3531 1 14766 1 15636 2 10144 1 10424 1 5298 1 10026 1 5694 1 12590 1 2377 1 2966 1 4413 1 2823 1 telegraphic 4659 2 11834 1 11163 1 2988 1 15987 2 4847 2 15630 1 7879 1 7218 1 10556 1 7658 1 6462 1 8287 2 6020 1 508 2 7473 1 7697 1 10005 1 4503 1 12889 2 2466 1 13730 14 8558 1 3406 1 3599 2 14600 1 15459 4 3483 1 8559 1 9764 1 13408 1 3971 2 962 3 10840 1 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11728 1 2598 6 8309 1 2869 1 10666 1 293 2 9598 1 1697 1 2227 1 5295 1 4925 1 3706 1 13859 1 13714 1 4099 1 10512 1 11327 1 5422 2 10127 1 5298 1 3006 2 12795 2 8550 1 447 1 1845 1 15572 1 6801 1 11474 3 7420 1 802 1 6941 1 10264 3 13967 2 4446 1 9206 1 953 1 12536 1 12682 6 5436 1 9210 1 309 1 812 2 13734 53 13735 1 13978 1 12414 1 12811 1 16442 1 15034 1 10411 1 6580 1 9216 2 11612 1 16210 1 7568 2 5443 1 15582 1 13609 1 10535 1 6201 1 9219 1 4335 1 6080 1 13985 1 11619 1 2489 2 10543 1 7574 2 11353 1 6965 1 5449 1 1999 1 10421 1 8702 1 4591 1 335 1 5455 2 15473 1 3992 2 3324 1 1595 1 3487 2 13359 1 13878 1 1596 1 5972 1 8581 2 4347 1 14135 1 10554 1 16459 2 6350 1 9635 5 5853 1 16335 1 9903 1 8099 1 3623 1 12059 1 4143 1 11227 1 5722 1 3501 1 2011 1 15491 1 12192 1 11645 1 1893 2 16104 1 3863 1 9106 1 12838 1 2291 1 1357 2 2921 1 2517 1 7598 1 5984 1 14674 1 3200 1 16350 1 6731 1 2143 1 2019 1 1755 1 9922 1 3636 1 6985 1 10310 3 1756 1 9115 1 12983 1 12326 1 4263 2 15973 1 9116 1 5347 1 14027 1 12590 1 3875 1 11527 1 12991 1 1621 1 15736 1 1915 1 2028 1 3760 4 9536 1 998 1 4994 1 14830 1 13535 1 869 1 4030 1 12732 1 3524 2 10742 3 14557 1 7221 1 4031 1 10461 1 8259 1 7006 1 1932 1 11956 2 9681 1 7230 1 13150 3 14965 1 7744 1 13280 3 16494 1 4280 1 14445 1 4512 1 13665 1 5766 4 16259 1 15253 1 12353 1 14047 1 1936 1 disposed 6011 1 11290 1 5007 1 14317 4 4054 1 7505 1 2326 1 11819 1 4781 1 3779 1 391 1 7759 6 15901 3 3101 1 8513 1 4529 2 7630 2 16270 2 9021 1 5778 1 5648 1 4788 1 7762 1 896 1 5528 1 2333 1 13684 1 11697 1 13171 1 527 2 5169 1 2712 1 8167 1 10492 1 400 1 10363 1 271 1 774 2 3113 1 3924 1 9716 1 3689 1 911 2 2449 1 13833 1 8529 1 5173 1 12372 1 8900 1 4539 1 2061 1 1283 2 13942 3 13575 1 13040 1 8903 1 3932 1 8645 2 5913 3 5176 1 12131 1 7390 1 5543 2 12513 1 15918 1 12249 1 11315 1 5178 1 5179 1 9443 1 12002 1 12383 1 4082 1 2859 4 3431 3 16036 2 1055 2 15784 1 13452 2 12657 1 5183 1 16180 1 9183 1 5184 1 6549 1 11858 1 3271 1 12140 1 12141 1 13707 1 9982 1 6794 1 38 3 10247 1 4806 1 11727 1 12145 1 1964 1 5929 1 3949 1 12268 1 4211 1 3582 1 1176 1 4680 1 7413 1 3001 1 5677 1 3815 1 4437 1 12795 2 13329 2 8550 1 6936 3 6444 1 7169 2 14094 1 16303 1 9608 1 300 1 10264 3 11735 1 10265 1 15022 1 15576 1 10814 1 4822 1 9203 2 8556 1 689 1 11199 1 3017 1 3153 1 305 1 14626 2 5056 1 12682 1 5436 1 309 1 13076 1 813 1 10524 1 8328 1 13734 1 5576 1 13735 54 15312 1 13082 1 2767 2 11749 1 5694 1 8946 1 16316 1 10018 1 13870 1 7067 2 5207 3 4713 1 9622 1 2255 1 12551 1 704 2 5836 1 3169 1 11618 1 13087 1 13490 3 6081 3 8571 1 13746 1 13614 1 8961 2 8084 1 5706 1 14127 2 14650 1 1875 1 7189 1 5214 1 15330 1 1595 1 10840 1 3617 1 15207 4 5087 1 12569 2 4723 1 7078 1 13753 1 10290 1 9509 1 7707 1 4466 2 1211 1 4728 1 15065 1 15842 1 7585 1 9903 1 15846 3 13371 1 11777 1 5722 1 7839 1 3859 1 99 1 12188 1 2646 1 10564 1 11109 1 9247 1 8223 1 2511 1 6980 1 15077 1 9249 1 8719 1 11648 1 13380 1 13765 1 4152 1 8847 1 14018 1 14411 1 7464 1 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2759 1 4041 1 7230 1 2547 2 4169 1 13408 1 3971 1 15121 1 8618 1 8195 2 11812 1 5512 1 2694 1 68 1 15752 1 12105 1 5957 2 6264 1 independently 5442 1 16374 1 758 1 6958 1 11754 1 389 1 4950 1 13674 1 11147 2 11293 1 9285 1 13089 3 3320 1 16001 1 3546 1 10421 1 16217 1 3483 1 15590 1 16158 1 15906 1 10424 1 1156 1 14658 1 3732 1 11984 1 11834 1 15772 1 4466 1 13299 1 10430 1 6287 1 6035 1 779 1 5790 1 11309 1 7039 1 5913 1 1552 1 9639 1 4200 1 8108 1 14600 1 3504 1 9583 1 3862 2 12006 1 15423 1 8180 1 3265 1 13519 1 12979 1 12718 1 928 1 13779 20 1907 1 10727 1 7473 1 670 1 2466 1 5240 1 13586 1 6925 1 10867 3 11659 1 5243 1 16356 2 4928 2 10324 1 1180 1 3954 1 6562 1 3816 1 15933 2 12795 1 14828 1 2815 1 6801 1 4932 1 9473 1 15241 1 8066 1 7218 1 12342 2 13969 1 7738 1 9203 2 8070 1 3718 1 14626 1 4502 1 5756 1 3531 1 11678 1 13075 1 11002 1 1014 1 8939 1 11680 1 15459 2 13076 1 8559 2 3830 1 8941 1 3900 1 7009 1 13408 1 13736 1 6760 3 16494 1 10144 1 11419 1 7623 1 11287 1 equine 9210 1 7574 1 10635 1 8595 1 12540 1 15047 1 3013 1 5776 1 2560 1 11612 1 9206 1 804 1 willingham 5739 1 8002 1 11406 1 7693 1 12424 1 13360 1 1014 1 14009 1 arithmetic_operation 12730 2 15940 6 8025 1 341 1 16478 1 1565 5 8663 2 4996 1 1490 2 14181 3 11237 1 11728 1 9116 2 6508 2 10410 1 10664 1 13781 5 9365 2 1845 1 3161 1 10018 1 10842 1 590 2 2960 1 6792 1 12578 5 perturbing 9830 1 16151 1 1580 2 9021 1 12249 1 1363 1 5737 1 7635 1 4056 1 1053 1 5129 1 7061 1 6389 1 14284 2 15572 1 8128 1 6853 1 6477 1 1175 1 601 1 nullified 4659 1 12998 1 14994 1 10109 1 6140 1 2263 1 2802 1 13614 1 2277 1 9837 1 7210 1 stoneflies budge 12149 1 8870 1 14978 1 break_down 5183 1 1331 1 7567 1 573 1 14507 1 12231 1 12658 1 14151 1 4982 1 1145 1 5264 1 13525 1 858 1 10792 1 15636 3 2627 1 10543 1 8400 1 6367 1 4129 1 4307 1 6617 1 15048 3 13782 14 13492 1 10867 1 14980 3 7476 1 5648 1 5677 4 5528 1 10195 1 12020 1 4001 1 10067 1 10879 1 10428 1 2159 1 14760 1 9714 1 10363 1 13794 1 9576 1 9473 1 14036 1 11709 2 8026 1 11223 2 7585 1 343 1 14557 1 945 1 779 1 1641 1 13837 1 14624 1 15880 1 3223 1 5722 1 972 1 14525 1 15028 1 10366 1 15918 1 10751 2 10939 1 14145 1 4578 1 12809 1 11139 1 12007 1 7260 1 7122 1 13083 1 10216 4 7393 1 15753 1 1562 3 3571 2 13016 1 wisteria 1560 1 10168 1 5331 1 5455 1 10743 1 9922 1 1755 1 11666 1 1403 1 13970 1 8490 1 15574 1 neo 8020 1 7794 1 12969 1 646 2 4855 1 12266 1 15459 1 16019 3 9519 1 8851 1 1982 2 supersede 13155 1 8879 1 12880 1 12326 1 4263 1 3513 1 7302 1 1301 1 4813 1 8512 1 8458 2 3404 3 16478 2 13783 11 10321 1 15267 1 85 1 6442 1 3490 1 10326 1 2904 1 14658 1 2394 1 1432 2 5088 1 2815 1 15301 2 14270 1 12283 1 12181 3 8529 1 13799 1 7992 2 6601 1 15937 1 4110 1 11228 1 8192 1 5361 7 14908 1 3084 1 5225 1 11956 1 11063 1 15653 1 1014 1 3461 1 14009 1 2857 1 13076 1 13150 1 8180 1 629 1 6483 1 14776 1 7045 1 4232 1 4363 1 11346 1 13382 1 8851 1 9791 1 serially 1565 1 8635 1 4761 1 1137 1 1291 1 11895 1 15728 1 1242 1 9698 1 10197 1 5584 1 14047 1 14911 1 en_masse 4149 1 3660 1 10825 1 8916 1 9833 3 6241 1 9220 1 15077 1 8091 1 6924 1 1242 1 282 1 9761 1 15301 1 2881 1 2921 1 8829 1 14028 1 591 1 ventricose 3346 1 10131 1 7744 1 7882 1 13531 1 langston_hughes 6782 1 10167 1 15174 1 4222 1 6335 1 frame 5517 1 387 3 15254 1 12229 2 7368 1 12231 2 10473 2 2 1 14847 1 1145 1 6016 2 13674 1 13422 2 7505 1 7757 1 11147 1 15898 1 3907 1 3676 3 9697 1 6135 1 7239 1 2045 1 9018 1 6398 1 15901 1 8277 2 2960 1 2961 1 14855 1 3404 1 3914 1 15403 1 5779 1 16004 1 12497 1 5648 1 9704 2 5384 1 5164 1 8891 1 11827 2 7636 1 12764 1 3788 1 2055 1 16 1 13171 1 8408 2 11020 1 11443 1 16015 1 9830 1 4659 1 3685 3 12643 1 6907 1 12645 3 3556 1 14469 1 1282 1 15542 1 4410 1 1042 1 13836 1 7146 1 6910 2 4907 1 9723 1 13840 1 12911 1 11171 2 11572 1 8769 1 11714 1 2577 1 13443 1 7391 1 4542 1 7256 3 8292 1 4200 1 7527 1 9836 1 2857 3 8907 1 2982 1 2214 1 9971 2 13578 2 280 1 15423 1 1053 1 9586 1 1292 1 5404 1 8298 1 13451 2 16408 1 1562 1 10656 1 9047 1 14226 1 421 1 4426 1 13314 4 10380 3 14087 1 7912 3 9323 2 12662 1 3948 1 8052 1 2598 2 10506 1 10119 1 10381 1 8309 1 7274 5 5805 1 3703 3 13461 2 4921 1 11865 1 13712 1 2873 1 15293 2 44 1 7541 1 4925 1 3136 1 4680 1 6437 1 14352 1 12274 1 12669 1 7055 2 6310 1 10671 1 9461 1 13718 2 1180 1 10128 1 5424 1 10392 1 7799 1 10130 1 15572 1 1846 1 10131 1 556 1 11474 4 10678 1 800 1 8803 1 7421 1 12027 1 2091 1 9996 1 1435 1 8439 1 3145 1 4692 1 5814 1 12800 1 62 1 14246 1 10685 1 4446 1 14911 1 9077 1 15940 1 16200 1 15684 1 2760 1 13076 1 10141 1 6574 1 3971 2 13481 1 8449 1 3831 9 14776 1 8817 2 10018 1 11612 2 2109 1 7068 1 12297 1 1331 3 4456 1 10824 1 10535 1 15692 3 4335 1 16448 1 704 1 12556 1 13611 1 8200 1 5318 1 12167 1 16078 1 1869 1 4588 1 6080 4 6828 1 11756 1 10279 1 2623 1 6463 1 14644 1 3030 3 2625 1 5319 1 6830 3 3320 1 2260 1 6965 2 4129 7 1086 1 12301 1 2262 1 12045 1 8702 1 7946 1 6713 1 2263 1 11357 1 16328 1 12430 1 3483 1 5214 1 14651 1 6967 2 4720 2 5326 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8869 2 13801 2 6629 1 4762 1 7224 1 3376 1 13006 1 12094 1 9415 1 4998 1 4040 5 1251 1 9550 3 1014 1 11002 1 16491 1 2546 1 1383 1 3767 1 7744 1 13408 2 8502 1 4172 8 16494 2 7120 1 142 1 12487 1 2953 1 5634 1 14044 1 15518 1 13411 1 12353 2 12620 1 14047 1 14972 1 1020 1 6894 1 6644 2 channel 1660 4 11141 1 14055 3 14056 2 15636 2 12754 1 1663 1 3099 1 5380 21 2836 1 12889 1 5648 1 3785 1 11 1 9705 2 8517 1 14581 1 7882 1 10769 2 10358 1 5271 1 7133 1 2052 1 14584 1 6145 1 14585 2 768 1 8021 1 10771 1 1668 1 8408 3 9830 1 15772 1 13034 1 15539 1 12123 2 10628 4 13035 7 8763 1 6287 1 3418 1 779 1 14864 1 12243 1 1549 1 10638 1 12911 1 3263 1 4078 1 16399 2 8292 1 11999 1 15419 1 8040 1 2857 1 8907 1 13700 1 3695 1 7042 1 659 1 5033 1 4082 1 3128 1 9587 1 10240 1 2220 1 927 1 12393 1 13054 1 11859 2 10380 1 166 1 2597 1 1836 1 1568 1 1300 1 7158 3 11728 1 6171 1 13063 1 14885 1 11046 1 293 7 673 1 7789 12 16047 1 1309 1 6311 1 13718 3 3815 1 16420 1 14354 1 6933 1 5424 2 7796 2 9860 1 15572 1 7800 2 7169 1 11474 2 11601 1 10809 2 10398 1 3717 1 9342 1 15021 1 9475 2 5432 1 8675 1 4696 1 6691 1 8072 1 3719 1 4114 1 4446 2 2882 1 9077 1 4829 3 11062 1 8076 1 3156 1 14103 1 812 1 11746 1 15579 1 10823 1 698 1 5694 2 14379 1 2488 1 10692 1 5442 2 3310 2 323 1 10694 3 15467 1 3164 5 2114 2 16321 5 3166 1 12815 1 4950 1 16078 1 6080 2 3985 1 13490 1 6830 4 14646 1 6211 1 11493 1 7440 1 1999 2 1730 5 15049 1 6592 1 6212 1 3992 1 5708 1 85 1 13749 1 1339 2 8459 1 3617 2 4463 3 15332 1 13993 1 2276 1 14798 1 13365 1 10430 1 10556 2 3738 2 12181 5 2911 1 8099 1 14664 1 5469 1 4472 1 12705 1 13886 1 16225 2 2284 1 4854 1 11924 1 5722 1 15218 1 7319 1 1604 1 972 3 10302 1 8367 1 12446 1 2404 2 2135 1 5228 4 11646 1 3744 2 14145 1 4610 2 7091 1 1107 1 3057 1 349 1 11939 1 4865 1 10182 1 9527 1 5733 2 5610 1 1113 1 16471 1 12077 1 7850 1 16116 1 2658 1 14026 4 12720 1 5347 1 11123 2 7856 1 6368 1 6494 1 1233 1 7341 1 8383 3 13784 81 10868 1 6498 1 13260 2 11404 1 16355 1 5743 1 10325 2 14548 1 2306 2 9402 1 5131 2 1773 1 3521 2 13264 2 7486 1 14956 2 1635 1 9405 1 505 3 1246 1 15241 1 2165 1 6750 1 1782 1 8254 1 2419 1 5505 1 1507 1 10335 2 13405 1 3376 2 624 1 5756 2 3084 3 136 1 12744 1 7998 1 10751 2 756 2 7360 1 7009 1 8000 1 4507 1 1260 1 14042 1 16494 3 7012 1 14969 1 2953 1 7623 1 1022 1 wetland 9681 1 10704 1 14173 1 6204 1 12467 1 6890 1 3258 1 12326 1 6017 1 13606 1 10539 1 834 1 11292 1 3384 1 9612 1 5347 1 988 1 12939 1 7485 1 1403 1 6708 1 smothered 4659 1 3112 2 6549 1 12229 1 4293 1 2019 1 13900 1 10813 1 10493 2 13797 1 16114 1 9745 1 4263 1 4471 4 9775 1 4539 1 7572 1 14002 1 16061 1 14196 1 3875 2 16436 2 8012 1 8939 1 16008 1 10176 1 13786 8 1789 1 1288 1 67 2 6542 1 2902 1 14297 1 12795 1 6114 1 10533 1 15317 1 7984 1 pliers 6230 1 7368 1 3156 1 13366 1 14935 1 15230 1 7344 1 1616 1 11349 1 658 1 13787 8 14725 1 10840 1 14267 1 13013 1 7120 1 8449 1 12978 1 15933 1 12723 2 1163 1 11312 2 536 1 2425 1 glazed 5442 1 1170 1 7752 1 1112 1 4017 1 3750 1 4393 1 14572 1 5071 1 732 1 2020 1 1334 1 6337 1 10758 1 11349 1 7937 2 3983 2 12785 2 15759 1 7505 1 7158 1 5773 1 3676 1 1965 1 13526 1 3875 3 4129 1 10798 1 2662 4 3403 2 6494 1 2873 2 11593 1 14124 2 14980 1 9800 2 5083 6 12430 1 11403 1 12995 1 13789 25 9739 2 9709 1 14655 1 3816 1 5681 1 8210 1 13330 1 1596 2 2394 1 9507 1 12054 1 9742 1 5995 1 11474 1 15574 2 13002 1 7707 1 7421 4 9714 1 3685 1 9715 1 3793 2 12643 1 4692 1 11506 1 777 1 9998 1 7549 2 7423 1 7587 1 8364 4 6509 1 7957 1 7616 1 8192 1 1130 1 972 2 14910 1 877 1 11572 4 12578 1 1350 1 11842 3 5226 1 11375 2 6788 1 4358 2 13277 2 11746 5 12540 1 7360 1 10569 1 11811 1 7872 1 12809 3 13845 1 7120 1 6762 2 14021 1 6264 1 perceptual 4740 2 8426 1 219 2 16265 1 12814 1 13525 1 13552 1 10351 1 12815 1 3172 1 4782 1 14883 2 8794 1 15929 1 11400 1 5214 1 6241 1 6929 1 10513 1 469 2 13788 11 14165 1 4790 1 15871 1 996 1 5745 1 6969 1 14391 1 2088 1 15805 1 7800 1 11985 1 11602 1 5718 1 6446 1 12439 1 8531 1 11308 2 532 1 2240 1 9579 1 12741 1 15339 3 4907 1 5599 1 14626 1 8769 1 8292 2 9780 2 3741 1 5475 1 16491 1 11136 1 6788 1 7649 1 3630 1 12159 1 13082 1 8004 1 14044 1 16178 1 12353 1 12228 1 6853 1 9920 1 tulipwood 12822 1 5295 1 12811 1 15047 1 16350 1 1665 2 11058 1 arak 13082 1 9831 1 3344 1 11919 1 10573 1 11282 1 6043 1 11777 1 15602 1 5228 1 12959 1 5307 1 15342 1 decent 2462 1 9495 1 6896 1 9359 1 3094 1 4155 1 733 2 12659 3 14024 1 3166 1 3393 1 2863 1 606 1 12041 1 2658 1 6080 1 15729 1 6134 1 5520 1 3320 1 8512 1 5190 1 13712 1 7667 3 6530 1 4680 1 6495 1 3103 1 1148 1 14160 1 8314 1 9928 1 14651 1 12995 1 1063 1 12993 1 14463 1 1090 1 13790 12 12637 1 7481 1 396 1 16053 1 13791 1 2846 1 126 1 15984 1 5628 1 12796 1 12569 1 1596 3 13171 1 4565 2 2447 1 4164 1 12240 1 15987 2 11951 1 10519 1 5430 1 3924 1 8900 1 14244 1 1949 1 4734 1 16338 1 11309 1 16096 1 12611 1 4446 1 2426 1 2404 1 14078 2 4041 1 3741 1 484 1 10465 1 14565 1 5662 1 2247 2 7009 1 1259 1 1516 1 8265 1 8081 1 4707 4 13182 1 598 1 7464 1 570 1 occidental 16315 1 2921 1 1475 1 11094 1 717 1 12848 1 13777 1 radiosensitive 7421 2 4933 1 5547 1 5033 1 15744 3 9810 2 9728 1 6020 2 8523 2 3223 1 11080 1 1140 1 6172 1 murky 6401 1 3310 1 16264 1 10712 1 13909 2 9219 2 9087 2 7762 1 14555 1 12622 1 676 2 16386 1 442 2 6953 2 10298 2 1825 1 8526 2 6419 2 petered 6520 1 14929 1 1309 1 4761 1 16326 1 clothed 9755 1 11966 1 14314 1 6198 1 7068 2 15817 1 11008 1 323 1 9359 1 11349 1 6339 1 4715 1 4185 5 4338 2 11081 1 16002 1 12822 1 2331 2 8012 1 8205 1 12893 1 5214 1 8162 1 3492 1 6281 1 1818 1 2335 2 10094 1 11021 1 6470 2 7952 1 9902 1 15840 2 15591 1 2722 1 837 1 4352 1 14212 2 13111 1 16224 2 5029 1 4540 1 14407 1 9173 1 7257 1 14010 1 15221 1 1893 1 16292 2 3429 1 3569 1 14083 1 2797 2 9918 1 599 1 7776 1 9731 1 8373 1 1753 4 13454 1 3750 1 2596 1 14676 1 5556 1 12326 1 1836 1 12786 1 5188 1 15792 1 7662 1 3277 1 170 1 1909 1 12590 1 3875 2 12268 1 5190 1 13712 2 6678 2 14424 1 13261 1 11129 1 13790 1 13791 21 15299 2 997 1 12023 1 6876 1 6801 1 8438 3 800 1 12731 1 5196 2 8319 1 1075 1 5049 1 10136 1 10742 1 15110 1 8738 1 5570 2 953 1 4698 1 8935 1 15310 4 14772 1 10599 1 16258 1 5257 1 11279 1 8265 1 8817 2 6072 2 6264 1 herbert 763 1 906 1 9086 1 11760 1 1148 1 219 1 6531 1 7249 1 3967 1 8796 1 7990 2 3862 1 2188 1 5412 1 7550 1 9506 1 6668 1 1055 1 11612 1 white_perch 7457 1 3954 1 4255 1 12123 1 11259 1 3992 1 1264 1 break_into 10415 1 14255 1 15948 1 4806 1 13129 1 11349 1 14949 1 12041 1 8787 1 1059 1 3248 4 4744 1 12590 1 6367 1 2997 1 9153 1 14855 6 14650 1 13908 1 14819 1 16418 1 13392 1 6498 2 5457 1 12563 1 4216 1 2631 1 11090 1 1242 1 8352 1 15572 1 7139 1 10961 1 13792 26 5995 1 14956 1 13002 1 1211 1 12471 1 1435 1 8554 1 3556 3 10265 3 5505 1 4965 1 3418 1 7423 1 12220 1 6630 3 9167 2 8807 1 14214 3 2755 1 13301 1 8744 1 16200 1 12410 1 2245 1 10339 2 4938 1 16065 1 4169 1 14810 2 2581 1 9310 1 7260 1 6043 3 2918 1 7393 1 4233 1 1562 1 14047 1 13519 3 neural_network 6766 1 16416 1 3617 1 10867 1 918 1 7497 1 3223 1 15378 1 13821 1 6264 1 muscle_fiber 9021 1 2245 1 13416 1 14746 1 213 1 469 1 15378 1 2968 1 7302 1 15636 1 14147 1 13099 1 8524 1 5616 1 4747 1 11443 1 16327 1 platinum 10691 1 4709 1 15430 1 12231 3 9388 1 9922 1 11349 1 4261 3 15130 1 389 1 15470 3 12145 1 10915 1 15043 1 13583 2 4185 1 16236 1 2873 5 1088 1 3992 5 7189 1 14092 1 13392 5 5742 10 6498 9 15055 1 11327 4 8666 10 13263 1 6244 1 11054 1 4220 1 12599 3 9467 2 15572 1 4534 1 15875 1 4464 5 14095 1 13793 30 8409 2 14956 1 3793 1 9097 2 1469 1 10430 3 1213 1 7833 2 10559 1 7032 2 3189 1 10775 1 11133 1 6910 2 15880 1 8643 1 14436 1 13273 1 11171 1 12943 2 11999 2 8939 1 9551 2 10970 1 10369 3 4417 1 3227 1 4911 2 14374 4 7360 2 14221 1 2648 1 659 2 9618 1 11380 1 1389 1 wartlike 10282 2 6502 1 732 1 9328 1 76 1 3013 1 311 1 3134 1 4143 1 8180 1 10128 1 1420 1 7994 1 6396 1 13591 1 3875 1 11262 1 eskalith 11998 2 13177 1 15918 1 2115 1 531 2 14251 1 8210 1 15251 1 4220 1 6536 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13735 1 4889 1 12617 1 6389 2 8746 1 16316 2 7750 1 13870 12 12620 1 11612 1 12657 2 opportune 3749 1 400 1 15797 1 9021 4 1014 2 4041 2 14913 1 11137 1 8869 6 2967 1 6972 2 15898 1 3771 1 9698 2 15720 2 9377 3 15339 1 9394 1 6520 1 14626 1 4211 1 12071 2 13871 9 vapidly 15587 1 16385 1 11453 1 dogged 3163 1 11289 1 8834 1 5214 1 3886 1 14363 1 14088 1 6988 1 14812 1 11776 1 6500 2 8615 1 1604 1 13598 1 8769 1 16144 1 whaling_ship 1252 1 6792 1 6643 1 4758 1 3223 1 unman 6497 1 219 1 5995 1 descendant 5580 1 13872 9 8594 1 1755 1 9114 1 4487 1 14055 1 7210 1 4893 1 15232 1 15695 1 2865 1 11147 3 7239 1 5990 1 11620 1 4716 1 13712 1 13587 1 11 1 5496 1 2901 1 14616 1 16244 1 8021 1 12795 1 2005 1 6146 2 13206 1 8128 1 15573 1 5714 1 1576 1 9340 1 4468 1 6117 1 620 1 9345 1 3822 1 4539 1 4110 1 14694 1 4907 1 5361 1 9747 1 1982 1 13009 1 5689 2 7526 1 3692 1 185 1 6423 2 8875 1 8942 1 10271 1 14376 1 1583 1 7120 1 3266 1 31 1 9383 1 11612 1 peeled 16429 1 2386 1 8153 1 13626 2 13873 6 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13531 1 4132 1 13859 1 1205 1 9532 1 4269 1 2083 3 6499 1 10389 1 10546 1 7167 1 469 1 16051 1 5527 1 2603 1 7636 1 3881 1 1466 1 172 1 298 1 6178 1 3920 1 3076 1 2715 1 7421 1 869 1 13034 1 6180 1 9067 1 531 1 2312 1 14212 1 7770 1 10001 1 16196 1 1284 1 11171 1 11886 1 11572 1 13920 12 15686 1 2012 2 12192 1 10465 1 2858 1 13893 1 157 2 2214 1 5363 1 15161 1 6261 1 15687 2 4707 1 8651 1 10018 1 9356 1 9321 1 12620 2 11940 1 9920 1 nominated 8954 1 16117 2 6166 1 10594 1 9203 1 4143 1 7158 1 16168 1 12156 1 10744 1 723 1 14290 1 13338 1 12757 1 3283 1 2665 1 10255 1 4169 1 7056 1 6241 1 16356 1 7516 1 6715 1 638 1 14969 1 1450 1 15225 1 8197 1 12352 1 14946 1 footman 2190 1 8632 1 english_person 11059 1 13076 1 atomic_bomb 11163 1 505 1 4728 1 12123 1 12644 1 6131 1 8868 1 13020 1 328 1 559 1 547 1 13422 1 12705 1 8881 1 11728 1 1907 1 10335 1 15339 1 11508 1 12609 1 293 1 16436 1 16398 1 9305 1 4200 2 2263 2 14888 1 16100 2 9247 1 13921 7 1134 1 7898 1 5228 2 16494 2 8113 1 1520 1 10701 2 10928 1 8724 1 6801 1 14956 2 catechistic 7750 1 12856 1 tineidae 15110 1 12044 1 1457 1 sequentially 1320 1 505 1 15665 1 5494 1 13314 1 5417 1 9025 1 13578 1 897 1 8246 1 11147 1 12567 2 14969 1 10901 1 13214 1 13461 1 7169 1 5888 1 7856 1 12228 1 slider 9501 1 3258 1 amphibious 8492 4 11966 1 3253 1 11670 1 3350 5 10983 1 5897 1 6502 1 5373 1 8595 1 9115 1 12109 1 4335 1 4352 1 4093 1 4304 1 7571 1 8881 1 10077 2 607 2 11972 1 8903 5 8512 5 5873 4 6977 1 16478 1 10029 1 9210 1 11034 1 11496 1 14065 1 100 1 12467 1 6997 1 16158 4 13496 4 16207 1 13922 27 14235 1 7329 1 3361 1 9875 1 12066 1 7120 1 2306 1 15206 1 12812 1 4000 1 11686 1 16423 1 1685 1 11346 1 6580 1 11612 4 14189 1 6487 2 13312 1 7350 1 food_turner 2089 1 6238 1 14718 1 1596 1 14189 1 5271 1 15342 1 67 1 marbles 12027 1 8292 1 5705 1 12718 1 12029 1 4998 1 11200 1 11233 1 5610 1 6204 1 6727 1 7370 1 14163 1 8246 2 2902 2 11227 1 12795 1 13432 1 8393 1 11766 1 7799 1 6283 1 10018 2 11080 1 14270 2 10068 1 9779 1 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4343 1 8890 2 14929 1 8281 2 469 1 6090 1 834 2 3615 1 11827 1 7636 1 3788 1 14585 1 1667 1 2335 1 6535 1 771 1 15840 1 8582 1 94 1 10706 1 14469 1 1472 1 1949 1 14470 1 7836 1 3501 1 536 1 7256 1 6230 1 2857 1 9523 1 11646 1 10970 1 14871 1 15422 2 5033 1 11647 1 14281 1 15920 1 15004 1 7463 1 3435 1 5180 1 6546 1 9920 1 12718 2 14606 1 8853 1 5412 1 9259 1 1300 3 6238 1 3356 1 5616 10 13063 1 7541 2 5618 1 2533 3 6740 2 9800 1 10923 1 14160 1 6497 1 15736 3 7344 4 5500 1 5681 1 998 2 9127 3 13264 1 7285 1 7421 1 14172 26 3219 1 4031 1 2423 1 12740 1 7358 1 9546 1 11677 1 1716 3 12943 1 10463 1 9550 4 8683 1 565 1 16068 1 11278 1 14044 1 5694 1 16318 1 peddling 9429 1 5184 1 6059 1 10628 1 3145 1 10380 1 13799 1 531 1 1989 1 9070 1 12818 1 6774 1 6080 1 3729 1 2694 1 10027 1 9844 2 lyric 7398 1 5671 1 15430 1 288 11 3094 1 1992 1 3133 1 12041 1 13458 1 12720 1 7605 1 8201 1 3676 1 8790 1 9260 1 11796 3 9563 1 2079 1 12360 1 3582 3 14232 1 16216 1 7304 1 8084 1 3612 1 2490 1 12270 1 4925 1 5128 1 9892 1 6214 2 3483 4 8965 2 8517 1 468 1 5244 1 10513 2 14236 1 5528 1 13648 2 11090 1 12674 1 1208 2 11917 2 5167 2 15877 1 6801 1 12090 1 505 1 3329 1 3041 1 3145 2 10265 1 13037 1 14174 30 4761 5 15912 1 13110 3 15113 1 13336 1 6630 3 5221 1 4110 1 9413 1 11924 3 14140 1 13273 1 6121 1 12970 1 10336 1 7203 1 11482 1 10339 1 2646 1 9210 1 8040 2 7178 2 15459 1 11646 2 15717 1 3628 1 14527 5 2950 7 9585 1 6328 1 10376 1 1328 1 13124 2 6070 3 8877 1 1685 2 4235 1 lumbering 9501 1 9527 1 3350 1 10564 1 12231 1 7299 1 3744 1 13975 1 7585 1 1288 1 13641 1 1324 1 15077 1 10056 1 5538 1 101 1 15313 2 3789 1 3477 1 13395 1 3676 1 3723 1 14587 1 4113 1 7314 1 1296 1 petit_jury 11147 1 urbanized 2384 1 14514 1 4519 1 2263 1 16182 1 2070 1 3636 1 5228 1 7793 1 12848 1 9165 3 8738 1 13674 1 3952 1 13395 1 1055 1 3596 1 1685 1 9920 1 ansaid 11851 1 5878 1 688 1 531 1 14025 1 9493 1 naturalness 6630 1 15191 2 2023 1 10430 4 14018 2 6029 1 6367 1 9093 2 2171 1 4495 2 1595 3 12383 2 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2 4031 1 1709 2 11413 1 14175 25 5754 1 8259 3 5361 1 4698 1 12537 1 6888 1 7360 1 5364 1 311 1 7010 1 16314 1 9945 1 10756 1 4511 1 3160 1 11427 3 plagiarized 4770 2 2447 2 14585 2 5786 2 15933 2 14864 2 12255 2 falsify 763 2 4660 1 15479 1 8625 2 15910 2 5165 1 2273 2 7372 1 15637 1 14864 2 15805 1 7426 1 14658 1 14176 11 1656 2 11974 2 arjuna 906 1 2974 1 2959 1 2326 1 6630 1 4041 1 12834 1 12158 1 1055 1 9291 1 468 1 6825 1 14911 1 14527 1 3642 1 13674 1 3568 1 shell_game 8156 1 3461 1 11081 1 16300 1 10821 1 13626 1 prophesy 905 1 12726 1 7569 1 1766 1 5901 1 14177 5 383 1 1313 1 5439 1 13011 1 13552 1 4761 1 2950 1 2326 3 10279 1 1653 1 1262 1 3676 1 11796 1 1871 1 12620 1 3458 1 aides 1517 2 5498 1 6635 1 12471 1 1642 1 15191 1 10846 1 4886 1 12954 1 846 1 5312 1 12907 1 6087 1 6426 1 16117 1 11791 1 furan 6467 1 2625 1 6731 1 10 1 9219 1 5104 1 11425 1 7913 1 13892 1 7939 1 9301 1 unduly 2945 1 5198 1 11781 1 15559 3 866 1 11753 1 2299 1 10600 1 9717 1 6585 1 14178 8 16191 1 11784 1 16106 1 3064 1 15759 1 9080 1 7120 1 7121 1 2354 3 5309 1 6390 1 2445 1 1140 1 7509 1 gourd 5906 2 4933 1 557 1 16350 2 6378 1 9635 1 3371 1 8236 1 2128 1 5173 1 7471 1 14196 1 15939 1 3501 1 11744 1 2873 1 11956 1 8012 1 12682 1 9056 2 9210 2 16438 2 15267 1 6574 1 14179 11 11647 1 3760 1 5257 1 7904 6 10962 1 4673 1 6801 1 9047 2 8581 2 11474 1 poetic_rhythm 9415 1 3951 1 8997 1 6498 1 15812 1 12674 1 15987 1 chemical_change 15499 2 15818 1 16182 1 10453 3 8566 1 15089 1 4053 1 13021 1 15636 2 12145 1 15880 1 2045 1 9017 1 10798 1 14909 2 6655 1 3859 1 10421 1 10867 1 9021 2 4041 1 4530 1 16438 1 15441 1 14651 1 1064 1 14180 5 5729 1 1055 1 30 1 15645 1 14956 1 calcify 3300 1 9550 2 4591 1 12472 1 10556 2 5505 3 6552 3 10906 1 4031 1 5729 1 14182 10 1042 1 15636 1 3972 2 779 1 7120 1 4951 1 11178 1 7239 1 2089 1 12214 1 7473 1 11807 1 16327 1 mario_vargas_llosa 9860 1 5393 1 6667 1 9478 1 11748 1 9882 1 866 1 1947 1 retardation 10195 2 450 1 5443 1 9601 1 9112 1 5128 1 14495 1 schematize 152 1 2176 1 15430 1 7536 1 5695 1 14215 1 8529 1 6349 1 389 1 7133 1 hearth 15499 1 5116 1 7365 1 12229 1 14507 1 12231 1 15692 1 15561 1 8117 1 10223 1 1836 2 15759 1 8788 1 3169 1 11394 2 1028 1 11796 1 9655 1 3320 3 13583 2 4921 2 4433 1 2263 1 14980 2 15802 1 676 2 11662 2 10514 1 4062 1 6442 1 12020 1 13649 2 2636 2 13264 1 10491 1 4464 3 3329 1 10965 1 8582 1 7142 1 14555 2 7585 1 11637 1 12031 1 2060 1 13801 3 562 1 6753 2 13176 2 13837 1 3223 1 11924 1 3596 1 15916 1 1982 1 7256 1 13545 2 15940 1 5757 1 16491 1 11890 1 213 10 1893 1 6981 1 1324 1 8421 1 6849 2 14183 17 7012 1 15992 4 13982 1 8269 1 13286 1 12136 1 8948 1 oppose 5442 3 7069 3 13158 2 252 1 3096 2 13551 1 6824 1 12556 1 7302 1 15636 1 14642 1 11757 1 2833 1 10899 1 6400 1 12630 1 6530 2 3404 2 3679 2 6024 1 9704 2 7378 1 14323 1 4342 1 6279 1 1151 1 5271 1 7133 1 11826 1 1666 1 5528 1 6404 1 6969 1 10770 1 4534 1 6536 1 3852 1 4844 2 3038 1 13938 2 16332 1 8284 1 15704 1 10363 1 1040 1 9511 1 11709 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9920 4 1903 1 8234 3 13521 1 219 1 5120 1 12586 1 2300 2 4487 1 8853 1 14025 1 12848 2 9259 1 15729 1 3354 2 5347 1 15601 1 9656 1 3356 1 7473 1 8603 1 5616 2 6367 1 4157 1 15092 1 5240 2 10867 3 11400 2 5878 2 6740 1 11401 1 2534 1 2152 1 9532 1 6497 1 15979 1 7344 4 11406 1 14548 1 15512 1 230 1 10992 1 998 2 6876 1 11804 1 868 1 7486 1 9665 1 14956 3 1004 1 12732 1 3218 2 10202 1 6627 1 12341 5 12474 1 9670 1 14557 1 2312 1 876 1 15114 1 8739 1 8258 1 1249 2 3223 3 7358 1 508 1 2822 1 9546 2 11677 1 12346 1 5361 1 8616 1 1787 1 13918 1 7496 1 4275 1 9550 2 3661 1 14699 1 7230 1 7359 1 13150 1 4169 1 5762 2 4170 3 1136 1 4172 1 16494 2 6516 1 5366 2 4772 1 16495 1 11814 1 14969 1 6389 2 4512 1 3386 5 11686 1 5766 4 12353 1 13286 1 6264 2 4390 1 gastrocybe 16047 1 2152 1 1961 1 9477 1 5358 1 11343 1 1624 4 14634 1 12849 2 1428 1 7120 1 5849 1 1818 1 5449 1 13965 1 8661 1 9172 1 7743 1 8056 1 xerophytic 5906 1 8319 1 8879 1 10983 1 15241 1 10364 2 4238 1 9635 2 14606 1 2972 1 12882 1 1161 1 7210 1 12968 2 7694 1 16411 1 133 1 14196 2 1510 1 7321 1 9103 2 5361 1 14562 1 3444 1 15219 1 5455 1 10566 1 16313 1 14501 11 2613 1 6345 1 3487 2 3760 2 13878 1 13608 2 6535 2 13950 1 9216 1 3699 3 5273 2 capitalism 16110 1 7069 1 13668 1 4332 1 927 2 10453 1 6301 1 5836 1 16117 1 5412 1 3753 1 12145 1 1526 1 3907 1 6081 1 10119 2 5700 2 4745 2 257 1 10899 1 610 1 2438 1 8312 1 121 1 10867 1 7729 1 3483 1 6498 2 265 2 15441 1 8631 1 9089 1 5328 1 12856 1 9738 1 12962 1 16420 1 3493 1 52 1 15447 2 11917 1 15301 1 7169 1 11920 1 8833 1 11223 1 5718 1 4441 1 10884 1 8764 1 10814 1 10137 1 7453 1 9203 1 7222 1 3500 2 14728 2 7389 1 8323 2 13576 1 2377 1 7226 1 8769 1 1982 1 3531 1 2945 1 2404 1 6230 1 13075 1 8040 1 8684 1 5279 1 13079 2 2581 2 14502 13 11381 1 413 1 8004 1 5366 1 15191 1 1055 2 11686 1 6671 4 12620 1 7966 1 11939 1 fecund 813 1 9670 1 16191 1 14716 1 12288 1 trefoil 7 1 1737 1 13583 1 5246 1 8047 3 ablative 9533 1 11684 1 15875 1 15827 1 14956 1 501 1 precook 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6382 1 5754 1 3764 1 9546 1 12608 1 14626 1 10745 1 1982 1 10751 1 1855 1 10211 1 813 1 6890 1 2691 1 2183 1 9081 1 4327 1 6070 1 14047 1 1863 1 perambulating 5089 1 14154 1 reopen 10262 1 9509 1 905 1 387 1 5171 1 13486 1 9735 1 4155 1 7522 1 8156 1 13674 1 3701 1 16078 1 13322 1 3320 1 7389 1 15762 1 8055 1 379 1 13711 1 6655 1 14232 1 11108 1 10868 1 6531 1 10465 1 11512 1 8208 1 14702 1 13752 3 12569 1 15164 1 heavy_cream 9162 1 11280 1 12780 1 10375 1 1238 1 13995 1 3112 1 16213 1 8668 1 10924 1 15089 1 angle_of_view 4933 1 5443 1 11932 1 8848 1 1134 1 8523 1 8330 1 6112 1 12815 1 buttercup 1487 3 4332 1 2236 1 9922 1 1755 1 4238 1 9186 1 4730 1 14173 2 6287 1 4263 5 7711 1 2436 4 14002 1 14196 1 2836 1 6708 1 10300 1 409 1 7085 1 11956 1 8458 1 8886 1 11574 1 5083 1 6681 2 3744 1 6890 2 9008 1 3760 2 9587 1 2904 1 10079 1 14932 8 9860 1 15753 1 1685 1 2921 2 16163 1 14189 1 11474 1 7350 4 byelorussia 11517 1 10825 1 1755 1 1084 1 8889 1 12181 1 14998 1 14546 1 8043 1 10325 1 13380 1 7838 1 9420 1 1685 1 2560 1 2921 1 16373 1 4829 1 juicy 16075 1 15192 2 4332 1 15037 1 9359 1 10473 1 6585 3 8452 1 6132 1 13086 1 7302 1 4780 1 14712 1 5319 1 7239 1 11436 1 14196 2 2836 1 10482 1 2262 1 6022 2 9955 1 8458 3 3032 1 9703 1 3176 1 5455 1 3992 1 15267 2 8517 1 3487 1 7636 3 1155 1 769 2 5849 1 1734 1 2198 1 14520 1 10962 7 12311 1 14933 33 7384 1 8581 3 12964 1 10965 1 10704 1 9635 5 5718 1 3258 2 6287 1 8361 1 10561 2 14137 1 12968 1 719 1 1548 1 11777 1 13576 1 9724 2 9172 1 3501 4 9639 1 13118 1 5662 1 5179 5 13760 2 7457 1 13843 1 1355 1 5608 1 1560 3 8180 1 9587 1 6105 1 13845 1 7904 18 15082 5 598 1 13518 1 9047 8 5733 1 7657 1 1487 1 2989 1 11457 1 13900 1 10310 3 4092 1 9651 1 4263 6 2598 1 5347 1 5126 2 16411 1 6238 1 293 1 931 1 12988 1 2873 1 10253 1 11527 1 4925 1 12466 1 3757 1 10321 3 14352 1 12792 1 1621 1 1624 1 11129 1 5562 1 126 1 447 2 7922 1 12022 1 555 2 7486 1 6801 1 11474 1 14623 1 9343 1 4499 1 10742 10 15376 1 8555 2 8556 1 10334 1 7224 2 2096 1 4698 3 4829 1 15940 1 13867 1 11956 1 15685 1 15459 1 16438 3 10141 2 4169 1 7360 2 2247 2 13279 1 4701 1 6387 1 14838 5 12809 2 5257 1 9945 2 7120 1 68 1 3902 2 4579 1 2043 1 12546 1 883 1 exclusion_principle 4096 1 8845 1 11163 1 9242 1 7729 1 13996 1 8408 1 4014 1 13011 1 rushy 8983 1 15181 1 12738 1 old_man 758 1 10473 1 1027 1 16147 1 13674 1 14576 1 16268 1 11435 1 7239 1 4185 1 15901 1 6655 1 15404 1 10483 1 1148 1 1807 1 5648 2 765 1 3409 2 8517 1 5526 1 8757 1 7636 1 769 1 11830 1 13171 2 1538 1 13567 1 10492 1 400 1 4536 1 15913 1 10775 1 7387 1 2061 1 1042 2 4664 1 4666 1 6415 1 1163 1 8645 2 2978 1 4541 1 536 1 15416 1 11999 1 15419 1 7392 1 157 1 9443 1 1681 1 12383 1 1053 1 6043 3 9046 1 6161 1 3271 1 15561 1 12011 1 1300 2 2598 2 15010 1 4921 1 13588 1 9601 1 12672 1 3813 1 2085 1 5424 1 12795 1 678 1 12279 1 680 1 6444 1 6178 2 15181 1 12936 2 13728 1 15808 1 561 1 12534 1 4110 1 7291 1 9869 1 10004 1 4574 1 6948 1 6325 1 5953 1 3461 1 8325 2 5440 1 6455 1 4706 1 7066 1 13982 3 1198 1 9086 1 10149 1 3312 1 9219 1 10537 1 5836 1 4950 1 6080 1 5699 1 10152 1 6829 1 14121 1 7072 1 10832 1 12302 1 7828 1 5966 1 14650 1 1204 1 11763 1 14128 1 9233 1 7447 1 5846 1 3729 1 10424 1 8972 1 8829 1 3852 1 14934 39 5594 1 4728 1 9511 1 15591 1 10556 1 343 1 11370 1 4143 2 15846 2 2284 1 7956 1 14941 1 12970 1 9779 1 9639 1 11108 1 484 2 212 1 6978 1 13893 1 845 1 3629 2 594 1 15345 1 3344 2 2797 2 1108 1 13518 1 5983 1 3059 1 12718 1 8592 2 12075 1 14412 1 9258 1 15089 1 12848 1 15503 1 6237 4 12723 1 5493 1 14160 1 9532 1 8125 1 227 1 11406 1 10067 1 2814 1 7611 1 15611 1 12731 1 13794 1 7218 1 11674 1 14555 1 2541 2 6118 1 1784 1 13540 1 3654 1 3377 1 9546 1 5887 1 4040 1 6124 1 2320 1 1253 1 7230 2 10465 1 2546 1 8875 2 1259 1 14838 1 3384 3 16370 1 6263 1 11963 1 13412 1 15992 1 883 2 circumscribe shrimp_butter 9916 1 leftist 252 1 13722 1 8021 1 near_thing 1920 1 8410 1 2945 1 8319 1 16065 1 12973 1 15502 1 8155 1 9717 1 14181 1 8137 1 13360 1 14044 1 3248 2 10395 1 quantitative_chemical_analysis 16329 1 14838 1 11013 1 12020 1 motel marginal_utility 4502 1 5212 1 13576 1 7966 1 2051 1 3483 1 abbey 4143 1 1075 1 12727 1 13838 2 10441 1 10356 1 9644 1 notch 5442 1 15889 2 12718 1 8592 1 12393 1 9359 1 6204 1 15230 3 3477 2 7505 1 3320 2 9228 1 13461 2 3703 1 2806 1 2836 1 5618 1 14351 1 6592 1 6438 2 12669 2 11403 1 14462 1 13787 1 7882 1 1595 1 1812 1 9465 1 10128 2 2902 1 14466 1 16484 1 10489 1 173 1 4001 1 9860 2 11474 3 941 1 7421 3 11331 1 12936 1 14935 26 13366 3 15910 1 8135 1 5950 1 14212 3 718 1 13836 1 1163 1 15414 1 13406 1 14961 1 12578 1 8193 1 4998 1 5103 1 14009 1 15887 1 11927 1 13118 2 8980 3 2647 1 6667 1 3668 1 9971 1 13578 1 1104 1 7010 6 784 1 16494 1 6105 1 13013 1 14106 1 14044 1 3939 1 1656 1 16179 1 11427 2 7014 1 16470 1 9920 1 mastodon 16314 2 9648 2 1457 1 9820 2 14626 2 1510 2 5361 2 13712 1 rhizophoraceae 1457 1 9635 1 dialectical 12270 1 15940 1 7723 1 13075 1 252 1 11495 1 8654 1 12856 1 10351 1 14738 1 9535 1 10374 1 50 1 14558 1 3223 1 4813 1 10395 1 hovel 6184 1 10495 1 16225 2 13683 1 8435 3 8817 1 10727 1 14469 1 9971 1 2613 1 convoluted 12813 2 7567 1 1565 1 14675 1 4457 1 9922 1 7851 4 12326 1 2896 1 12849 2 6080 1 4812 1 6239 1 13063 1 4185 2 2836 1 11081 2 7410 1 8057 1 7376 2 10387 1 8243 2 5349 2 1426 6 1624 1 12898 2 12500 1 9535 2 8246 2 1428 1 13648 2 792 2 4310 2 3846 2 16055 1 9664 1 11769 2 13068 2 7421 2 10492 2 1406 1 14936 12 11675 1 12966 2 9135 4 4252 1 15215 1 4143 1 2755 4 6692 1 14525 1 3501 1 6419 1 4698 1 918 2 7230 1 8325 1 9175 2 7649 2 2982 1 5607 2 2214 2 1516 2 1104 1 13736 1 9445 1 12714 1 3158 1 12036 2 11814 4 2407 2 9148 1 10018 1 4634 2 12353 2 10468 2 13706 4 wave_theory 8180 1 529 1 1680 1 7824 1 13064 1 10247 1 4502 1 2882 1 8628 1 5403 1 15089 1 bating 4228 1 984 1 14658 1 2325 1 7532 1 11239 1 8659 1 8073 1 11686 1 hypophysis formative 219 3 4155 2 2525 1 1836 1 14937 13 6828 2 7159 1 4951 3 8258 1 7824 1 7473 1 13576 1 4748 1 3501 1 9521 1 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1 12543 1 14186 1 4327 1 8946 1 2487 1 9216 1 1792 1 ablaut 12943 1 12399 1 16056 1 spearheded 6530 1 12471 1 186 1 8309 1 1501 1 analyzed 7847 2 1866 2 2862 1 9219 1 3164 1 16265 2 12554 1 15899 2 2960 1 14232 2 2532 2 14855 1 4160 3 5160 1 7342 1 2229 2 5810 1 3684 1 13432 2 12795 1 2086 1 8997 1 13171 1 3181 2 3818 1 15741 13 11221 1 10430 1 10811 1 13574 1 11838 1 623 4 9411 1 14864 3 9546 1 2942 2 15940 1 6633 1 12247 1 5475 2 14009 2 11109 1 7178 1 13118 3 14913 1 5439 2 539 1 1934 1 6328 1 11896 1 14969 1 9107 1 5694 1 12953 1 12353 1 14047 3 13482 1 3269 1 speck 13009 1 15772 1 4933 1 13681 2 13356 1 2300 1 10221 2 1290 2 3158 1 13283 1 12795 1 1997 1 11949 1 9860 1 6520 1 10772 1 1730 1 1002 1 mycoplasma 12643 1 3145 1 15576 1 14640 1 14287 1 1963 1 6303 1 9140 1 7537 1 16236 1 16327 1 14500 1 7230 1 4169 1 7360 1 14616 1 5033 1 14066 1 6232 1 13283 1 12308 1 230 1 6389 1 12795 1 4512 1 9383 1 10654 1 3571 1 13223 1 genus_pitta 6927 1 4030 1 13004 1 3636 2 7798 1 6535 1 4352 1 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12207 1 unequivocally 168 1 3022 1 6359 1 12249 1 9546 1 7751 1 15015 1 11405 1 1886 1 indonesia 7015 1 3310 1 6524 1 884 1 3093 1 8156 1 8008 1 14055 1 12815 1 13821 1 13422 2 4521 1 11693 1 2436 1 2623 1 14712 1 9285 1 11619 2 7824 1 14196 1 12959 1 8512 1 2560 2 8458 1 8886 1 9021 1 15404 1 16004 2 151 1 3992 1 15267 1 15136 1 8346 1 14929 1 15138 1 14584 4 768 1 3492 1 13295 6 5849 1 2198 1 9507 1 5088 1 1596 3 5972 1 10701 1 904 1 5169 1 8581 1 6835 1 2971 1 6350 1 9635 1 11100 1 9032 1 2128 1 5173 1 9834 4 13834 1 3189 2 12059 1 717 1 778 1 2726 1 10907 1 2063 1 11228 1 11572 1 11716 2 484 1 8040 1 16100 1 13893 1 5228 4 9524 5 922 2 1893 1 6727 1 11847 1 539 1 15281 1 10372 1 1560 1 159 1 8227 2 8044 1 1685 3 10656 1 14022 1 13312 3 421 1 12521 1 7657 1 5555 1 10983 3 8477 1 1755 1 10660 1 9186 3 5867 1 11859 1 1567 1 5556 2 12326 1 16117 1 12848 1 3134 2 1837 1 5347 2 11941 2 293 2 13712 1 10253 1 2227 1 16478 1 2358 1 7412 1 8484 1 11660 1 6498 1 11255 1 4216 1 14756 1 14235 1 11468 1 15176 1 6442 1 13648 1 13720 1 7922 1 2089 2 7798 1 8997 1 10131 1 799 1 11055 1 14686 1 505 13 13004 1 13799 1 11541 2 1709 1 10461 1 15746 36 10681 1 13071 1 3653 1 10335 1 3764 1 7868 4 3961 1 4829 1 9415 1 16200 1 11002 2 1134 1 15248 1 8875 1 13079 1 10142 1 14966 1 7744 3 12485 1 8502 1 10271 2 6636 1 12811 1 13343 1 1328 1 13981 1 3606 1 14841 2 9420 1 7014 1 15688 1 delilah 6685 1 9116 1 5750 1 4531 1 15034 1 13659 1 11215 1 15175 1 5400 1 bilberries 7289 1 5718 1 13583 1 7360 1 1403 1 10704 1 mrna 15192 1 351 2 12718 1 13849 4 7504 1 1903 1 8951 1 12075 1 6958 1 13676 1 2542 1 1836 1 1949 1 9015 1 5786 1 15729 1 9698 1 3223 1 4210 1 3764 2 8512 1 15964 2 931 1 9153 1 8056 1 7809 1 2426 1 673 1 1014 1 9705 2 656 1 10340 1 8281 1 3863 2 5033 4 5847 1 6442 1 10755 2 9147 1 4421 1 1242 1 4722 2 13665 3 14658 1 12716 2 9664 1 8316 1 12228 1 7262 3 pumped 8726 1 3310 1 9049 1 6898 1 9359 1 984 1 12075 1 5187 1 1963 1 6080 2 7238 1 9188 1 9116 1 11729 1 4951 1 5988 1 10186 1 5964 1 1999 1 2329 1 891 1 3546 1 11400 1 3138 1 6592 1 11403 1 2083 1 11946 1 5969 1 1812 1 1180 1 5811 1 14983 1 14548 1 10901 1 10675 1 14892 1 4991 1 4902 1 8524 1 7799 1 1574 1 7025 1 15572 2 10131 1 10772 1 13830 1 15140 1 13002 1 1635 1 13034 1 557 1 3793 1 14208 1 10432 1 14469 1 7030 1 3258 1 10906 1 8871 1 6185 1 9518 1 4906 1 15880 1 15747 13 5722 1 4446 2 5913 1 15940 1 2012 2 212 1 12380 1 4886 1 7359 2 8939 1 6888 1 10305 1 16068 1 3971 1 11812 1 9587 4 12414 1 8983 1 14969 1 11849 1 3386 1 385 1 11686 1 16370 1 8946 1 5694 7 2797 1 8197 1 10656 1 1389 1 4154 1 10050 1 6792 1 6264 1 13706 1 cakchiquel 12545 1 10070 1 once_again 2332 1 16182 1 12249 1 12592 1 3314 1 11783 1 585 1 3684 1 4540 1 11395 1 15880 1 10153 1 7646 1 12774 1 15748 8 13063 1 10989 1 931 1 cirsium 2291 1 2999 1 8446 1 4698 1 avalanche 4925 1 772 1 1004 1 11941 1 3242 1 16492 1 11196 1 10340 1 7321 1 7169 1 7361 1 14430 1 hunched 9364 1 3532 1 11739 1 passover 2840 2 11064 1 10895 1 13530 1 11152 3 10369 1 1621 1 12011 3 15281 1 12424 1 16494 1 14319 2 8083 1 5298 1 9038 1 1056 1 15749 6 9822 1 15028 1 stapelia 9014 1 16442 1 7574 1 9345 1 12326 1 14055 1 15028 1 5624 1 ur 8704 1 15365 1 7357 1 3483 1 fawn_lily 3760 1 10941 1 2436 1 1738 1 4829 1 unverified 10418 1 1635 1 12480 1 14969 1 529 1 15630 1 15556 1 edict 8680 1 2155 1 6035 1 12021 1 2290 1 7908 1 1611 1 66 1 5810 1 3766 1 15312 1 6206 1 innocuous 1635 1 4660 1 6498 1 8464 2 531 1 1169 1 6817 2 1784 2 6389 2 4707 2 11054 1 9076 1 2234 1 staying_power 761 1 14420 1 thermonuclear 9429 1 10421 1 959 1 15430 2 11035 1 9416 1 16174 1 14152 1 13460 1 2953 1 11017 1 6366 1 471 1 2467 1 179 1 anatolian 6498 1 13648 1 1649 1 multiracial 9203 2 15115 2 8070 2 5187 2 12416 2 6776 2 high_pressure 542 1 13669 1 6899 1 15230 1 12623 1 12848 1 860 1 16078 1 5485 1 6080 1 5347 2 3874 1 14679 1 293 1 10955 2 2873 1 9153 1 10421 2 2263 2 673 1 3992 1 14819 1 9704 3 1811 1 7344 1 14546 1 11217 1 7134 1 16087 1 2306 1 11016 1 12997 1 7417 2 1916 1 10200 1 1369 1 57 1 11307 1 302 1 8637 1 15576 1 11308 1 13111 1 11882 1 7388 1 9869 1 3223 1 15964 1 15028 1 4668 1 11234 1 15491 1 11453 2 13118 1 1134 2 9551 1 4169 2 5858 1 15750 12 213 1 9310 1 9971 1 6542 1 595 2 593 1 11140 1 385 1 11686 1 5694 1 8590 1 2918 1 4515 1 6764 1 6894 2 9920 1 drag_on 15085 1 557 1 14819 1 15311 1 9971 1 442 2 16052 1 587 1 16363 1 7973 1 8678 1 5822 1 6792 1 6894 1 1296 1 teetotal 2535 1 10770 1 10884 1 4155 1 6943 1 11821 1 3699 1 13581 1 mythologist healed 10492 1 10135 1 11772 1 10503 2 631 1 16092 1 13112 3 10681 1 4881 1 13192 2 10418 2 10206 1 9907 1 2868 1 5573 1 13711 1 9079 1 8517 1 13280 1 11327 1 4480 1 3813 1 1812 1 124 1 15751 7 13029 1 13752 2 2548 1 795 1 10490 1 7313 1 7674 2 6375 1 10068 1 sacramento_river 3444 3 11313 1 12284 1 5228 1 6498 1 884 2 13821 2 7528 1 13648 1 7962 1 11147 1 6686 1 11619 1 10635 1 14865 1 8512 1 10070 1 berkeley 6818 1 905 1 1053 1 14225 1 3444 2 13299 1 6450 1 5897 1 1501 1 5228 1 9664 1 1685 1 3174 1 excellent 16261 1 11550 1 12622 1 3396 1 6018 1 7505 1 8749 1 14057 2 3676 1 9285 1 8512 1 2836 1 6021 1 15403 1 5648 1 10 2 8517 1 6026 1 5271 2 8632 1 11155 2 14327 1 2057 2 4659 1 10492 1 12643 1 774 1 8026 1 9717 1 5173 1 5654 2 9300 1 16281 1 2062 1 779 1 4540 1 11309 1 8904 1 11171 1 15916 1 8769 3 14076 1 16398 1 7256 1 13445 1 12247 1 11033 1 1409 1 4909 1 5179 1 7900 1 2214 1 6042 1 3568 1 4912 2 1169 1 1560 1 13702 1 16294 1 7260 1 7904 1 12260 1 32 1 6793 1 3271 2 13314 1 9735 1 7269 1 2222 1 5187 1 11325 1 6433 1 2598 1 15436 1 4678 1 7536 1 13461 2 4211 1 7665 1 6053 1 6925 1 7541 1 10253 1 4680 1 14351 1 8796 2 12792 2 550 1 16049 1 790 1 11192 1 11050 1 4686 1 4437 1 3816 1 15933 2 2089 1 12796 1 174 1 15450 1 7421 1 11875 1 299 1 11602 1 15576 1 11878 1 8928 1 2241 1 4110 1 11742 1 10683 2 14626 1 14101 1 4223 1 11339 1 9206 1 2882 3 13074 1 2759 1 9210 1 7178 1 5311 1 14913 1 813 2 10142 1 10687 1 6574 1 1192 1 8195 1 16314 1 12416 1 8946 1 12687 1 14108 2 5957 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16373 1 3238 1 5142 1 wiggling ignobly 6534 1 10076 1 12643 1 15466 1 3303 1 13418 1 5325 1 9270 1 7314 1 13965 1 15662 1 leakage 4040 1 15884 1 12139 1 10751 1 15430 1 9474 1 1479 1 5173 1 11327 1 15968 1 7134 2 4031 1 2422 2 2902 1 4951 1 1561 1 6830 1 7347 1 5694 1 15754 5 11261 1 13264 1 15028 1 fire_and_brimstone 2326 1 7587 1 13511 1 15034 1 2581 1 8146 1 8038 1 clark_gable 4913 1 16494 1 11227 1 12565 1 9116 1 126 1 13276 1 9650 1 3070 1 4499 1 12995 2 9817 1 launching_pad 15005 1 9533 1 13813 1 anatomize 906 1 16417 1 8309 1 4707 1 7291 1 10216 2 2814 1 15609 1 813 1 536 1 2577 1 landmass 421 1 4623 1 11002 1 13462 2 8663 1 7218 2 9032 1 4335 1 4487 1 4361 1 12083 1 10561 1 3135 1 12102 1 1231 1 14941 1 2920 1 7440 4 2560 1 2921 1 10356 1 13312 1 unfunny 8793 1 622 1 4701 1 2646 1 8662 1 11188 1 cassava 10253 2 8012 1 917 1 15994 1 10965 1 10828 1 16172 1 9635 1 10369 1 1621 1 11256 2 5386 1 5307 2 183 1 14672 2 13845 1 10409 3 10079 1 9285 1 15753 1 1596 2 3699 2 15755 5 8581 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12875 1 10018 1 16318 1 1656 1 15756 27 restock 13434 1 14833 1 6655 1 13888 1 ernie 2797 1 532 1 10447 1 5883 1 16195 1 8292 1 9097 1 7103 1 9971 1 9534 1 8523 1 papua 4877 1 12399 1 13648 1 1755 1 autumn 7015 1 5371 1 11967 2 12231 1 884 1 15757 53 13821 3 1799 2 7238 1 255 1 2436 2 14712 6 3676 1 14196 1 9428 3 4185 3 2560 1 2836 1 4529 3 15404 1 15267 1 9704 2 14718 4 14065 1 14323 1 8519 1 7636 1 5017 1 12120 1 11830 1 3684 1 641 1 1667 1 16389 1 5783 1 6146 1 10094 1 11019 2 11021 1 1403 1 2970 1 11162 2 8285 1 6778 1 3258 14 6287 1 5394 1 13834 1 15150 1 15916 1 8035 1 6419 1 12380 5 6788 2 5179 1 14997 1 8774 2 1560 2 16405 1 159 1 7904 5 414 1 12518 1 7393 1 12519 1 1685 5 6161 1 5181 1 284 1 3436 2 7657 2 12391 2 9186 2 11859 1 9450 1 666 5 12011 1 8787 1 12786 3 3135 1 13583 4 9190 1 293 1 7667 1 7410 1 13712 1 2873 1 11593 1 4433 2 5295 3 16301 1 4926 1 13462 3 6681 3 9328 1 11327 3 4216 1 1428 1 1311 1 14758 1 447 1 16055 4 15448 1 15574 3 15675 1 13726 1 8554 1 3956 1 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1 1836 1 1799 1 12395 1 2598 2 15792 10 9285 1 16411 1 7662 1 1760 2 7574 2 15975 2 12590 1 14091 1 293 1 2465 1 2873 1 8012 1 8458 1 8702 4 10321 1 4786 1 6175 1 3992 1 15267 1 15475 1 11129 2 8162 1 8734 1 3492 1 11092 1 8434 1 13791 1 447 1 2335 2 7485 1 4994 3 6801 2 15449 1 8581 2 7952 1 6377 1 15840 3 9635 4 15576 2 10742 2 719 1 15939 1 9103 1 9004 1 4829 1 4041 2 538 1 13760 1 7900 1 6814 1 12916 1 11203 1 5257 2 12811 2 7904 1 1357 2 15082 1 11489 1 platyhelminthes 16087 1 13609 1 10965 1 185 1 7744 1 15019 1 7486 1 11612 1 3162 1 4698 1 sooner 6896 1 8410 1 5344 2 573 2 7657 1 10703 2 15840 2 2419 1 2114 2 4193 2 6433 2 357 1 14693 1 9413 1 7628 2 15793 7 14626 1 7006 2 4680 2 14009 2 14650 1 6890 1 2214 1 1064 1 11662 1 3034 1 14585 1 11050 1 7460 1 15390 1 4511 2 13467 1 2797 1 5133 2 4565 2 visibly 10033 2 6601 1 6837 1 12192 1 2720 1 15905 1 12975 1 16482 1 13951 1 14911 1 4830 1 5926 1 horrid 8314 2 11744 1 independent_variable 10867 7 7567 3 10698 3 10265 1 8223 1 2581 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573 2 2623 1 13490 1 3726 1 13491 7 7943 2 7440 1 5967 2 3992 1 3483 1 13495 1 1338 1 15330 1 8968 1 15056 1 832 1 5458 1 3729 1 15058 2 13994 1 6970 1 4466 1 7450 1 13505 1 8975 3 15334 1 15063 1 9243 1 7452 1 9773 1 4966 1 14002 1 10846 1 7958 1 590 1 842 1 2403 1 2404 1 2646 1 3741 1 5475 1 14009 1 14810 1 8224 1 11378 3 2648 4 4976 1 9787 2 10047 1 13519 1 11939 1 15084 4 3749 1 10053 1 7209 1 3205 1 1116 1 14026 1 362 2 5240 1 7729 1 2931 1 3210 2 1915 1 6241 1 11662 1 11406 3 10325 2 10590 1 16127 4 2671 1 126 1 12727 1 10068 1 7486 1 1920 1 3218 1 621 1 4759 2 14035 1 5998 1 11674 1 7488 1 7990 1 15616 1 11413 1 2680 1 9546 1 3766 7 2175 1 1931 1 3531 1 2945 1 1649 1 15119 1 2179 3 8264 1 10340 1 14838 1 1650 1 4279 2 11959 1 14570 1 4511 2 3534 1 10343 3 6263 1 11426 1 1656 3 11966 1 4283 1 10606 1 4774 1 11141 1 884 1 10351 1 14851 1 13820 1 4054 1 14319 1 15399 1 6019 1 10087 1 9021 2 1148 1 5271 1 1947 1 14585 3 5528 1 6774 1 13029 1 9572 1 10901 1 902 1 7025 1 16164 1 905 2 10363 1 15646 1 9833 1 6287 2 8029 2 4539 3 406 1 7037 1 647 1 2212 1 11712 2 16170 1 5543 1 1165 1 7526 1 11453 3 14336 1 5031 1 3568 1 14338 1 4546 1 9046 1 2986 1 6547 1 7048 1 5038 1 10658 1 927 1 12011 3 11725 1 15170 1 2225 1 8055 1 1968 2 6053 2 4558 1 3813 1 10926 2 12795 3 15933 1 15445 2 11472 1 12284 1 14622 1 8553 1 11735 1 6318 1 10934 1 945 2 5570 1 4110 2 9350 2 11742 1 14626 1 5573 1 4829 1 8557 1 13338 3 15940 4 13076 1 15459 1 14374 1 2483 1 13606 2 16439 1 5065 1 4118 3 4327 1 699 1 3025 1 6071 1 10945 2 13871 1 7067 1 12813 1 9882 1 14114 1 9359 1 6584 1 6825 1 9886 1 15695 3 11212 1 3839 1 8083 1 14644 1 3320 1 4338 1 7304 2 5841 1 5586 2 2263 1 5084 1 11495 1 961 1 12563 2 3617 1 5590 3 12569 1 2496 1 12570 1 13104 1 10964 1 3329 2 10430 1 10705 1 4348 2 13368 1 1475 3 6351 1 11227 1 5598 1 7587 1 6841 1 15487 1 9377 1 14941 1 8365 1 13116 1 9639 1 5103 1 13633 1 13634 2 211 2 2511 1 11784 1 5608 1 12067 1 6359 1 8845 1 1750 1 13895 2 4153 1 14410 1 16469 1 8371 1 11789 5 8851 1 2016 1 1224 1 8591 1 10453 1 8117 1 4155 2 15502 1 12845 1 6864 1 1229 1 9116 1 15729 1 986 1 6108 1 9394 1 7099 2 1762 3 11250 1 9926 2 5877 1 4617 1 5621 8 2535 1 1502 1 13648 2 5353 1 16484 1 997 3 15740 1 9664 1 8128 1 2036 1 12339 1 3077 2 15987 2 6378 1 10202 1 3886 1 7864 1 13917 2 9937 2 3375 1 9413 1 3377 1 4626 1 1014 5 12097 1 3087 1 15249 1 3382 1 10756 1 12102 5 13924 1 12352 2 6641 1 12877 3 15756 1 11287 1 qindarka 14181 1 erroneous 15428 1 15479 1 4933 1 5671 1 15798 6 14918 1 3242 1 7189 1 8314 1 6929 2 6634 1 15634 1 8180 1 15105 1 13765 1 1838 1 1451 1 2755 1 15921 1 11583 1 7441 1 2490 1 5240 1 willful 13053 1 3672 1 8376 1 9359 1 37 1 2894 1 1027 3 15089 1 12145 1 15525 1 117 1 10418 1 15762 1 7441 1 11150 2 5240 1 5586 2 15799 14 7413 1 11358 1 8796 1 85 1 6771 1 14323 1 7670 1 10874 1 1627 1 12212 1 3617 1 7170 1 5883 1 4466 1 7286 1 10552 1 1004 1 529 1 774 1 3594 1 13795 1 8867 1 6351 1 8901 1 12534 1 1283 1 12969 1 5656 1 11199 2 8192 1 2377 1 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9819 3 8303 1 12392 1 10858 1 10742 1 14690 1 12181 1 1096 1 15888 2 13674 1 1137 2 5828 1 14002 1 13214 3 15445 1 6735 1 2797 1 5888 1 2036 2 luvaridae 1457 1 genus_illicium 14196 1 orudis_kt 14251 1 15251 1 11851 1 14025 1 531 1 shres 4129 1 11895 1 14980 1 1526 1 2709 1 lyrically 12959 1 16024 1 naively 10363 1 11679 1 484 1 12019 1 10689 1 4230 1 7035 2 9789 2 5913 1 3626 1 6053 1 smog 1599 1 5442 1 14314 1 3218 1 6750 1 9717 1 10608 1 9165 1 12041 1 1568 1 10906 1 9775 1 9517 1 15184 1 5137 1 1099 1 2869 1 2730 1 7390 1 5658 1 12822 1 8458 1 9209 1 15765 1 14092 1 7649 2 12397 1 4099 1 12563 1 13514 1 442 1 126 1 11666 1 4534 1 sensory_nerve 4570 1 11504 1 1738 1 8582 1 1438 1 12956 1 3376 1 5616 2 1805 1 10172 1 12943 1 673 1 9801 2 10937 1 8078 1 9532 1 14929 2 12580 2 227 2 5033 2 7636 1 3362 1 6145 1 16175 1 11895 1 2783 1 5563 1 15803 7 13099 1 998 1 2335 4 12353 1 15877 1 11473 1 pianist 3833 1 387 2 6013 1 13486 1 6336 1 3242 1 6204 2 12815 1 1523 1 9221 10 12041 1 2556 1 9821 1 759 1 13422 1 13021 1 11755 1 6080 1 10417 1 11294 1 8824 1 1943 1 6830 1 16080 1 5965 1 13354 1 9021 1 11760 1 7445 1 6214 2 3483 1 8965 1 9764 1 5214 1 578 1 12563 1 6774 1 7135 1 8021 1 13431 1 3848 1 14658 1 2848 2 8829 1 9830 2 7199 1 11704 1 4728 1 5718 1 4661 1 5654 1 3189 1 1096 1 6035 2 14002 1 11450 3 11992 1 11309 1 6416 1 8102 1 536 1 4200 1 5226 1 3741 1 6102 2 8108 1 14600 1 8907 1 4202 1 11646 1 15717 1 3628 1 12713 1 14738 1 10372 1 16105 1 101 1 595 1 15000 1 12067 1 12007 1 1413 1 8845 1 15551 1 5861 1 7260 1 11383 3 14409 1 1054 1 12519 1 5982 1 11789 2 2142 1 2349 1 11182 1 13581 1 13706 1 8231 1 6854 1 5120 1 4426 1 13314 1 12011 2 6300 1 11794 1 9116 1 10664 1 10916 1 7340 1 4749 1 6925 1 11188 1 14422 1 741 1 7729 6 6498 1 13260 2 6997 1 7607 1 2669 3 16356 1 14236 2 6931 1 3815 1 13395 1 3816 1 14892 1 15933 1 15804 36 11803 1 15445 2 9861 1 13000 1 1976 1 9863 1 3076 1 10517 1 7420 1 7985 1 4933 1 1127 1 15183 1 4761 5 13538 2 62 1 8931 1 563 3 2476 1 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3948 1 10664 1 2355 1 8309 1 1421 1 12663 1 14883 1 4813 1 13461 1 15795 1 7787 1 1176 1 2873 1 10253 1 6676 4 4925 7 8662 1 5561 1 3813 2 4928 1 16052 2 10391 1 15933 2 15805 78 7799 1 15572 1 9605 1 15301 1 6444 1 2232 1 556 1 10395 1 13209 3 3145 1 10264 1 7173 1 6446 1 15935 2 3294 2 2609 2 9611 1 7551 1 8556 1 13214 1 14770 1 2755 1 14771 1 14626 6 16436 1 5689 7 13218 1 7559 1 3971 1 9353 1 5828 1 5955 1 8449 1 4707 1 12416 1 5694 1 5317 1 6071 1 10018 1 5578 1 11901 1 14918 2 5072 1 11349 1 12814 1 13610 1 12815 1 8568 1 3726 1 13089 3 6962 1 13228 1 8571 1 7697 1 8203 1 4338 1 1588 1 8084 1 5841 1 2263 1 3992 1 5214 1 5215 4 7192 1 15330 1 8088 1 14931 1 834 1 5458 1 10161 1 9506 2 8209 1 2003 2 4247 1 6969 1 7831 1 14658 1 7451 2 1211 1 15706 2 3853 2 5331 1 13237 3 8463 1 14398 1 13754 4 5717 1 5718 1 15483 1 3189 2 12059 1 8361 1 14664 1 6601 1 1602 1 11227 1 585 1 2504 1 971 1 14941 1 13892 1 14007 1 15965 1 11641 1 5103 1 13633 1 2646 1 6230 1 12972 1 3741 1 8108 1 211 1 8110 1 13512 1 13893 2 14810 1 10970 1 3744 1 846 1 213 2 3198 1 4361 1 12448 1 16104 1 6980 1 4864 1 5608 1 4974 1 13895 2 14018 2 729 1 1611 1 11939 1 9920 1 4367 1 2143 1 219 6 16471 1 10859 1 5483 1 8853 1 2802 1 858 1 11523 4 8379 1 1617 2 12721 2 13387 1 12465 1 5240 3 611 1 10867 1 4617 1 15235 1 5242 1 6498 2 9533 2 16356 1 12856 1 12082 2 13788 1 14163 2 14165 1 9535 3 12596 1 4990 1 6747 1 14954 1 4873 1 12337 1 4874 1 13264 1 4495 1 15877 1 10460 2 13002 1 14956 1 12730 1 1635 1 12471 1 5883 1 13004 2 3367 1 7218 2 9672 1 14558 1 13271 1 15880 1 7357 2 3376 1 9546 2 1646 1 4502 6 14176 1 13406 1 9270 1 3528 1 5888 1 5758 1 5254 1 6257 1 11003 1 383 1 10465 1 10211 1 4630 1 13011 5 11811 1 1650 2 5889 1 6760 2 10753 1 757 1 12225 1 6892 1 14969 1 11282 1 10343 1 13016 2 12228 5 6643 1 13549 2 16372 2 13814 2 6894 1 16144 2 crew_member 13422 1 4029 1 idyllic 5555 1 1338 1 605 1 13674 1 8555 1 7993 1 5506 1 2388 1 16108 1 15752 1 11949 1 838 1 13694 1 6367 1 1188 1 15955 1 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7460 1 978 1 7593 1 8393 1 8946 1 15521 1 12620 1 11940 1 16144 1 smashing 2552 1 9833 1 13626 1 9359 1 15742 1 3396 1 7490 1 5099 1 15601 1 9500 1 1998 1 4338 1 11312 1 16436 1 4040 1 9021 1 2546 1 5214 1 4720 1 14838 1 2843 1 2442 1 11090 1 13395 1 16484 1 apex 7752 1 3200 1 7365 2 2350 1 6199 2 14639 1 7368 5 13900 2 5771 1 12815 1 12849 2 1836 9 10830 1 3135 1 2529 2 14712 2 8309 1 1998 1 13353 2 14059 1 10583 1 15472 1 8661 1 4748 1 2928 1 8012 1 8087 1 1840 2 1149 1 1501 1 15267 1 264 8 8926 2 1620 4 10158 4 7344 1 1624 1 13169 2 227 1 13935 1 791 1 3214 2 3644 1 10128 5 1238 1 6935 1 4001 1 9860 2 7284 1 12569 3 2033 1 10131 2 11443 1 11474 2 4659 3 7421 2 9470 2 10628 3 5331 1 12733 1 6750 1 14935 1 16460 1 13968 1 12342 2 5814 2 14764 2 16165 2 3258 1 14212 2 4143 1 10075 1 10169 1 9376 2 13656 2 8533 2 11171 2 5360 1 16436 2 11233 2 3662 1 15887 38 2042 1 5178 2 919 1 5179 1 11645 2 10211 2 5104 1 8875 2 7744 1 7872 1 1104 3 9587 1 1935 1 7904 1 11421 1 12161 1 6638 2 12162 4 10411 1 7814 1 10018 2 5180 2 13768 4 11427 1 14189 3 2986 1 15688 1 6644 2 naris 2862 1 3580 1 14989 1 1660 1 812 1 addition_reaction 5442 1 3684 1 memories 14314 1 6011 2 9527 1 14342 1 12229 1 9819 1 8303 1 13900 1 12586 2 12392 1 10858 1 9983 1 3981 1 9425 1 7724 1 13821 1 14949 4 13674 1 6828 3 6735 1 7239 1 3987 1 10727 2 5321 1 1459 1 5646 1 763 2 366 1 11400 2 1807 1 13428 1 7701 2 10284 1 15590 1 9368 2 14584 1 12336 1 9571 1 13360 2 13648 1 8209 1 6220 1 13099 1 795 3 5087 1 2846 2 15445 1 6596 1 9992 2 11917 1 2036 2 10361 1 11474 1 13104 1 1635 1 9830 1 7451 1 9296 1 12643 1 2164 1 3113 1 8026 1 10742 1 14690 1 13865 1 12181 1 6247 1 1096 1 945 1 7992 1 14002 1 3375 1 4666 1 13214 1 2755 1 3527 1 8192 1 3019 1 4764 1 14525 1 5888 1 877 1 8769 2 3626 1 11713 1 8292 1 10596 1 14008 1 918 1 3303 2 10211 1 657 1 14144 1 9176 1 5727 1 15888 14 1137 2 9587 1 10755 1 5828 1 12102 1 2797 1 1833 1 2593 1 14636 1 2016 1 9920 1 nystagmus 16261 1 15987 1 13627 1 6318 1 11308 1 532 1 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529 1 15318 1 3556 1 11709 1 10247 1 2802 1 14780 1 12078 1 15894 6 4539 1 14853 1 15306 1 15846 1 9579 1 13490 1 12043 1 4502 1 2377 1 13576 1 11437 1 2914 1 11339 1 7509 1 3531 1 7577 1 13445 1 9021 1 14009 1 7413 1 1134 1 1385 1 15249 1 11485 1 4216 2 16356 1 13606 1 12255 1 1595 1 14548 1 3617 1 14617 1 12795 1 9663 1 6146 1 8436 1 8476 1 1656 1 2277 1 1210 1 2016 1 harper 3128 1 12829 1 719 1 10287 1 2448 1 5373 1 12609 1 3933 1 5795 1 10178 1 13110 2 ex_post_facto 4466 1 8975 1 8449 1 2407 1 14500 1 7169 1 silverware 8438 1 12521 2 14532 1 14010 1 4854 1 6238 2 282 1 12792 1 6283 1 1650 2 10974 1 15574 1 1087 1 14956 1 15895 5 8246 1 presentation 6011 1 13486 1 8153 1 3242 1 1939 1 11349 1 704 3 2698 1 15695 1 15896 35 1395 1 519 1 9365 1 15200 1 3320 1 12959 1 2703 1 3101 1 4529 1 9021 1 11495 2 13093 1 2898 1 9233 1 2966 1 7076 1 896 2 8088 1 11090 1 7581 1 7830 1 11016 1 2004 1 10841 1 14858 3 2199 1 8523 2 13501 1 10038 1 3038 2 8093 1 7582 1 16455 1 3113 1 2720 1 5718 1 4661 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16043 1 8653 1 9451 1 10380 1 16116 1 1301 1 14481 1 7539 1 15978 18 2872 1 15093 1 6053 1 7411 1 5493 2 2535 1 5045 1 8314 1 9400 2 3362 1 10325 1 13648 2 5500 1 2231 1 8997 1 7800 2 7733 1 15020 2 7169 1 7170 1 4933 1 56 3 11876 1 14690 1 8255 1 14555 6 8869 1 15808 1 945 1 9348 1 6248 2 8073 1 15026 1 3377 1 508 1 4446 1 6321 1 8390 1 12034 4 3086 1 12410 1 2945 1 13148 1 9078 1 11002 3 16065 1 16491 1 2546 1 15459 2 4887 2 13975 1 13408 1 13606 1 4507 4 12101 1 11749 1 11960 1 14044 1 10411 1 16259 1 15033 1 4514 1 6520 1 1656 1 11427 1 10218 1 13814 1 hayfield 10238 1 10131 1 5910 1 5178 1 mummification 10217 1 9920 1 absorbent_cotton 1340 1 2662 1 953 1 inaudibly 5395 1 1893 1 1292 1 hazelnut 9119 1 10321 1 3258 1 7904 1 5651 1 4994 1 6692 1 13482 1 293 1 9555 1 on_the_fence 8167 1 4659 1 1211 1 12029 1 573 1 14432 2 5648 1 14749 1 6513 1 12556 1 8180 1 13526 1 3858 1 13168 1 11081 1 common_cold 8285 1 1660 2 8541 1 9342 1 8582 1 8272 1 13610 1 2312 1 3189 1 13674 1 12374 1 16026 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4925 2 10255 1 8432 1 15981 17 1595 1 7133 1 16052 1 7519 1 3327 1 15572 1 14658 2 5565 1 800 1 12730 1 13237 1 7218 1 9477 1 11838 1 2131 1 5025 1 14243 2 585 4 779 1 10097 1 14864 1 8412 1 6319 1 5722 1 16028 1 723 1 10744 1 2316 1 16030 2 7390 2 16399 1 4041 1 2581 1 14527 1 659 1 13765 1 5034 1 11896 2 2407 5 15497 1 14018 2 6955 1 30 1 3161 2 12353 1 4865 1 15688 1 time_constant 3404 1 7042 1 2838 1 mackerel 802 2 11670 1 9405 3 3310 3 6378 1 7657 1 14149 2 4838 2 15591 1 10400 1 856 3 12848 1 13112 1 3134 1 11292 2 9285 2 14091 1 11233 1 6175 1 3992 3 14352 1 5179 2 654 1 15717 1 14065 1 7103 1 14279 1 897 1 12084 1 13935 5 15982 20 16087 2 1773 1 5390 1 2089 5 6535 1 1596 4 1685 1 3633 4 6801 1 9384 3 8581 1 mount_mckinley 3134 1 2106 1 15573 2 13821 1 6281 1 6754 1 10372 1 regular_payment 11869 1 2646 1 1827 1 11419 1 8064 1 15339 1 6859 1 3114 1 11939 1 2036 1 12059 1 15089 1 aesir 905 4 7569 1 7218 1 6046 1 3 1 6825 1 5187 2 14317 1 3189 1 4539 1 12440 1 2598 1 2326 3 3907 1 6989 2 10297 1 8872 1 14101 1 7539 1 8769 1 7410 1 1498 2 12726 1 12377 1 1444 1 15363 1 11512 1 9176 1 9398 7 12563 1 2950 1 8421 1 7012 1 8209 1 4511 1 7482 1 10735 1 15983 9 374 1 1109 1 268 1 5180 1 15611 1 5956 1 modesty 16180 1 12585 1 1658 1 8592 1 9390 1 7936 2 10537 1 13610 1 4369 1 14024 1 3206 1 15729 1 14979 1 13490 1 5872 1 3702 1 15901 1 15829 1 2705 1 11188 1 10156 1 3103 1 9532 1 5214 1 14651 1 1915 1 1063 1 13912 2 13790 1 10874 1 15056 1 12795 1 4930 1 5087 1 15984 11 6596 1 9573 1 15877 1 1004 1 12643 1 3218 1 3145 2 9668 1 5534 1 1280 1 6505 1 12372 1 2060 1 15916 1 536 1 1826 1 11483 1 5474 1 7774 1 8646 1 484 1 14565 1 5000 1 14374 1 11485 1 2247 1 1259 1 8501 1 15814 1 1260 1 2951 1 13663 1 16344 2 6105 1 8265 1 11749 1 10374 1 4707 3 14186 1 4546 1 1194 2 1861 1 5767 1 blue_ribbon 1185 1 4198 1 7957 1 8582 1 15402 1 9694 1 2057 1 1730 1 6792 1 13214 1 11347 1 5487 1 6369 1 9821 1 expendable 9532 1 3396 1 2865 1 leptospirosis 10195 1 7462 1 manipulation 11367 1 5369 1 7802 1 12522 1 14532 1 12586 1 8821 1 6032 1 9390 1 13795 1 6247 1 13552 1 9594 1 14784 1 14864 1 13576 1 12940 1 2960 1 9153 1 10253 1 9926 1 12270 1 6495 1 1013 1 12668 1 14614 1 8223 1 15279 1 1770 1 14929 1 9400 1 4912 1 14236 1 12764 1 5366 1 7461 1 12795 1 1667 1 7313 1 7169 1 15985 7 outlined 2498 1 56 1 9882 2 6611 1 6731 1 2164 3 5612 1 1076 1 14555 1 11675 1 4487 5 7724 1 6448 1 8531 1 16281 1 2608 1 62 1 11227 1 9478 1 3676 1 9169 1 9453 4 5029 1 15602 1 10299 1 6830 1 14883 1 10418 1 1645 1 7697 1 16326 1 8381 1 763 1 12410 1 2945 1 5901 1 12192 1 3303 1 11110 1 8796 1 745 1 1625 1 8967 1 9400 1 1290 1 13360 1 2918 1 13171 1 14168 1 16209 1 4622 1 15986 6 14956 1 brown_bat 16314 1 15328 1 4030 1 3444 1 11431 1 10965 1 16148 1 1818 1 11646 1 7457 1 14626 1 6178 1 16103 1 4829 1 counteraction 7148 1 14185 1 truant 3396 1 5065 1 4728 1 780 1 3087 1 genus_nothofagus 8702 1 14297 1 15840 1 9731 1 9067 1 hereabouts 16398 1 2767 1 4517 1 pectoral_girdle 1042 1 13063 1 1534 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1 8180 1 6232 2 13845 1 7904 1 9841 1 599 1 12009 1 16039 31 5671 2 5555 3 9922 1 1755 1 10310 1 9115 2 5556 1 14607 1 12326 1 4263 3 5347 1 16411 1 10253 2 11944 1 12530 1 10389 1 11129 1 3760 2 10876 1 15872 1 11259 1 8434 1 447 11 9467 1 799 3 6801 1 11474 4 5502 1 16056 1 13004 1 13967 2 12799 1 13970 1 5136 2 14493 1 872 2 1709 1 14365 1 951 1 15026 1 8259 1 9206 3 4698 1 15028 1 15027 1 9210 2 8559 1 7360 1 9945 1 6516 1 10272 1 9216 5 12546 1 2043 1 11964 1 14972 1 11427 1 sternum 5443 1 16040 13 7365 1 15007 1 9849 1 146 1 10660 1 3167 1 15759 2 1301 1 5347 1 13904 1 7473 1 5616 1 6398 1 5931 1 2329 1 7946 1 4590 1 4591 2 8663 1 5648 1 10387 1 14927 1 4720 1 6310 1 10873 1 7882 1 14584 1 9465 1 12308 1 8435 1 13099 1 14519 1 1845 2 7024 1 10131 1 6502 1 11602 1 5718 1 10556 1 12342 1 16433 1 7387 1 4143 2 1042 1 13836 1 8070 1 11777 1 13337 1 7895 1 2067 1 3501 1 2477 1 13177 1 10464 5 6843 1 9550 1 6230 1 2012 1 2857 1 656 1 3532 1 6574 1 13736 1 1651 1 14042 1 16494 1 9587 1 11895 3 4300 1 16441 1 3386 1 5694 1 6520 2 10656 1 11939 1 condolence 12410 1 12643 1 6371 1 8414 1 14054 1 10333 1 16116 1 12255 1 8137 1 7718 1 5087 1 15601 1 15953 2 724 1 6852 1 12941 1 peeper 9359 1 4279 1 5180 1 13099 1 13076 1 missionary 421 1 7598 1 219 1 1755 1 16041 12 253 2 12555 2 13674 1 1229 2 15399 1 8083 1 9285 1 13089 1 3173 1 7408 1 10356 1 14458 1 1176 1 5777 1 12790 1 3444 1 5619 1 7729 1 10485 2 9291 1 3483 1 13260 2 11528 1 9738 1 12503 1 15933 1 13722 1 2056 2 10927 1 4723 1 10701 1 1402 1 8129 1 6801 1 15574 1 9863 1 5331 1 1923 1 14136 1 12735 1 5654 1 4733 1 6289 1 16283 1 11229 1 1510 1 1579 1 4296 1 8935 1 12410 1 13075 1 8040 1 4801 1 15717 2 13079 1 6890 1 15656 1 14811 1 5608 1 4912 1 13380 1 16369 1 348 1 14188 3 12228 1 5441 1 7968 2 3059 1 crenulate 10128 1 11171 1 11427 1 14212 1 ferrocyanic_acid 13733 1 10906 1 10559 1 15430 1 12059 1 ordering 15581 1 573 1 8821 1 13552 1 15636 2 6080 1 13745 2 15527 5 5156 1 8512 1 1530 1 9153 1 10088 1 5586 2 5899 1 3784 1 11495 1 15330 1 12962 1 13360 1 15058 3 14 1 9236 1 7519 1 8022 1 13434 1 11443 1 8829 1 3921 1 7027 1 10492 2 16164 1 1883 1 8635 1 9833 1 8026 1 15910 2 4855 1 4907 1 7320 1 15965 1 536 1 2855 1 6230 1 484 1 14010 1 13697 1 2136 1 10306 1 13893 1 9525 1 14527 1 13947 1 5033 1 12383 1 101 1 7460 1 6105 1 6359 1 13765 1 13895 1 7151 1 11584 1 14226 1 12522 2 16042 22 7851 1 16410 1 1229 1 11728 3 12721 1 15729 1 13192 1 14155 1 8730 1 1695 2 6053 1 6925 1 6557 1 7413 1 12669 4 7607 1 1915 1 8246 2 6685 1 14823 2 1242 1 12795 1 7169 1 5427 1 1635 1 4933 1 6180 2 448 1 2877 1 9407 1 7111 1 12534 1 691 1 14626 1 11273 1 2881 1 7809 1 14835 1 8325 1 11890 1 4766 1 8559 1 3464 1 8942 1 10340 1 1257 1 6514 1 8618 2 16494 4 8814 1 14969 1 12543 1 4707 1 8946 2 genus_erianthus 3569 1 15205 1 3354 1 rung 5770 1 10824 1 8747 1 2655 1 16043 11 7692 1 4950 2 13422 1 3477 1 11147 1 3320 1 4129 1 15978 1 6053 1 9822 1 14197 2 2332 1 5493 1 9021 1 14980 1 3992 1 12562 1 14929 1 14 1 7170 1 10491 1 4464 1 8250 1 10363 2 56 1 14208 1 11025 1 2128 1 14555 1 6909 1 6248 1 15026 8 9377 1 970 1 13543 1 8904 1 5223 1 10522 6 510 1 9172 1 14307 1 15029 1 12973 1 2510 1 15311 1 8502 1 4507 1 12383 2 1291 1 13979 1 11895 2 3831 1 16408 1 6520 1 11427 1 trackless 6635 1 6746 1 5329 1 202 1 11008 1 5861 1 2263 1 12643 1 5722 1 9737 2 9090 1 buckingham_palace 4941 1 7512 1 6772 1 1537 1 gestural 9506 1 33 1 10333 1 7762 1 5412 1 16477 1 2488 2 postmortem_examination 5883 1 12145 1 13752 1 3534 2 13214 1 6294 1 11327 1 12372 1 16117 1 3568 1 12383 2 shipment 1793 1 4017 1 11330 1 9342 1 10265 2 11492 1 10844 1 7851 1 15288 1 531 1 12818 1 1642 1 11819 1 4306 1 13745 2 6830 1 6962 1 4746 1 16044 10 4338 1 5873 1 3480 1 7856 1 5176 1 81 1 891 1 4987 2 3137 1 15073 2 10322 2 9193 1 3744 1 7148 1 16355 1 7361 1 15077 1 2346 1 8406 1 14823 1 12176 1 8800 1 1539 1 15907 1 14047 1 2159 1 7678 3 germanite 10045 1 12192 1 5041 1 10369 1 4185 1 11327 1 1929 1 itch_mite 13675 1 7636 2 8180 1 7413 1 6003 1 16026 1 8314 1 10199 1 2507 1 867 1 8146 1 531 1 factor_of_proportionality 16052 1 5841 1 585 1 6266 1 6747 1 473 1 order_diapensiales 11292 1 14196 1 884 1 1893 1 5173 1 5361 1 genus_alternanthera 15301 1 3404 1 12181 1 5361 1 stuffed 10415 1 1331 1 15599 2 1660 1 5262 1 7368 2 12391 1 13317 1 9359 1 10310 2 6204 1 13672 1 8156 1 6267 1 2698 1 3870 1 13422 1 9323 1 7572 1 7237 2 11819 1 15434 1 6134 1 15171 1 7239 1 11796 1 14882 1 6272 1 4986 1 11353 1 3875 1 8920 1 1999 1 7277 1 15011 1 16045 25 11593 2 2663 2 10 2 5967 1 16047 2 789 3 5457 1 13718 1 13720 1 11981 1 8828 1 1240 1 5298 1 12795 2 13649 1 16358 2 9237 1 9860 1 1596 2 4994 1 13965 2 11474 1 14956 2 14859 1 9162 1 12029 1 6777 1 3112 1 3793 2 10965 1 15062 2 2127 1 10706 1 13968 1 13037 1 9998 1 16092 1 4471 1 14246 1 12680 1 11882 1 3375 1 7587 1 12374 1 3378 1 13840 1 15916 1 14627 1 6452 1 4698 1 16436 1 12833 1 15684 1 12246 1 7257 1 2012 1 11375 2 10970 3 9582 1 5439 1 8263 2 9248 2 5728 1 67 1 1957 1 10143 1 7120 2 13450 1 2953 1 6580 1 11612 1 4365 5 2986 1 13706 1 14476 1 carol 6070 1 1208 1 8749 1 15912 2 15487 1 fairies 14993 2 9955 1 12283 1 11002 2 16046 8 6379 1 212 1 8006 1 8718 1 12181 1 1338 1 11212 1 13948 2 2604 1 12795 1 7490 1 3495 1 4398 1 10507 1 13434 2 1441 1 9420 1 8055 1 13482 1 10780 1 and_then 7367 1 13157 1 251 1 10473 2 1026 1 5 2 2326 1 5380 1 2836 1 12359 1 2560 1 6655 3 12889 1 3403 1 14459 1 14714 1 524 1 9705 3 10226 1 9958 1 5387 1 13935 1 7636 2 6145 1 3684 1 14202 1 1818 2 11022 1 4659 3 9715 1 10492 2 3112 1 14209 1 3113 1 4661 1 7891 1 10774 1 6910 2 14214 1 9723 1 11570 3 5913 1 9172 1 11312 1 13443 1 8176 1 13444 1 13177 1 5179 1 2581 1 1560 1 4913 1 1291 1 13702 1 9728 2 7904 1 12654 2 1562 2 3699 1 11584 1 284 1 14476 1 10244 1 5184 3 13958 2 15788 1 3134 1 7158 2 12145 1 16299 1 8661 2 293 1 10508 1 10253 1 16045 2 3444 1 9192 1 12669 4 16047 88 789 2 8925 1 7670 1 1309 1 11327 1 15802 1 6929 2 3954 1 10258 1 6686 1 8435 1 11051 1 7922 1 2089 1 9991 1 9992 1 15572 1 15301 1 12675 1 13965 4 7421 3 3145 1 10812 1 13967 1 11602 1 3455 1 9477 1 9998 1 59 1 8675 1 61 1 10002 1 13214 2 6188 1 9206 1 2097 1 13218 1 9209 1 2100 1 8940 1 8687 1 6453 1 11343 1 6697 2 67 4 10527 1 11895 1 11896 1 8449 1 4707 3 5694 2 4233 1 10018 1 16318 1 5957 1 4943 1 5442 2 573 1 14507 2 3164 5 14259 1 16077 1 16321 7 4950 2 4458 1 3986 2 4951 1 11619 2 7695 1 5449 1 5964 1 5965 2 5704 3 11624 1 8702 1 8458 1 6713 1 6215 1 3483 1 3995 1 7191 1 5969 1 6715 1 9506 2 9235 1 8211 1 1596 2 4723 1 5972 2 2395 1 9509 1 3736 1 15062 2 4729 1 10556 9 3737 1 10293 1 16094 1 2912 1 12968 2 10295 1 4471 2 12705 1 1346 1 8467 1 7956 1 11924 1 12442 1 4009 1 12970 1 14808 1 4476 2 11375 2 11110 1 7457 2 11646 1 9248 2 14527 1 1894 1 5729 2 1357 2 599 1 13768 1 11939 1 16348 1 15350 1 351 1 11119 4 12718 2 9527 1 1903 1 4017 1 12719 1 112 2 857 1 4983 1 2144 1 859 1 4263 1 7210 1 8989 2 7473 2 2662 1 5240 1 6494 1 14291 1 11944 1 10321 1 5494 1 13784 1 13258 1 2152 2 11403 1 7477 1 5742 1 13260 1 1624 5 16356 1 11662 1 10874 1 11405 1 8246 1 3760 1 5745 3 6244 1 12213 1 16358 1 10067 4 7731 1 374 1 13264 1 2673 1 12731 2 1370 1 2165 1 1924 2 4499 1 2419 1 7738 1 10333 1 6247 3 3219 1 14037 3 13799 1 13267 1 7992 1 7991 2 10077 1 10334 1 14305 1 3223 3 3764 1 15617 1 877 2 1928 1 7743 1 9271 2 13810 1 7230 1 7009 1 8502 1 2691 2 1136 1 2953 1 11686 1 11424 1 16143 1 13286 1 16373 1 5770 3 7504 1 1660 1 15397 2 16377 1 884 2 6267 1 2698 1 5264 1 13821 1 15635 1 11693 1 14576 1 11147 1 6019 1 4527 1 9285 1 1943 1 888 1 8279 1 8012 1 9021 4 5016 1 15135 1 15403 1 12761 1 14322 1 5271 1 14583 1 14585 2 1667 2 10092 1 4290 1 2710 1 6283 2 1404 1 4794 1 7028 1 13034 2 1406 3 10628 1 13299 3 7766 1 9833 1 10630 1 8529 1 7030 1 3258 1 16165 1 15147 1 9037 1 2211 2 15916 1 14866 1 13302 1 11714 1 11997 1 16398 1 7774 1 653 2 1410 1 10369 1 13578 1 5033 2 1957 1 15423 2 9587 1 7260 2 12517 1 161 1 15920 3 14604 1 421 2 9848 1 6550 2 927 1 4092 1 1961 1 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12599 1 2539 1 2043 1 family_staphylaceae orinoco_river 15142 1 4472 1 1685 1 10335 1 aquiline 15249 1 16258 1 6580 2 7045 1 basidiomycetes 4894 1 10321 1 8047 1 5988 1 12353 1 5950 1 11474 1 6494 1 12578 1 gree 6635 1 3760 1 11431 1 9097 2 15401 1 wales 4283 1 13455 1 1755 1 12524 1 253 5 3164 2 11859 1 12011 1 12555 1 5961 1 705 1 14056 2 12848 1 6587 1 6864 1 3907 1 13322 1 13189 1 1907 1 8339 1 15200 2 2628 1 13091 1 14348 1 293 3 2263 2 7789 11 1770 1 2966 1 10325 1 13648 1 4463 1 7830 4 5680 1 13878 1 154 1 6244 1 10259 1 10131 1 10038 1 6970 1 2673 1 12369 1 5945 1 2035 1 16088 27 1576 2 1737 1 505 2 12123 1 8527 1 10398 1 12284 2 15241 1 3855 1 11675 1 5537 1 7864 1 8711 1 12705 1 10335 2 1441 1 9748 1 15155 1 4829 1 10403 1 5228 3 14219 1 1103 1 13948 4 6818 1 16293 1 16494 1 15920 1 13154 1 2921 1 12296 1 2594 2 5441 1 15688 1 13312 1 beat_down 12621 1 7697 2 6612 1 2873 1 chargeable 12529 1 10156 1 3145 1 9804 4 139 1 7893 1 4889 1 16177 1 8362 1 4772 1 9826 1 4707 1 7884 1 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8829 2 3852 2 3038 2 3329 1 15704 1 11504 1 10704 1 475 1 7317 1 13110 1 15486 1 6351 1 15962 1 2009 1 719 1 13371 1 8100 1 14666 3 6604 1 3049 1 484 2 7086 2 6358 1 213 1 3630 1 5608 1 9107 5 15225 1 11789 1 6853 1 2016 2 7093 1 11517 1 15499 1 6854 1 8593 1 12586 1 16471 1 7850 3 4155 3 2802 2 15727 1 737 1 10725 1 10726 1 11796 2 3874 1 5616 1 7099 1 7856 2 15978 1 6617 1 2026 2 223 2 5619 3 2534 1 5621 1 10733 1 13912 1 2306 1 5131 1 2032 1 5882 2 7348 1 8129 1 10201 1 14956 1 9405 1 1004 1 13398 1 8868 3 8870 1 8137 1 13404 1 14693 2 3375 1 508 1 13656 1 3896 1 510 1 6385 1 13148 1 11002 1 1014 7 16491 1 3088 2 16492 1 2546 1 13408 2 7361 11 4172 1 7362 1 16494 3 14969 4 8393 2 6389 1 3902 1 13666 1 12352 2 12620 1 6894 3 15756 2 foreigh 11966 1 10771 1 12596 1 2263 2 equably 11960 1 13371 1 rhapsody 13543 1 sensuously 1596 1 12590 1 8103 2 14453 1 6663 1 methodist 1081 1 141 1 1667 1 13259 1 12165 1 704 1 13482 1 12848 1 ligature 2902 1 7740 1 5278 2 14103 1 10273 1 12539 1 14021 1 5055 2 11493 1 4171 1 1953 1 sector 8082 1 4712 1 15396 1 927 1 13674 2 9886 1 15897 1 1526 1 16118 32 9696 1 9285 1 13491 1 1422 1 1570 1 3441 1 5616 1 5964 1 2960 1 2927 1 13354 1 2467 1 6617 1 1499 1 5841 1 2026 1 10867 4 3404 1 5492 1 16328 1 15864 1 16417 4 5243 1 6498 7 2083 1 12237 1 6929 1 1180 1 9633 2 15105 8 13430 3 3493 1 6406 1 9860 4 15572 1 8997 4 2394 1 6444 1 13831 1 15450 1 4659 1 7421 2 1246 1 13365 1 3145 1 3113 1 4847 2 2448 1 8582 1 10705 1 11675 1 7990 1 12476 1 8070 1 779 1 9478 4 4853 1 9169 2 14864 11 8104 1 1604 1 15115 2 3720 1 15884 6 11033 4 6230 1 14010 1 5311 1 1605 1 15715 1 5362 2 12003 1 539 1 1652 1 12067 3 5861 1 629 1 14969 2 14044 1 12161 1 16178 1 14020 1 13738 2 7464 1 7596 1 13897 9 7014 1 13249 1 6547 4 impressive 1330 1 6011 1 6200 1 11142 1 11492 1 252 1 14845 1 5072 3 5581 1 6958 1 1939 1 15996 1 14640 1 8881 1 4526 1 13228 1 3399 1 1147 1 11821 1 16151 1 763 1 5454 1 14980 1 12495 1 3177 1 3992 1 5648 1 4531 1 11495 1 5214 1 13495 2 16330 1 7762 1 5165 1 4959 1 896 1 7581 1 8209 1 4843 1 12310 1 1596 1 13171 1 11019 1 14522 1 4659 1 11367 1 529 1 13035 1 15591 1 2720 1 3925 1 5719 1 8464 1 15146 2 9775 1 9515 1 12126 1 13373 1 13576 1 3859 1 8769 1 16398 1 7774 1 483 1 6424 1 7649 2 6103 1 5104 1 14221 2 9176 1 12449 1 5608 1 4082 1 14147 1 7260 1 4153 1 13451 1 12519 2 598 2 10575 1 284 1 13581 1 2924 1 15285 1 8183 1 7154 1 1616 1 3064 1 15089 1 1226 2 14609 1 12145 1 2598 2 13386 1 15436 1 3065 1 16119 32 7274 1 3874 1 7539 1 4211 1 2149 1 7161 1 7667 1 363 1 11658 2 1362 1 7413 1 14352 3 11403 1 9659 6 12792 1 4099 1 6241 1 10513 1 4216 1 11191 1 227 1 10875 1 6442 1 11406 1 10325 1 10064 2 10195 1 3449 1 3005 1 996 1 2748 1 3816 2 1916 1 13000 1 174 1 5427 1 6114 1 12090 1 10679 1 14897 1 4933 1 5302 1 1004 2 3145 1 10264 1 11538 1 6505 1 8930 1 8137 1 3824 1 15616 1 62 2 8555 1 8138 1 450 2 9544 1 3082 1 8677 1 15308 1 2881 1 2882 1 9077 1 14773 1 880 4 16492 2 2546 1 4169 1 695 1 14838 3 312 1 13013 1 16439 1 7563 1 5512 1 14969 1 7122 1 12102 1 11686 1 1656 1 13982 1 5142 1 5957 2 hydrofluoric_acid 10906 1 13967 1 13859 1 11403 1 2515 1 7427 1 9219 1 12620 1 14879 1 family_chlamydiaceae 9308 1 6389 1 1457 1 8309 1 reinforce 9509 1 9296 2 3437 1 14917 1 9577 1 5718 1 4761 1 4950 1 2658 1 12525 1 6828 1 8881 1 11739 1 2598 1 16120 11 9228 1 6964 1 2662 1 3501 1 3280 1 2705 1 7256 1 2332 1 5675 1 653 1 8382 1 1134 1 6741 1 695 1 12856 1 13749 1 4171 2 7010 2 2765 1 11827 1 1573 1 7120 1 12978 1 14892 1 13432 1 4707 4 13467 1 7025 1 4793 1 2695 2 11425 1 1656 1 7419 1 9899 1 5207 1 14956 1 buckler_mustard 3760 1 13462 1 admonish 15479 1 5120 1 8658 1 15318 1 4707 1 5227 1 14128 3 3806 1 6815 1 11081 1 tentaculata 8529 1 bulimia 8739 2 mustelidae 16121 9 passenger_train 6232 1 206 1 1707 1 544 1 12301 1 6287 1 spring_water 9056 1 5785 1 9065 1 9819 1 81 1 mournfully 4778 1 9397 1 7446 1 13002 1 coconspire 8826 1 5214 1 13972 1 830 1 13736 1 8088 1 frequency_distribution 15695 1 6168 1 1395 1 5602 1 4448 1 10698 1 3241 1 11596 1 5103 1 2989 3 9236 1 12992 1 2927 1 536 1 charred 4433 1 5295 1 16122 5 6059 1 15539 1 2664 1 11951 1 10 1 7343 1 5000 1 16329 1 15230 1 8418 1 213 2 16258 1 328 1 2273 1 8017 1 9558 1 11406 1 15927 1 15636 1 6629 2 7260 1 1301 1 6953 1 930 2 12803 1 2211 1 8101 1 1019 1 7384 1 3269 1 metrical_unit 11606 8 16123 7 12674 7 15002 10 795 1 steam_boiler 3320 1 3874 1 2426 1 5245 1 4944 2 9728 1 2857 1 2953 1 11884 1 14391 1 4142 1 4950 1 13276 1 2512 1 bathetic 1513 1 8233 1 4202 1 3367 1 2950 1 4912 1 6630 1 5087 1 wed 12731 1 5065 1 9116 1 6134 1 6200 1 7316 1 3520 2 5134 1 13516 1 13666 1 10625 1 2214 1 brutalize 5416 1 13870 1 14881 1 6972 1 13674 1 flowered 11431 1 15994 1 5897 1 10083 1 884 2 10349 1 13821 5 12882 1 11292 1 9560 1 1799 1 15636 1 2436 12 14712 2 12627 1 7239 1 13290 1 14196 2 2560 3 12359 1 14458 1 3783 1 8012 2 13681 1 14980 1 8886 4 15404 3 5901 1 15267 1 5903 1 14065 1 2563 1 7023 1 8021 6 1667 1 11017 1 6146 4 6535 1 10094 1 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6977 4 211 1 6480 1 5228 1 7457 2 9643 1 16465 2 14279 1 1893 5 14527 1 6104 1 2291 1 5731 1 7846 1 15082 1 2921 9 1487 5 16350 1 13900 1 1755 1 9922 1 10310 1 13640 1 4263 30 13777 1 15232 1 1227 1 7471 3 5126 1 5347 3 11397 1 3875 1 13645 1 13646 1 10321 2 16124 75 1621 1 2932 1 11256 1 3760 12 5355 2 14297 1 9664 1 9266 1 2159 1 14830 1 7350 15 8252 1 13004 1 14173 1 2819 1 16251 1 5751 1 2312 1 15616 1 8257 1 15380 2 8259 2 5361 1 1932 2 11956 2 1014 5 6003 1 7360 9 12873 1 9945 2 15753 1 14972 2 12228 1 6644 1 lise_meitner 7562 1 2011 1 8845 1 11163 1 6854 1 9728 1 16428 2 7729 1 3764 1 15576 1 14012 1 3630 1 2376 2 14318 1 389 1 14738 1 5271 1 11925 1 hauler 5336 1 9823 1 11789 1 10363 1 12798 1 2583 1 askance 8874 1 5785 1 7766 1 2546 3 10556 1 8868 1 8464 1 14492 2 15103 1 9719 1 5576 1 7593 1 98 1 10131 2 1968 3 6419 1 11572 2 scale_of_measurement 15695 1 16052 1 10867 1 12145 1 4925 1 9021 1 5103 1 585 2 529 1 12669 1 11017 1 7899 1 hydrogen_ion_concentration 9415 1 15228 1 10133 1 1203 1 438 1 85 1 4169 2 11403 1 10816 1 1893 1 7017 1 4951 2 5694 2 3435 2 8408 1 1389 1 2881 1 5079 1 3347 2 12964 1 quadrant 9639 1 11716 1 11189 1 8749 1 6747 1 5425 1 9705 1 8997 1 11410 1 genus_calochortus 12968 1 2651 1 12697 1 10871 1 7457 1 6178 1 14607 2 11474 3 3983 1 slowing 1635 1 15499 2 1004 2 1660 1 3113 4 12393 2 13318 1 7218 1 9451 1 4761 1 15470 2 4182 1 12145 2 11728 1 14002 2 688 1 12080 4 536 1 4998 1 5878 1 9641 1 15864 1 9872 1 2563 2 15736 1 11327 2 10442 2 5033 1 2747 2 7133 1 16125 16 8449 2 3684 1 5694 1 11424 4 12353 2 11804 1 11612 4 13286 1 15394 1 13814 2 4464 4 1296 1 vaguely 10492 1 10867 1 9297 1 2546 1 8515 1 274 1 10247 1 6338 1 9425 1 5403 1 7932 1 16117 1 171 1 6145 1 4110 1 4688 1 16484 1 8073 2 12337 1 14186 1 2797 1 9394 1 5964 2 7169 1 antarctic 11118 1 1331 1 3636 1 884 1 12077 1 4335 1 5235 1 707 1 5347 2 13089 1 12266 1 8512 1 7697 1 7408 1 4185 1 293 1 5455 1 3176 1 13682 1 5213 1 10158 1 4620 1 3997 1 5271 1 11468 1 3361 1 4062 1 7134 1 14584 1 3760 1 10033 1 10161 1 16126 11 3490 2 2306 1 11259 1 397 1 6535 1 10094 1 1635 1 4030 1 4140 1 11603 2 13968 1 3114 1 6752 1 4734 2 8031 1 6977 1 5031 1 3088 1 1558 4 16036 1 3307 1 6520 1 5342 1 11612 1 14704 1 13312 1 motor_end_plate 6924 1 12606 1 8218 1 5131 1 6497 1 5616 1 5313 1 12620 1 531 1 7133 1 royal 4283 1 6011 1 15254 1 9819 1 15630 1 8153 4 2255 1 6337 1 1084 1 13552 3 1523 4 13821 1 13674 1 9558 1 11212 4 885 1 6208 1 11755 1 11147 2 13679 1 1526 2 14644 1 9627 1 7574 1 11436 1 3610 1 333 1 15200 1 575 1 5380 1 195 2 13930 3 13354 1 763 1 8962 1 5586 1 8702 1 2263 1 5455 2 9956 1 10956 1 9632 1 3483 3 7191 1 13027 1 1595 1 5271 1 7132 2 1036 1 8350 1 5528 1 1667 1 14795 1 14 1 8408 2 10428 1 268 2 11021 1 4002 3 7314 2 13830 1 472 1 5275 3 8214 2 13367 1 1279 1 12372 1 6287 1 7710 2 12059 1 6351 2 4143 4 20 4 5598 7 778 1 587 1 9518 1 13888 1 3739 2 2212 4 725 1 2453 1 5176 2 12578 2 4077 1 12377 4 11718 1 278 1 2290 1 8718 1 14221 1 4477 1 12003 7 9644 2 13843 1 11847 1 659 1 14738 1 158 2 14529 1 6105 1 11848 1 5035 1 4153 2 13451 1 9918 1 599 1 2921 2 13768 1 1834 1 6853 2 4483 1 11516 1 11584 4 7969 1 6672 1 1902 1 4426 1 288 1 8376 1 12524 1 9735 1 9186 1 15089 1 1836 1 6168 1 7158 1 9116 4 12395 3 11728 1 668 1 6238 1 3872 1 4023 1 16189 1 1762 1 9261 1 5415 2 6172 3 14232 1 13712 1 8057 1 15797 4 1305 1 10321 1 4376 2 7789 1 7477 2 14159 3 16419 1 14233 1 13260 1 6498 1 3002 1 1915 1 12672 2 4683 1 11662 1 1180 1 297 2 125 1 6562 1 3816 1 5680 1 503 1 16127 64 16484 4 3520 3 12727 1 10131 1 9664 1 4029 1 13534 1 2035 1 2935 1 1576 1 1635 1 1125 2 10809 1 1637 1 8320 2 1507 1 9203 1 16195 1 13801 3 14693 1 9614 1 9268 1 1712 2 951 3 3376 1 508 1 15115 1 9075 2 11414 1 13009 1 5757 2 4998 1 9079 1 11002 1 13079 1 1257 1 8942 5 14838 1 7872 1 11279 2 7012 2 15579 1 456 2 1328 1 5694 2 69 1 6581 1 5957 1 6644 1 15688 1 ionized 3912 1 9830 2 9755 1 15228 1 10421 1 12972 1 8541 1 3473 1 16438 1 16329 2 12459 1 1925 1 7102 1 2433 1 8628 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8040 2 5228 1 6890 1 3862 1 16292 2 4230 2 8180 1 1750 1 6361 1 8228 1 11489 1 1835 1 rushdie 14378 1 9663 1 3145 1 8153 1 14658 1 13428 1 1281 3 1656 1 13706 1 atomistic 10247 1 4502 1 13075 1 14527 1 1907 1 11796 1 gloriosa 8458 1 3404 1 133 1 16432 1 4495 1 6425 1 5361 1 anastomosis 5442 1 7256 1 6739 1 12297 1 14103 1 8822 1 1947 1 13844 1 9587 1 9932 1 12653 1 8989 1 14504 1 5694 1 3320 1 5282 1 8408 1 3580 1 7058 1 13264 1 1999 1 14972 1 3084 1 triton 1799 1 2982 1 5797 1 2529 1 1104 1 13935 1 transferase 16355 1 3684 1 510 1 10765 1 11850 1 7262 1 13402 2 looking_for 573 1 14507 1 4774 1 1660 2 13418 1 11349 1 10760 1 12041 2 3396 1 7302 1 14319 1 6080 1 7374 1 13490 1 11148 1 13089 2 12959 1 15472 1 4129 2 11081 1 2490 1 5323 1 6713 1 11357 1 3483 1 13428 1 961 1 11360 1 5528 1 5458 1 7135 1 15 1 13992 1 2445 1 4659 1 4466 1 10429 1 836 1 9297 1 9243 1 965 1 8026 1 11709 1 11223 1 14210 1 8582 1 15409 1 8531 1 10775 1 2973 1 8031 1 11776 1 12969 1 12909 1 6353 1 13374 1 14525 1 11312 1 5795 2 11572 2 9521 1 12971 1 3741 1 11999 2 5475 1 7649 1 5228 2 2290 1 6980 1 1104 1 5231 1 12382 1 16344 1 2513 1 661 4 7260 1 729 1 6668 1 16178 1 2016 1 284 1 3059 1 12718 1 7398 1 11119 1 1755 1 424 2 12077 1 7850 1 14413 1 7405 1 16117 1 4808 1 13777 1 4370 1 6167 1 11461 1 4264 1 12145 1 13386 2 9189 1 4306 1 5988 1 5291 1 15975 1 13063 1 2869 1 7856 1 1176 1 7665 1 11943 1 10253 1 222 1 15864 1 1364 1 2360 1 12274 1 8432 1 9601 1 1065 1 12856 1 13395 1 3816 1 2231 1 50 1 15933 1 9663 1 12795 1 7796 1 4991 1 126 1 15609 1 8436 1 12598 2 1368 1 13965 1 1632 1 4029 1 7169 2 6801 1 15574 1 3077 2 12284 1 11330 1 11735 1 1924 1 12472 1 13795 1 4693 1 1708 1 4696 1 2680 1 2754 1 15114 1 9869 1 950 1 11742 1 12407 1 5888 1 9616 2 2882 1 1716 1 11339 1 6325 1 1014 1 4041 1 6949 1 2546 1 16312 25 8875 1 5439 1 11747 1 4701 1 757 1 4772 1 12102 1 1328 1 11686 1 4118 1 1656 1 6643 1 2826 1 2488 1 tonicity 2699 1 13013 1 5443 1 7372 1 14147 1 14 1 11105 1 10706 1 8512 1 5616 2 10772 1 12485 1 469 1 mexican_hat 15884 1 8702 1 11184 1 5663 1 5718 1 7288 1 3258 2 12326 2 4263 2 872 1 1836 1 4143 1 6020 1 12359 1 unknown 11966 1 10757 1 14053 2 8156 1 13674 1 15635 1 9015 1 2190 1 11147 1 2326 1 6019 1 8512 1 10617 1 9955 1 10483 1 10485 2 5648 2 4897 1 15765 5 13024 1 8517 1 1534 1 14066 1 7132 1 12764 4 5904 2 13029 1 2444 1 267 1 16162 2 8522 1 6535 1 13171 2 5906 1 1406 1 10492 1 13034 2 14589 1 13035 1 9717 2 2972 1 1161 1 4539 2 10097 1 16026 2 14215 1 13576 1 13695 1 1826 3 5543 1 11172 1 11842 1 4670 1 8292 1 13698 1 538 1 657 1 8647 3 4912 1 8911 1 8180 3 7260 1 1055 1 9589 1 13581 2 15283 2 15430 2 927 1 3700 2 2076 2 9186 1 8184 1 7911 1 9451 2 12011 1 1418 1 15170 1 12145 1 11728 2 8309 4 14481 4 4211 1 14232 2 10253 6 12669 1 12397 1 11327 2 10513 1 2747 1 7793 1 790 1 15570 1 12400 1 15444 1 5679 1 12795 3 15446 1 15572 6 10132 1 8319 1 3145 1 3594 3 7682 1 16196 2 13214 1 1441 2 14626 1 2881 1 5573 2 1579 1 4698 1 11483 1 15459 1 8559 1 10011 1 13077 1 813 5 4576 1 14501 1 16313 73 10525 1 4450 1 3605 1 6328 2 11896 1 5694 4 3161 2 6071 1 4708 1 16318 1 7567 1 13485 1 573 3 14258 1 4333 2 12693 1 14259 1 12556 1 6828 1 4951 2 10152 1 3726 1 15472 2 10698 1 2263 1 12561 2 12047 1 3992 2 14927 1 6345 1 9506 1 2783 1 3494 1 10288 1 8580 2 3732 1 11771 2 7582 1 10553 1 9635 1 3189 9 7835 1 4143 1 969 6 10299 1 14140 1 8713 1 1099 1 2507 1 11108 1 12192 1 10566 1 12975 1 6980 1 16344 1 101 2 595 1 1896 1 10975 1 13380 1 15719 2 729 1 2799 1 7596 1 15084 1 9648 1 12324 1 7850 2 7851 3 14025 1 7210 1 13251 1 5347 3 6365 1 5988 1 5240 1 5618 1 4158 1 222 1 15864 2 11403 6 9532 1 15736 1 9397 1 5244 1 6113 1 11406 1 13648 1 13395 1 2306 1 9663 1 16484 3 2814 2 10070 1 869 1 505 4 13651 1 1370 1 4497 1 6502 1 4165 1 7218 1 2680 1 2423 5 1510 2 1250 1 15115 1 510 2 9272 2 13919 1 15244 1 1014 1 4041 1 7230 1 4886 1 13150 1 10211 1 6258 1 4888 1 13408 2 6513 1 14838 1 16494 1 7012 1 12875 1 1264 1 4512 1 3386 1 385 1 6520 2 13814 2 15756 1 4390 1 thorn_apple 6207 1 15376 1 15685 1 mercury_thermometer 1211 1 11163 1 5443 1 2945 1 5128 1 13968 1 16008 1 9451 1 14738 1 15470 4 12145 1 8083 1 641 1 10664 1 1631 1 1907 1 5447 1 12599 1 10685 1 12480 1 10131 1 2881 1 1389 2 13286 1 unhewn 11406 1 1635 1 13484 1 1859 1 14507 2 12304 1 4185 1 13736 1 15965 1 wallenstein 15294 1 10868 1 9833 1 11512 1 4155 1 8942 1 16302 1 state_of_grace 2332 1 5922 1 14126 1 1014 1 14650 1 12165 1 10453 1 4700 1 14555 1 2387 1 15590 1 10137 1 11539 1 12059 1 14236 1 2326 1 1907 1 9264 1 11924 1 2617 2 8100 2 2628 1 8128 1 14482 1 5573 1 nailhead 8438 1 4770 1 11227 1 6538 1 674 1 170 1 3888 2 1369 1 2855 1 club_soda 2237 1 2432 1 10543 1 15943 1 denote 9011 1 11551 2 3242 1 3776 13 13552 1 13422 1 1395 1 11012 1 7374 3 11147 1 10480 2 6902 1 8512 2 11553 4 2560 1 9021 1 10483 1 5648 1 11826 1 4401 1 13295 1 904 1 7765 1 11701 1 10363 1 3113 1 8529 3 9834 1 5173 1 10775 2 9435 1 4412 2 536 1 11033 2 11999 1 8907 3 4910 2 15656 1 10106 1 2581 2 13579 1 12653 1 7260 1 6043 1 6428 1 1055 1 1054 1 9978 5 5406 3 11584 1 1565 3 10380 3 15790 1 5926 2 3134 1 1836 1 10662 1 3135 1 8545 1 13063 1 4921 1 7785 1 293 1 4307 3 2873 1 3951 1 10253 1 4925 1 673 1 2358 1 7163 3 12669 1 14236 1 297 1 12674 1 9334 1 12020 2 13467 1 7169 1 10395 1 8438 1 6180 12 10265 1 14363 1 4823 1 945 1 62 1 7060 1 2241 1 951 2 2755 2 3596 1 6807 1 2377 1 2881 1 15310 1 7428 1 10139 1 13076 1 15459 2 3830 1 10939 1 3970 1 2104 1 10822 1 13082 2 11205 4 11896 15 1450 1 16315 65 6192 2 12296 1 7567 1 9882 1 15318 1 11349 1 12555 2 4950 1 16078 1 11619 1 2627 1 13228 2 5078 1 5841 2 5705 1 8965 2 11085 1 8349 1 1091 2 8091 2 8090 1 10424 1 6596 7 1596 1 12570 1 1208 1 1209 1 7314 1 11094 1 9509 1 4139 1 13368 1 5854 3 3189 1 1475 2 717 1 15846 1 11924 2 1099 9 15847 2 13374 1 13117 1 3626 1 4256 1 2404 2 2646 1 14009 2 7717 1 9644 2 1893 1 10572 1 9915 1 6483 1 12323 1 106 1 11789 1 2921 1 2016 4 12841 1 9920 1 12585 1 15228 1 110 1 2802 3 12848 4 5613 2 13777 1 2658 1 11392 1 5347 1 15602 1 6367 1 5128 2 14292 1 866 1 2535 1 13260 1 3759 3 6498 1 11256 1 8246 1 11406 1 13648 2 5500 1 15105 1 1238 1 12597 1 8997 12 2159 1 15877 1 13794 1 620 1 10333 3 7222 1 13801 1 13273 2 12612 1 6255 2 510 3 9679 3 1014 1 4169 1 8502 1 4172 1 13283 1 11959 1 10893 1 6389 1 13924 1 9010 1 14841 1 5891 1 9420 2 12620 6 11964 3 9422 1 12878 7 barbarous 869 1 11290 1 12643 1 3271 1 10397 1 2311 1 12141 1 4982 1 13552 1 3205 1 11727 1 8739 1 3779 2 9546 1 11031 2 2438 2 5876 1 3741 1 12249 1 2581 1 13735 1 5403 1 6760 1 8811 1 9535 1 3815 1 9212 1 16316 5 15856 1 12620 1 13870 2 7170 1 12657 1 sarraceniaceae 13422 1 12266 1 12711 1 7744 1 1868 1 11414 1 bituminous_coal 5442 1 5658 1 9470 1 4433 1 3793 1 14674 1 14010 1 4469 1 3286 1 7343 1 13187 1 6235 1 7911 1 12983 1 10513 1 3777 1 12837 1 5354 1 9728 1 5126 1 3684 1 5390 1 1009 2 12353 1 11261 1 16143 1 16317 7 6792 1 6172 1 ecstatically 15517 1 12556 1 7574 1 carnevals 4198 1 10418 1 1730 1 in_the_final_analysis 2797 1 1130 1 1510 1 9719 1 wedding_guest 5226 1 6713 1 13428 1 13552 1 12363 1 759 1 15932 1 7012 1 9613 1 6946 1 5180 1 12863 1 11572 1 neglected 8116 1 5184 1 5120 1 7154 1 6860 1 15925 1 12623 1 5265 1 9886 1 15132 1 3135 1 1301 1 520 1 6655 2 8084 1 4590 1 4212 1 1362 1 4531 1 14159 1 2931 1 16050 1 1595 1 14547 1 15959 1 11406 2 12795 1 9334 1 16387 1 6243 1 7169 1 2203 1 9607 1 14829 1 8671 1 3714 1 12643 1 529 1 3113 1 5305 1 13689 1 7925 1 4031 1 13175 1 7836 1 2451 1 4881 1 3740 3 1646 1 13374 1 9677 1 12776 1 16398 1 3022 1 1014 1 11512 1 14838 1 9485 1 3382 1 5034 1 7563 1 11206 1 6850 1 15251 1 11282 1 12161 1 4118 1 12352 1 12620 1 2016 1 16319 12 6894 2 9920 1 dehiscent 15141 1 8012 1 13528 1 6626 1 7976 1 7736 1 13708 1 7900 1 8519 1 1893 1 12326 1 11647 1 12786 1 1138 1 7904 2 9950 1 5754 1 10094 1 5449 1 6820 1 12353 1 3699 1 2751 1 8366 2 4698 1 obliterating 14155 1 10136 1 15103 1 827 1 bessemer_process 5729 1 15198 1 12360 1 3336 1 1907 1 5271 1 15692 1 11394 1 9077 1 crudeness 11614 2 12643 1 12284 1 1333 1 14207 1 4499 1 3 1 7469 1 9593 1 4490 1 15399 1 9479 1 4813 1 8512 1 13576 1 12270 1 2405 1 7178 1 4628 1 6890 2 9644 1 1595 1 13232 1 8180 1 12810 1 7544 1 6518 1 6029 1 12105 1 13171 1 9920 1 oil_production 8777 1 9285 1 10335 1 1134 1 5228 1 12848 1 lettuce 15500 1 14112 1 3750 1 16320 24 6364 1 10310 4 8007 1 14606 1 15230 1 11075 1 3392 1 1115 1 12786 1 14288 1 3578 2 14782 1 3676 1 8309 1 11395 1 13063 1 15201 1 6369 2 4987 2 10668 1 10319 1 8012 2 8886 1 5494 1 5587 1 14351 1 2152 1 5903 2 3409 1 6308 1 789 1 12856 1 12147 3 8607 1 5422 1 5562 1 152 1 3760 1 14549 1 16087 1 11830 4 447 1 6146 1 7731 1 12862 1 7486 4 8581 1 11474 1 3038 1 14859 2 15675 1 1247 1 16459 1 10965 2 4692 1 15062 1 13626 1 2127 1 5997 1 6411 1 5504 1 6287 1 10561 1 12031 1 4143 7 2062 3 16224 1 15184 1 13888 1 16366 1 4907 2 13656 1 14911 1 6419 2 12246 2 5253 1 10303 1 14010 1 13118 1 9582 2 5954 1 1447 1 12384 1 16494 1 9945 1 2291 1 13845 1 14251 1 9081 1 7044 1 6389 1 68 3 6954 1 14047 1 4365 1 2349 1 2986 2 11287 1 10721 1 serial_music 5442 1 11760 1 7729 1 211 1 13260 1 5653 1 15230 2 4761 1 9221 1 15728 1 3839 1 14106 1 8020 1 12278 1 13273 1 590 1 14911 1 13482 1 11312 1 sparge 1584 1 7657 1 211 1 12803 1 7790 1 3332 2 12836 1 6894 1 bowdlerize 16211 1 3736 1 5620 1 1689 1 1510 1 14047 1 566 1 4912 1 cypriote 5105 1 717 1 class_larvacea 11147 1 candelilla 11746 1 11327 1 12326 1 13003 1 2873 1 conspicuously 0 1 1330 1 13486 1 13609 1 16145 1 14975 1 14383 1 327 1 7371 1 16322 14 14196 1 15047 1 12044 1 14854 1 2262 1 9021 1 14980 1 5083 1 4718 1 16008 1 7762 1 13169 1 10358 1 3617 1 9896 1 1667 1 7244 1 12569 1 1209 1 1541 1 9575 1 906 1 16459 1 4352 1 3258 1 12372 1 7834 1 12574 1 8362 1 3421 1 7587 1 1742 1 9172 1 16398 1 5474 1 15219 1 11576 1 15279 1 595 1 2859 1 15553 1 7904 1 6790 1 5110 1 5983 1 11584 1 12585 1 13186 1 11520 1 4263 1 13130 1 12849 1 494 1 4743 1 988 1 2805 1 13583 1 9531 1 8792 1 8547 1 12270 1 4680 1 2152 1 8796 1 8314 1 1624 1 6929 1 16051 1 3644 1 13648 2 2366 1 3816 1 10674 1 11531 1 14828 1 7798 1 11474 1 7420 1 8438 1 1635 1 4030 1 16487 1 14623 3 3145 1 4934 3 13652 1 15110 1 3293 1 15025 1 8137 1 2241 1 9479 2 9138 1 5360 2 2881 1 953 1 12033 1 3529 1 12942 1 1014 1 3599 1 8325 1 3087 1 8559 1 185 1 4701 1 1257 1 1988 1 12101 1 13082 1 9945 1 11684 1 4705 1 12810 2 4707 1 5514 1 3773 1 4516 1 14189 1 burhinidae 9910 1 14507 1 1457 1 irrelevancies 12536 1 4539 1 14884 1 2879 1 3839 1 film_director 11183 1 6786 1 8108 1 7729 2 12231 1 3483 2 11519 1 15717 1 7049 1 14838 1 14346 1 1180 1 6168 1 12534 1 414 1 4110 1 16283 1 7539 1 16323 5 10154 1 3818 1 9817 1 adjust 12297 1 7368 1 6336 1 11349 1 16448 1 15996 1 15470 1 3245 1 10541 1 2699 1 15695 1 7302 1 16079 1 7505 2 519 1 3839 2 15827 2 6830 1 9953 1 7759 1 13168 1 14578 1 16325 44 6655 2 13354 1 2838 2 892 1 5268 1 6530 1 3404 3 9021 2 11760 1 15831 1 2121 2 15958 1 13093 1 9704 1 6343 1 4957 1 1593 1 7189 1 16329 1 14651 1 4648 1 5457 1 15330 1 14857 3 10546 2 11154 2 8163 1 3552 1 13099 2 2846 1 14132 1 3918 1 11698 1 1093 1 13171 1 2394 1 8408 1 16221 1 15907 2 7080 1 15704 1 13299 1 2719 1 13997 2 7318 1 12059 1 4353 1 585 1 13837 1 6910 1 7587 1 4907 2 11105 1 13839 1 9723 1 8469 1 14940 1 14525 1 536 1 11572 3 12578 1 8292 1 12973 1 11782 1 15421 1 3194 1 7900 2 7148 1 9971 1 6294 1 1893 1 1749 1 11380 1 1169 1 1957 1 1053 1 8298 3 14020 1 9448 1 15659 1 9978 1 7464 1 14340 1 9920 4 12522 1 5555 1 15858 1 9849 1 12393 1 15502 1 9983 1 8653 1 2802 1 7912 1 3701 1 11461 1 14480 1 12145 2 9116 1 15729 2 10506 1 10664 1 13903 1 13526 1 6736 1 3874 2 6170 1 3640 1 15093 1 16237 1 11188 1 7667 1 436 1 3138 1 8382 1 14352 2 8314 1 1064 1 11327 1 8246 1 3815 1 6443 1 8799 1 996 1 15106 1 15933 1 14892 1 13263 2 4991 2 14828 1 5813 1 15572 1 5245 1 13965 1 7169 1 7486 1 2036 1 3076 1 1920 2 9339 1 8438 1 13209 1 4164 3 3715 1 8131 1 14691 1 11197 1 11878 4 4761 1 8869 1 11479 1 8930 1 15676 1 9203 1 240 1 3223 2 2822 1 13008 2 2881 1 11339 1 5056 1 2425 1 7494 2 15940 1 2945 1 2760 1 3601 1 15459 1 14913 1 1134 1 4169 1 10340 1 6890 1 566 1 3971 1 2951 1 16494 1 6328 1 13480 1 15390 1 15579 1 4706 1 8393 1 68 1 15191 1 14044 1 11686 1 13608 1 12620 2 14047 1 1389 1 11427 1 13813 1 6894 1 6196 1 gooseberry 11527 1 421 1 10742 1 1237 1 7904 1 5455 2 9047 1 4954 1 diagnostic 10470 1 11119 2 12297 1 6610 1 13668 3 16182 1 13158 1 10757 1 15395 1 1225 2 10660 1 9219 1 7911 3 8156 1 5986 1 5519 1 2597 1 12848 2 5926 3 11523 3 3170 2 3206 1 1420 1 5347 1 4951 2 6 1 8309 3 2627 1 5987 1 16080 1 16324 29 3989 3 12271 2 15235 1 2359 1 10800 1 12669 1 14579 1 7344 1 3813 1 12898 2 7636 1 10195 1 1091 1 5679 2 8021 1 8489 1 998 1 10198 1 7244 1 8580 1 8801 1 6375 1 14271 3 11534 1 13914 1 13209 5 4379 1 12732 2 13299 1 15987 1 15540 1 8867 1 14469 2 7523 1 343 1 3014 1 7002 1 15935 1 2609 1 11739 1 4110 2 12374 1 2212 2 5573 2 9970 6 694 1 1352 1 4041 8 12249 2 5475 1 7649 1 1955 1 14218 2 1260 1 11139 2 8180 1 14703 1 14147 1 15719 6 14969 5 3901 1 3534 3 4707 1 7066 1 12416 1 1055 1 5694 1 5766 1 14636 1 royal_road 8137 1 10492 1 7021 1 2581 1 11188 1 weakly 5958 1 1487 1 12458 3 9186 1 7724 1 4263 1 5346 3 7471 1 2436 2 3676 1 8699 3 9453 4 15602 1 14883 1 13583 1 11249 1 16326 17 9153 1 2467 1 10253 1 5455 1 5901 1 5991 1 1088 1 3992 1 6498 1 16124 1 14929 1 1309 1 1625 1 8967 1 12502 1 15297 1 5422 1 13360 1 15738 1 1466 3 3684 1 15986 1 12964 1 7350 3 7420 3 869 1 56 1 9635 1 6598 1 14555 1 2128 1 5505 1 4761 1 13970 1 6287 1 10559 1 14557 1 4143 1 11227 1 1645 1 510 1 6977 1 12410 1 2945 4 12192 1 3303 1 10106 3 1560 1 5034 1 6520 1 1685 1 7435 1 14972 1 dyson 12643 1 3741 1 16316 1 11031 2 13552 1 2438 2 6760 1 3205 1 copiously 14681 1 15724 1 14980 1 16350 1 6175 1 15840 1 7360 1 16465 1 8698 1 13086 1 7471 1 3959 1 4035 1 3328 1 divide 11966 1 6011 1 8507 1 3241 2 7365 1 6266 1 3095 1 8008 1 14056 1 15634 1 9821 1 12110 2 13422 2 15759 1 10762 1 15897 2 14712 2 3908 1 6527 1 257 1 15900 1 5380 1 2960 1 4061 1 14855 5 9955 1 3546 1 3404 1 7376 1 14980 7 5779 1 13293 1 10226 1 634 1 767 1 8631 1 7882 1 14584 1 7636 1 14585 1 9709 1 10901 2 3684 1 13432 1 8021 1 1667 14 13434 4 4534 1 10361 1 4069 2 8409 1 8166 1 4659 1 3685 1 4660 1 5275 1 10628 1 5172 1 10629 1 2722 1 2725 1 7387 1 15776 1 779 1 14864 2 9169 1 11992 1 13575 2 4907 1 9723 2 15277 1 8034 1 13576 1 536 1 4296 2 1953 2 7256 1 9305 1 1047 1 12249 1 10782 1 5279 1 921 1 2581 1 658 1 5033 5 14473 1 12383 1 16405 1 9587 1 3432 1 7045 1 163 1 1562 2 3571 1 1687 1 3269 1 13312 3 13581 1 2861 1 543 1 9733 1 12524 2 7911 1 15788 1 2863 1 8787 2 11588 1 3134 1 1300 1 3439 1 7661 1 2598 2 11043 1 10665 1 5807 1 13855 1 6798 1 931 1 11865 3 13712 1 12270 1 4925 3 3138 1 14351 4 8663 1 7789 2 2229 1 12792 1 6309 1 11327 1 5810 1 14616 2 2747 1 14236 1 935 1 12400 1 12674 1 6686 1 14892 1 7796 1 2605 1 4687 1 8668 1 15572 1 8316 1 8188 1 10131 3 2751 1 11474 1 300 1 4933 1 9608 1 9996 1 7802 1 11330 1 12284 1 3145 2 16056 4 1707 1 9477 1 4822 1 62 1 2754 1 4110 1 8192 1 452 2 14908 1 10403 1 15940 5 10937 1 15459 2 8942 3 2613 1 3158 1 14251 2 9081 1 11896 2 698 1 5694 1 13738 1 817 1 10018 2 16318 1 11612 1 3162 1 2110 1 8332 1 11614 1 15818 1 9882 1 9359 1 3164 2 8697 1 11349 1 6204 1 14640 2 2896 1 10541 1 3477 2 6080 1 1458 1 3030 1 11619 1 6830 1 8202 1 6962 2 8456 1 5449 1 5584 1 15472 3 7943 1 7442 1 16327 1 3481 1 5586 1 1460 1 2263 5 16328 98 6592 1 12430 1 15473 1 13231 1 15699 1 3493 1 13099 3 2005 1 15701 1 14393 1 2394 1 12569 5 14270 1 1737 1 14802 1 10430 1 15591 1 10706 3 15843 1 3738 1 2911 1 4352 1 6226 1 1475 1 3188 1 10561 1 1215 1 4143 1 12441 1 15846 1 4354 1 11228 1 1099 1 14525 1 12578 6 9639 3 2509 1 14010 1 12973 1 2135 1 8980 2 11646 1 5228 2 5104 1 6979 2 1893 1 12067 1 1107 1 2797 1 7332 2 7464 1 4977 1 10182 1 1903 1 5733 1 15860 1 10859 1 14287 4 2802 1 7210 2 9116 1 16118 1 6238 3 7473 1 5988 1 5616 2 6109 1 2806 1 3875 1 4870 1 14290 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13079 1 13606 1 4324 1 3605 1 13607 1 9877 1 10144 2 4706 2 15191 5 12161 1 13084 1 12953 1 14636 2 1861 1 7436 1 13482 4 3834 1 6582 1 11900 1 4713 1 14918 1 2255 1 12165 1 7825 1 14387 1 5586 1 11495 5 2122 1 11087 2 6090 3 9506 3 1733 1 7581 1 8209 2 14658 1 4723 1 16332 1 4466 3 11367 1 1211 1 11365 1 16455 63 7708 1 14398 1 5716 1 3185 1 6601 1 585 1 971 1 14941 3 1604 1 16227 1 4475 1 8108 1 8223 2 8718 4 2916 1 12448 2 1607 1 13380 1 14018 2 15498 1 4363 1 2921 1 9790 1 14946 1 7723 1 5345 2 9113 2 12200 1 13525 1 605 2 12848 7 6989 1 496 1 15093 1 362 3 12465 1 498 1 3072 1 4376 1 3878 4 866 1 12592 1 16356 1 6623 1 10325 2 5500 1 15105 1 9663 1 14954 2 8128 2 4495 1 14956 3 1635 1 10741 2 7989 1 4501 1 11133 2 9546 1 15115 1 5888 1 2173 1 7226 3 14307 1 9679 1 11002 1 10212 1 1017 1 1136 3 1260 1 141 2 11684 1 13013 1 5366 1 12102 2 6389 1 15624 1 10343 3 14044 2 10216 2 1656 1 14047 1 6894 1 repugnance 1784 1 3500 1 62 1 6557 1 15886 1 2598 1 1040 1 6389 1 13684 1 2001 1 8651 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1 12395 2 3676 1 7956 1 451 1 7574 1 14196 1 7942 1 16435 1 12268 1 6708 1 11956 2 5295 1 11108 1 7634 1 16465 1 16258 1 10442 1 3489 1 9081 1 12795 1 9490 1 13812 1 2381 1 9148 1 1685 1 3536 1 2921 1 14972 1 14189 1 7384 1 7350 2 spittlebug 1560 1 6442 1 8169 1 9879 1 7772 1 897 1 vacuum_pump 7421 1 4169 1 13034 1 10421 1 10816 1 197 1 7030 1 4464 1 9153 1 12059 1 oxalic_acid 10906 3 719 2 1183 1 9084 1 15430 1 14209 1 13367 1 8353 1 4290 1 12792 1 11831 1 1595 1 12059 1 narcotized 996 1 3706 1 462 1 15501 2 12590 1 10742 1 14236 1 6264 1 lower_jaw 1576 1 2971 1 5749 1 4933 1 6502 1 3390 1 1738 1 8541 1 9635 1 16460 15 6796 1 16431 2 12800 1 10295 1 5396 1 15759 1 4143 1 2598 2 3907 1 1907 1 11228 1 2755 1 5616 2 5379 1 11171 3 5016 1 15887 1 8325 1 7789 1 5179 1 9532 1 16418 1 9971 1 11928 2 13935 2 13718 3 14584 1 977 2 7120 1 3881 1 15058 2 16087 2 1935 1 2089 2 7024 1 10259 1 10131 4 14189 1 13363 2 11473 1 10719 1 13504 1 deviant 10376 1 4707 1 13512 1 5060 1 4907 1 12506 1 4495 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16464 5 13064 1 4169 1 12616 1 16036 1 12653 1 13684 1 1667 1 5003 1 6178 1 7314 1 4087 1 4177 1 7014 3 10289 1 length_of_service 13881 1 14855 1 14009 1 12557 1 bloomy 11008 1 6681 1 13806 1 3875 1 7321 1 12738 1 11133 1 branched 5144 1 7015 1 11431 1 8879 2 4393 1 15994 1 13669 1 5771 1 884 1 15757 1 4179 1 254 2 10346 3 14056 1 13821 2 885 1 1799 1 6133 2 2436 12 2190 1 6020 1 3676 1 9284 1 9285 1 14196 2 2560 1 14458 1 6656 1 16003 1 7019 1 4529 1 14714 1 8012 8 5016 1 8886 2 13681 1 16004 1 15404 7 5901 2 13682 1 16156 1 7634 1 14065 1 897 1 3683 1 3684 1 8021 2 2198 1 1667 4 5783 2 13434 1 1818 2 10094 2 5906 2 2971 1 12643 1 8894 4 10628 1 12644 1 5173 5 3258 4 13834 1 779 2 14470 1 8642 1 6785 1 6419 2 4296 1 14076 2 2578 3 2980 1 1409 2 7900 1 2581 1 1558 1 8774 1 1560 2 13309 1 8180 1 159 2 7904 3 2590 1 12518 2 1685 1 13848 2 3571 1 3699 2 284 1 3436 2 7657 1 5555 1 8047 1 12263 2 9186 7 11859 1 14606 1 14607 1 12786 3 1836 2 5927 1 7159 2 12395 1 4678 2 16411 2 13461 1 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7437 1 7124 1 amylolysis 897 1 3223 1 slushy 1513 1 8233 1 4202 1 3367 1 2950 1 4912 1 6630 1 15518 2 5087 1 7897 1 pitched_battle 7466 1 8334 1 10868 1 10722 1 3126 1 13463 1 5743 1 14322 1 1136 1 5271 1 5148 1 13749 1 804 1 6035 1 7563 1 126 1 11686 1 15115 1 7435 1 201 1 7665 1 1982 1 battle_of_gettysburg 16398 1 3350 1 1578 1 7149 1 1279 1 11903 1 5988 1 9664 1 12372 1 362 1 7914 1 6757 1 consumption 6011 1 7068 1 15818 1 3388 1 1084 1 6958 1 8156 1 6204 2 13674 1 1395 1 5075 1 14979 3 4951 1 13745 5 10543 4 6962 3 8398 1 14922 2 12889 1 5702 1 12961 1 9429 1 892 1 8458 1 3404 2 7700 3 10483 1 85 1 9764 1 12562 1 7189 1 2563 1 12563 1 2273 1 5386 1 8282 1 7636 1 13750 1 6144 1 6145 1 710 1 2055 1 13878 1 1667 1 13031 1 10770 3 1596 4 1208 1 90 1 6536 1 9830 1 906 2 11773 2 9833 1 1213 1 8173 1 3258 1 4539 1 21 1 719 1 13371 1 4540 1 10299 1 9038 1 12512 1 3501 1 16341 1 7774 1 2646 1 2136 1 15492 1 14336 1 11512 1 16172 1 10044 1 8223 1 2858 1 538 1 15279 3 5228 1 12383 1 2139 1 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4143 2 136 1 12744 1 14980 1 14154 1 6592 1 7239 1 8980 1 3173 1 7234 1 keyboard 387 2 14315 1 15692 1 6336 4 6337 1 12556 1 4950 2 12110 1 13021 2 15897 1 11294 1 7375 1 8512 1 8277 1 2960 3 4187 1 4530 1 1877 1 6026 1 15833 1 11156 2 13430 1 14391 1 3918 1 4001 1 6596 1 8408 1 5169 1 4464 1 3497 1 2499 1 15774 1 836 2 16390 1 14136 1 2720 1 3556 1 477 1 15647 1 10167 1 7587 1 11312 1 8840 1 14077 1 8716 1 918 1 14009 1 4608 1 11782 1 11646 1 3744 1 16174 1 2581 1 9912 1 13578 1 14738 1 14015 3 4975 1 12007 1 9728 1 1054 1 14018 1 9920 1 5923 1 13314 1 11653 1 16471 1 9186 1 2300 1 9258 1 7210 1 7536 1 9394 1 7340 1 4211 1 611 3 3951 1 14888 1 12669 1 6176 1 8796 2 5244 1 8246 2 6442 1 3816 1 1631 1 14892 6 15804 1 13263 1 9860 1 11665 2 6445 1 3076 1 299 1 9996 1 4933 1 448 1 4761 3 8555 1 12534 2 4762 1 7614 1 11884 1 2476 1 12094 1 9073 1 13273 1 5756 1 14911 1 4998 3 13218 1 15248 1 13278 1 566 1 16493 24 4172 1 12159 4 8814 1 15251 1 13344 1 8561 1 15752 1 5317 1 14108 1 6894 1 3238 1 in_league 12568 1 4529 1 6530 1 13386 1 2757 1 4425 1 3992 1 truce 3531 1 14855 1 300 1 1344 1 3113 1 6498 1 16092 1 2071 1 9774 1 14317 1 3542 1 5499 1 8881 2 1629 1 2231 1 11686 2 7800 1 11500 1 5181 1 11182 1 unveiled 5545 2 5498 2 7774 2 13829 2 16251 2 11266 2 15875 2 12078 2 7425 2 straggly 12786 1 13391 1 1667 1 467 1 9263 1 9705 1 9148 1 4907 1 8140 1 10346 1 15980 1 13762 1 6727 2 227 1 pyrimid 1836 1 763 1 15940 1 15860 2 373 1 11474 1 6644 2 folk_music 13314 2 11551 1 9921 2 9359 1 253 1 13672 1 9221 6 11971 1 12848 2 10540 1 13490 2 16080 1 15200 1 6367 1 2046 1 13229 1 293 1 13617 1 9955 1 7729 2 15329 1 9291 1 14234 1 3642 1 5271 1 10325 1 13648 1 13395 1 3816 2 15933 1 14892 2 12020 1 15804 1 12504 1 4723 1 1208 1 2848 3 6801 2 3732 1 10679 1 2161 1 11952 1 6035 1 11270 1 12939 1 15940 1 4998 2 6102 3 4202 1 14998 1 12614 1 15462 2 14105 1 1413 1 16177 1 10602 1 14018 1 16496 13 14108 1 1834 1 6391 1 bandit 16008 1 15817 2 --Vectors End-- WordNet-Similarity-2.05/lib/WordNet/Similarity/0000755000175000017500000000000011025552366017551 5ustar sidsidWordNet-Similarity-2.05/lib/WordNet/Similarity/lch.pm0000644000175000017500000003202211015336622020646 0ustar sidsid# WordNet::Similarity::lch.pm version 2.04 # (Last update $Id: lch.pm,v 1.24 2008/03/27 06:21:17 sidz1979 Exp $) # # Semantic Similarity Measure package implementing the measure # described by Leacock and Chodorow (1998). # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # Jason Michelizzi, Univeristy of Minnesota Duluth # mich0212 at d.umn.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity::lch; =head1 NAME WordNet::Similarity::lch - Perl module for computing semantic relatedness of word senses using the method described by Leacock and Chodorow (1998). =head1 SYNOPSIS use WordNet::Similarity::lch; use WordNet::QueryData; my $wn = WordNet::QueryData->new(); my $myobj = WordNet::Similarity::lch->new($wn); my $value = $myobj->getRelatedness("car#n#1", "bus#n#2"); ($error, $errorString) = $myobj->getError(); die "$errorString\n" if($error); print "car (sense 1) <-> bus (sense 2) = $value\n"; =head1 DESCRIPTION This module computes the semantic relatedness of word senses according to a method described by Leacock and Chodorow (1998). This method counts up the number of edges between the senses in the 'is-a' hierarchy of WordNet. The value is then scaled by the maximum depth of the WordNet 'is-a' hierarchy. A relatedness value is obtained by taking the negative log of this scaled value. =head2 Methods =over =cut use strict; use Exporter; use WordNet::Similarity::DepthFinder; our @ISA = qw/WordNet::Similarity::DepthFinder/; our $VERSION = '2.04'; =item $lch->setPosList() This method is internally called to determine the parts of speech this measure is capable of dealing with. Parameters: none. Returns: none. =cut sub setPosList { my $self = shift; $self->{n} = 1; $self->{v} = 1; } =item $lch->getRelatedness ($synset1, $synset2) Computes the relatedness of two word senses using a node counting scheme. For details on how relatedness is computed, see the Discussion section below. Parameters: two word senses in "word#pos#sense" format. Returns: Unless a problem occurs, the return value is the relatedness score. If no path exists between the two word senses, then a large negative number is returned. If an error occurs, then the error level is set to non-zero and an error string is created (see the description of getError()). Note: the error level will also be set to 1 and an error string will be created if no path exists between the words. =cut sub getRelatedness { my $self = shift; my $wps1 = shift; my $wps2 = shift; my $wn = $self->{wn}; my $class = ref $self || $self; unless ($wn) { $self->{errorString} .= "\nError (${class}::getRelatedness()) - "; $self->{errorString} .= "A WordNet::QueryData object is required."; $self->{error} = 2; return undef; } # Initialize traces. $self->{traceString} = ""; # JM 1-21-04 # moved input validation code to parseWps() in a super-class my $ret = $self->parseWps ($wps1, $wps2); ref $ret or return $ret; my ($word1, $pos1, $sense1, $offset1, $word2, $pos2, $sense2, $offset2) = @{$ret}; my $pos = $pos1; # Now check if the similarity value for these two synsets is in # fact in the cache... if so return the cached value. my $relatedness = $self->{doCache} ? $self->fetchFromCache ($wps1, $wps2) : undef; defined $relatedness and return $relatedness; # Now get down to really finding the relatedness of these two. # JM 3-9-04 # Modified to use the methods of DepthFinder et al. my @LCSs = $self->getLCSbyPath ($offset1, $offset2, $pos1, 'offset'); # check if there is no path between synsets unless (defined $LCSs[0]) { return $self->UNRELATED; } # find the LCS (well, path really) that is in the deepest taxonomy my $maxdepth = -1; my $length; foreach (@LCSs) { my $lcs; ($lcs, $length) = @{$_}; my @roots = $self->getTaxonomies ($lcs, $pos1); foreach my $root (@roots) { my $depth = $self->getTaxonomyDepth ($root, $pos1); unless (defined $depth) { $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .="\nWarning (${class}::getRelatedness()) - "; $self->{errorString} .= "Taxonomy depth for $root undefined."; return undef; } $maxdepth = $depth if $depth > $maxdepth; } } if ($maxdepth <= 0) { $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::getRelatedness()) - "; $self->{errorString} .= "Max depth of taxonomy is not positive."; return undef; } my $score = log (2 * $maxdepth / $length); $self->storeToCache ($offset1, $offset2, $score); return $score; } 1; __END__ =back =head2 Discussion The relatedness measure proposed by Leacock and Chodorow is S<-log (length / (2 * D))>, where length is the length of the shortest path between the two synsets (using node-counting) and D is the maximum depth of the taxonomy. The fact that the lch measure takes into account the depth of the taxonomy in which the synsets are found means that the behavior of the measure is profoundly affected by the presence or absence of a unique root node. If there is a unique root node, then there are only two taxonomies: one for nouns and one for verbs. All nouns, then, will be in the same taxonomy and all verbs will be in the same taxonomy. D for the noun taxonomy will be somewhere around 18, depending upon the version of WordNet, and for verbs, it will be 14. If the root node is not being used, however, then there are nine different noun taxonomies and over 560 different verb taxonomies, each with a different value for D. If the root node is not being used, then it is possible for synsets to belong to more than one taxonomy. For example, the synset containing turtledove#n#2 belongs to two taxonomies: one rooted at group#n#1 and one rooted at entity#n#1. In such a case, the relatedness is computed by finding the LCS that results in the shortest path between the synsets. The value of D, then, is the maximum depth of the taxonomy in which the LCS is found. If the LCS belongs to more than one taxonomy, then the taxonomy with the greatest maximum depth is selected (i.e., the largest value for D). =head2 Usage The semantic relatedness modules in this distribution are built as classes that define the following methods: new() getRelatedness() getError() getTraceString() See the WordNet::Similarity(3) documentation for details of these methods. =head3 Typical Usage Examples To create an object of the lch measure, we would have the following lines of code in the Perl program. use WordNet::Similarity::lch; $measure = WordNet::Similarity::lch->new($wn, '/home/sid/lch.conf'); The reference of the initialized object is stored in the scalar variable '$measure'. '$wn' contains a WordNet::QueryData object that should have been created earlier in the program. The second parameter to the 'new' method is the path of the configuration file for the lch measure. If the 'new' method is unable to create the object, '$measure' would be undefined. This, as well as any other error/warning may be tested. die "Unable to create object.\n" if(!defined $measure); ($err, $errString) = $measure->getError(); die $errString."\n" if($err); To find the semantic relatedness of the first sense of the noun 'car' and the second sense of the noun 'bus' using the measure, we would write the following piece of code: $relatedness = $measure->getRelatedness('car#n#1', 'bus#n#2'); To get traces for the above computation: print $measure->getTraceString(); However, traces must be enabled using configuration files. By default traces are turned off. =head1 CONFIGURATION FILE The behavior of the measures of semantic relatedness can be controlled by using configuration files. These configuration files specify how certain parameters are initialized within the object. A configuration file may be specified as a parameter during the creation of an object using the new method. The configuration files must follow a fixed format. Every configuration file starts with the name of the module ON THE FIRST LINE of the file. For example, a configuration file for the WordNet::Similarity::lch module will have on the first line 'WordNet::Similarity::lch'. This is followed by the various parameters, each on a new line and having the form 'name::value'. The 'value' of a parameter is optional (in case of boolean parameters). In case 'value' is omitted, we would have just 'name::' on that line. Comments are supported in the configuration file. Anything following a '#' is ignored till the end of the line. The module parses the configuration file and recognizes the following parameters: =over =item trace The value of this parameter specifies the level of tracing that should be employed for generating the traces. This value is an integer equal to 0, 1, or 2. If the value is omitted, then the default value, 0, is used. A value of 0 switches tracing off. A value of 1 or 2 switches tracing on. A trace level of 1 means the synsets are represented as word#pos#sense strings, while for level 2, the synsets are represented as word#pos#offset strings. =item cache The value of this parameter specifies whether or not caching of the relatedness values should be performed. This value is an integer equal to 0 or 1. If the value is omitted, then the default value, 1, is used. A value of 0 switches caching 'off', and a value of 1 switches caching 'on'. =item maxCacheSize The value of this parameter indicates the size of the cache, used for storing the computed relatedness value. The specified value must be a non-negative integer. If the value is omitted, then the default value, 5,000, is used. Setting maxCacheSize to zero has the same effect as setting cache to zero, but setting cache to zero is likely to be more efficient. Caching and tracing at the same time can result in excessive memory usage because the trace strings are also cached. If you intend to perform a large number of relatedness queries, then you might want to turn tracing off. =item rootNode The value of this parameter indicates whether or not a unique root node should be used. In WordNet, there is no unique root node for the noun and verb taxonomies. If this parameter is set to 1 (or if the value is omitted), then certain measures (wup, path, lch, res, lin, and jcn) will "fake" a unique root node. If the value is set to 0, then no unique root node will be used. If the value is omitted, then the default value, 1, is used. =item taxonomyDepthsFile The value for this parameter should be a string that specifies the location of a taxonomy depths file (as generated by wnDepths.pl). If no path is specified, then the default file is used, which was generated when the Similarity package was installed. =back =head1 SEE ALSO perl(1), WordNet::Similarity(3), WordNet::QueryData(3) http://www.cs.utah.edu/~sidd http://wordnet.princeton.edu http://www.ai.mit.edu/~jrennie/WordNet http://groups.yahoo.com/group/wn-similarity =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Jason Michelizzi, University of Minnesota Duluth mich0212 at d.umn.edu =head1 BUGS None. To report bugs, go to http://groups.yahoo.com/group/wn-similarity/ or e-mail tpederse at d.umn.edu. =head1 COPYRIGHT AND LICENSE Copyright (c) 2005, Ted Pedersen, Siddharth Patwardhan and Jason Michelizzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/Similarity/lin.pm0000644000175000017500000002722211015336622020670 0ustar sidsid# WordNet::Similarity::lin.pm version 2.04 # (Last updated $Id: lin.pm,v 1.22 2008/03/27 06:21:17 sidz1979 Exp $) # # Semantic Similarity Measure package implementing the measure # described by Lin (1998). # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # Jason Michelizzi, Univeristy of Minnesota Duluth # mich0212 at d.umn.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity::lin; =head1 NAME WordNet::Similarity::lin - Perl module for computing semantic relatedness of word senses using the information content based measure described by Lin (1998). =head1 SYNOPSIS use WordNet::Similarity::lin; use WordNet::QueryData; my $wn = WordNet::QueryData->new(); my $mymeasure = WordNet::Similarity::lin->new($wn); my $value = $mymeasure->getRelatedness("car#n#1", "bus#n#2"); ($error, $errorString) = $mymeasure->getError(); die "$errorString\n" if($error); print "car (sense 1) <-> bus (sense 2) = $value\n"; =head1 DESCRIPTION Lin (1998) describes a method to compute the semantic relatedness of word senses using the information content of the concepts in WordNet and the 'Similarity Theorem' (also described in the paper). This module implements this measure of semantic relatedness of concepts. =over =cut use strict; use WordNet::Similarity::ICFinder; our @ISA = qw(WordNet::Similarity::ICFinder); our $VERSION = '2.04'; =item $lin->getRelatedness ($synset1, $synset1) Computes the relatedness of two word senses using an information content scheme. The relatedness is equal to twice the information content of the LCS divided by the sum of the information content of each input synset. Parameters: two word senses in "word#pos#sense" format. Returns: Unless a problem occurs, the return value is the relatedness score. If no path exists between the two word senses, then a large negative number is returned. If an error occurs, then the error level is set to non-zero and an error string is created (see the description of getError()). Note: the error level will also be set to 1 and an error string will be created if no path exists between the words. =cut sub getRelatedness { my $self = shift; my $wps1 = shift; my $wps2 = shift; my $wn = $self->{'wn'}; my $class = ref $self || $self; # Check the existence of the WordNet::QueryData object. unless ($wn) { $self->{errorString} .= "\nError (${class}::getRelatedness()) - "; $self->{errorString} .= "A WordNet::QueryData object is required."; $self->{error} = 2; return undef; } # Initialize traces. $self->{traceString} = ""; # JM 1-21-04 # moved input validation code to WordNet::Similarity::parseInput() my $ret = $self->parseWps ($wps1, $wps2); ref $ret or return $ret; my ($word1, $pos1, $sense1, $offset1, $word2, $pos2, $sense2, $offset2) = @{$ret}; my $synset1 = "$word1#$pos1#$sense1"; my $synset2 = "$word2#$pos2#$sense2"; # Now check if the similarity value for these two synsets is in # fact in the cache... if so return the cached value. my $relatedness = $self->{doCache} ? $self->fetchFromCache ($synset1, $synset2) : undef; defined $relatedness and return $relatedness; # Now get down to really finding the relatedness of these two. my @LCSs = $self->getLCSbyIC ($synset1, $synset2, $pos1, 'wps'); my $ref = shift @LCSs; unless (defined $ref) { return $self->UNRELATED; } my ($lcs, $lcsic) = @{$ref}; my $ic1 = $self->IC ($offset1, $pos1); my $ic2 = $self->IC ($offset2, $pos2); my $score = ($ic1 && $ic2) ? ((2 * $lcsic) / ($ic1 + $ic2)) : 0; # what does this do? $score = ($score == -1) ? 0 : $score; if ($self->{trace}) { $self->{traceString} .= "Concept1: $synset1 (IC="; $self->{traceString} .= sprintf ("%.6f", $ic1); $self->{traceString} .= ")\n"; $self->{traceString} .= "Concept2: $synset2 (IC="; $self->{traceString} .= sprintf ("%.6f", $ic2); $self->{traceString} .= ")\n"; } $self->{doCache} and $self->storeToCache ($wps1, $wps2, $score); return $score; } # JM 1-16-04 # moved subroutine _getLeastCommonSubsumers to ICFinder.pm 1; __END__ =back =head2 Discussion The relatedness value returned by the lin measure is a number equal to S<2 * IC(lcs) / (IC(synset1) + IC(synset2))>. Where IC(x) is the information content of x. One can observe, then, that the relatedness value will be greater-than or equal-to zero and less-than or equal-to one. If the information content of any of either synset1 or synset2 is zero, then zero is returned as the relatedness score, due to lack of data. Ideally, the information content of a synset would be zero only if that synset were the root node, but when the frequency of a synset is zero, we use the value of zero as the information content because of a lack of better alternatives. =head2 Usage The semantic relatedness modules in this distribution are built as classes that expose the following methods: new() getRelatedness() getError() getTraceString() See the WordNet::Similarity(3) documentation for details of these methods. =head3 Typical Usage Examples To create an object of the lin measure, we would have the following lines of code in the Perl program. use WordNet::Similarity::lin; $measure = WordNet::Similarity::lin->new($wn, '/home/sid/lin.conf'); The reference of the initialized object is stored in the scalar variable '$measure'. '$wn' contains a WordNet::QueryData object that should have been created earlier in the program. The second parameter to the 'new' method is the path of the configuration file for the lin measure. If the 'new' method is unable to create the object, '$measure' would be undefined. This, as well as any other error/warning may be tested. die "Unable to create object.\n" if(!defined $measure); ($err, $errString) = $measure->getError(); die $errString."\n" if($err); To find the semantic relatedness of the first sense of the noun 'car' and the second sense of the noun 'bus' using the measure, we would write the following piece of code: $relatedness = $measure->getRelatedness('car#n#1', 'bus#n#2'); To get traces for the above computation: print $measure->getTraceString(); However, traces must be enabled using configuration files. By default traces are turned off. =head1 CONFIGURATION FILE The behavior of the measures of semantic relatedness can be controlled by using configuration files. These configuration files specify how certain parameters are initialized within the object. A configuration file may be specified as a parameter during the creation of an object using the new method. The configuration files must follow a fixed format. Every configuration file starts with the name of the module ON THE FIRST LINE of the file. For example, a configuration file for the lin module will have on the first line 'WordNet::Similarity::lin'. This is followed by the various parameters, each on a new line and having the form 'name::value'. The 'value' of a parameter is optional (in case of boolean parameters). In case 'value' is omitted, we would have just 'name::' on that line. Comments are supported in the configuration file. Anything following a '#' is ignored till the end of the line. The module parses the configuration file and recognizes the following parameters: =over =item trace The value of this parameter specifies the level of tracing that should be employed for generating the traces. This value is an integer equal to 0, 1, or 2. If the value is omitted, then the default value, 0, is used. A value of 0 switches tracing off. A value of 1 or 2 switches tracing on. A trace level of 1 means the synsets are represented as word#pos#sense strings, while for level 2, the synsets are represented as word#pos#offset strings. =item cache The value of this parameter specifies whether or not caching of the relatedness values should be performed. This value is an integer equal to 0 or 1. If the value is omitted, then the default value, 1, is used. A value of 0 switches caching 'off', and a value of 1 switches caching 'on'. =item maxCacheSize The value of this parameter indicates the size of the cache, used for storing the computed relatedness value. The specified value must be a non-negative integer. If the value is omitted, then the default value, 5,000, is used. Setting maxCacheSize to zero has the same effect as setting cache to zero, but setting cache to zero is likely to be more efficient. Caching and tracing at the same time can result in excessive memory usage because the trace strings are also cached. If you intend to perform a large number of relatedness queries, then you might want to turn tracing off. =item rootNode The value of this parameter indicates whether or not a unique root node should be used. In WordNet, there is no unique root node for the noun and verb taxonomies. If this parameter is set to 1 (or if the value is omitted), then certain measures (wup, path, lch, res, lin, and jcn) will "fake" a unique root node. If the value is set to 0, then no unique root node will be used. If the value is omitted, then the default value, 1, is used. =item infocontent The value for this parameter should be a string that specifies the path of an information content file containing the frequency of occurrence of every WordNet concept in a large corpus. A number of utility programs are included in this distribution that can be used to generate an infocontent file (see utils.pod). If no path is specified, then the default infocontent file is used, which was generated from SemCor using the sense-tags. =back =head1 SEE ALSO perl(1), WordNet::Similarity(3), WordNet::QueryData(3) http://www.cs.utah.edu/~sidd http://wordnet.princeton.edu http://www.ai.mit.edu/~jrennie/WordNet http://groups.yahoo.com/group/wn-similarity =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Jason Michelizzi, University of Minnesota Duluth mich0212 at d.umn.edu =head1 COPYRIGHT AND LICENSE Copyright (c) 2005, Ted Pedersen, Siddharth Patwardhan and Jason Michelizzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/Similarity/GlossFinder.pm0000644000175000017500000004107511015336622022327 0ustar sidsid# WordNet::Similarity::GlossFinder version 2.04 # (Last updated $Id: GlossFinder.pm,v 1.13 2008/03/27 06:21:17 sidz1979 Exp $) # # Module containing gloss-finding code for the various measures of semantic # relatedness (lesk, vector). # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity::GlossFinder; =head1 NAME WordNet::Similarity::GlossFinder - module to implement gloss finding methods for WordNet::Similarity measures of semantic relatedness (specifically, lesk and vector) =head1 SYNOPSIS use WordNet::QueryData; my $wn = WordNet::QueryData->new; defined $wn or die "Construction of WordNet::QueryData failed"; use WordNet::Similarity::GlossFinder; my $obj = WordNet::Similarity::GlossFinder->new ($wn); my ($err, $errString) = $obj->getError (); $err and die $errString; my $wps1 = 'england#n#1'; my $wps2 = 'winston_churchill#n#1'; # get the glosses of these two synsets, since we are not using a # configuation file to specify relations, we will only get the # immediate glosses of the two wps entries. The default weight and # relation appear in $weight and $relation - these can be modified # via a configuration file. my ($wps1gloss, $wps2gloss, $weight, $relation ) = $obj -> getSuperGlosses ($wps1, $wps2); print "$wps1gloss->[0]\n"; print "$wps2gloss->[0]\n"; print "$weight->[0]\n"; print "$relation->[0]\n"; =head1 DESCRIPTION =head2 Introduction This class is derived from (i.e., is a sub-class of) WordNet::Similarity. Two of the measures of similarity, provided in this package, viz. WordNet::Similarity::lesk and WordNet::Similarity::vector deal with WordNet glosses. This module provides methods for easy access to the required glosses. =head2 Methods This module inherits all the methods of WordNet::Similarity. Additionally, the following methods are also defined. =head3 Public methods =over =cut use strict; use warnings; use WordNet::Similarity; use File::Spec; use WordNet::get_wn_info; our @ISA = qw/WordNet::Similarity/; our $VERSION = '2.04'; WordNet::Similarity::addConfigOption("relation", 0, "p", undef); WordNet::Similarity::addConfigOption("stop", 0, "p", undef); WordNet::Similarity::addConfigOption("stem", 0, "i", 0); =item $measure->setPosList(Z<>) Specifies the parts of speech that measures derived from this module support (namely, nouns, verbs, adjectives and adverbs). parameters: none returns: true =cut sub setPosList { my $self = shift; $self->{n} = 1; $self->{v} = 1; $self->{a} = 1; $self->{r} = 1; return 1; } =item $self->traceOptions(Z<>) Overrides method of same name in WordNet::Similarity. Prints module-specific configuration options to the trace string (if tracing is on). GlossFinder supports module specific options: relation, stop and stem. Parameters: none Returns: nothing =cut sub traceOptions { my $self = shift; $self->{traceString} .= "relation file :: ".((defined $self->{relation}) ? ($self->{relation}) : "")."\n"; $self->{traceString} .= "stopwords file :: ".((defined $self->{stop}) ? ($self->{stop}) : "")."\n"; $self->{traceString} .= "stem :: ".((defined $self->{stem}) ? ($self->{stem}) : "")."\n"; $self->SUPER::traceOptions(); } =item $self->configure($file) Overrides the configure method in WordNet::Similarity. This method loads various data files, such as the stop words and relations. Parameters: $file -- path of the configuration file. Returns: nothing =cut sub configure { my $self = shift; my $class = ref $self || $self; my %stopHash; my $gwi; # Call the configure method in parent (WordNet::Similarity) $self->SUPER::configure(@_); $self->{maxCache} = 5000; # Initialize the stop list. $self->{stopHash} = {}; my $wn = $self->{wn}; # Use default relation file if specified by module... $self->{relation} = $self->{relationDefault} if(!($self->{relation}) && defined $self->{relationDefault} && $self->{relationDefault} ne ""); # Load the stop list. if(defined $self->{stop}) { my $line; my $stopFile = $self->{stop}; if(open(STOP, $stopFile)) { while($line = ) { $line =~ s/[\r\f\n]//g; $line =~ s/^\s+//; $line =~ s/\s+$//; $line =~ s/\s+/_/g; $stopHash{$line} = 1; $self->{stopHash}->{$line} = 1; } close(STOP); } else { $self->{errorString} .= "\nWarning ($class->configure()) - "; $self->{errorString} .= "Unable to open $stopFile."; $self->{error} = 1 if($self->{error} < 1); } } # so now we are ready to initialize the get_wn_info package with # the wordnet object, 0/1 depending on if stemming is required and # the stop hash if($self->{stem}) { $gwi = WordNet::get_wn_info->new($wn, 1, %stopHash); $self->{gwi} = $gwi; } else { $gwi = WordNet::get_wn_info->new($wn, 0, %stopHash); $self->{gwi} = $gwi; } # Load the relations $self->_loadRelationFile(); # Initialize traces for relations... $self->{relationTraces} = []; my $i = 0; while(defined $self->{functions}->[$i]) { my $functionsString = ""; my $weight = $self->{weights}->[$i]; # see if any traces reqd. if so, create the functions string # however don't send it to the trace string immediately - will # print it only if there are any overlaps for this rel pair $functionsString = "Functions: "; my $j = 0; while(defined $self->{functions}->[$i]->[0]->[$j]) { $functionsString .= ($self->{functions}->[$i]->[0]->[$j])." "; $j++; } $functionsString .= "- "; $j = 0; while(defined $self->{functions}->[$i]->[1]->[$j]) { $functionsString .= ($self->{functions}->[$i]->[1]->[$j])." "; $j++; } push(@{$self->{relationTraces}}, $functionsString); $i++; } } =item $self->getSuperGlosses($wps1, $wps2) This method returns a list of large blocks of concatenated glosses (super-gloss) for each specified synset. A super-gloss is the block of text formed by concatenating the glosses of a synset with glosses of synsets related to it in WordNet. "Related" synsets are identified by specific relations specified in the "relations" file. If no relations file was specified in the configuration, only the gloss of that synset is returned. Parameters: wps1 and wps2 -- two synsets. Returns: List of superglosses for both synsets (2-D array). =cut sub getSuperGlosses { my $self = shift; my $wps1 = shift; my $wps2 = shift; my $class = ref $self || $self; my $rArray = []; my $gwi = $self->{gwi}; # NOTE: Thanks to Wybo Wiersma for providing the following (faster) # super-gloss code. # check if the supergloss of the left word is in the cache. # If it is not, add it. if(!defined($self->{cache}->[0]->{$wps1})) { push(@{$self->{cachelist}->[0]}, $wps1); # Remove the oldest cache-entry if there's no more room if(scalar(@{$self->{cachelist}->[0]}) > $self->{maxCache}) { my $todel = shift(@{$self->{cachelist}->[0]}); delete ($self->{cache}->[0]->{$todel}); } $self->{cache}->[0]->{$wps1} = $self->_getSuperGlosses($wps1, $gwi, 0); } # check if the supergloss of the right word is in the cache. # If it is not, add it. if(!defined($self->{cache}->[1]->{$wps2})) { push(@{$self->{cachelist}->[1]}, $wps2); # Remove the oldest cache-entry if there's no more room if(scalar(@{$self->{cachelist}->[1]}) > $self->{maxCache}) { my $todel = shift(@{$self->{cachelist}->[1]}); delete ($self->{cache}->[1]->{$todel}); } $self->{cache}->[1]->{$wps2} = $self->_getSuperGlosses($wps2, $gwi, 1); } return ($self->{cache}->[0]->{$wps1}, $self->{cache}->[1]->{$wps2}, $self->{weights}, $self->{relationTraces}); } sub _getSuperGlosses() { my $self = shift; my ($wps, $gwi, $zron) = @_; my @stringArray; # and now go thru the functions array, get the strings my $i = 0; while(defined $self->{functions}->[$i]) { # now get the string for the first set of synsets my %seth = (); $seth{$wps} = 1; my @arguments = \%seth; # apply the functions to the arguments, passing the output of # the inner functions to the inputs of the outer ones my $j = 0; while(defined $self->{functions}->[$i]->[$zron]->[$j]) { my $fn = $self->{functions}->[$i]->[$zron]->[$j]; @arguments = $gwi->$fn(@arguments); $j++; } # finally we should have one cute little string! push(@stringArray, $arguments[0]); $i++; } return \@stringArray; } =back =head3 Private Methods =over =item $self->_loadRelationFile() This method loads relations from a relation file. Parameters: none Returns: nothing =back =cut sub _loadRelationFile { my $self = shift; my $class = ref $self || $self; my $gwi = $self->{gwi}; if($self->{relation}) { my $header; my $relation; if(open (RELATIONS, $self->{relation})) { $header = ; $header =~ s/[\r\f\n]//g; $header =~ s/\s+//g; if(defined $header && $header =~ /RelationFile/) { my $index = 0; $self->{functions} = (); $self->{weights} = (); while($relation = ) { $relation =~ s/[\r\f\n]//g; # now for each line in the file, extract the # nested functions if any, check if they are defined, # if it makes sense to nest them, and then finally put # them into the @functions triple dimensioned array! # remove leading/trailing spaces from the relation $relation =~ s/^\s*(\S*?)\s*$/$1/; next if($relation =~ /^$/); # now extract the weight if any. if no weight, assume 1 if($relation =~ /(\S+)\s+(\S+)/) { $relation = $1; $self->{weights}->[$index] = $2; } else { $self->{weights}->[$index] = 1; } # check if we have a "proper" relation, that is a relation in # there are two blocks of functions! if($relation !~ /(.*)-(.*)/) { $self->{errorString} .= "\nError (${class}::_loadRelationFile()) - "; $self->{errorString} .= "Bad file format ($self->{relation})."; $self->{error} = 2; close RELATIONS; return; } # get the two parts of the relation pair my @twoParts; my $l; $twoParts[0] = $1; $twoParts[1] = $2; # process the two parts and put into functions array for($l = 0; $l < 2; $l++) { #no strict 'subs'; $twoParts[$l] =~ s/[\s\)]//g; my @functionArray = split(/\(/, $twoParts[$l]); my $j = 0; my $fn = $functionArray[$#functionArray]; unless($gwi->can($fn)) { $self->{errorString} .= "\nError (${class}::_loadRelationFile()) - "; $self->{errorString} .= "Undefined function ($functionArray[$#functionArray]) in relations file."; $self->{error} = 2; close RELATIONS; return; } $self->{functions}->[$index]->[$l]->[$j++] = $functionArray[$#functionArray]; my $input; my $output; my $dummy; my $k; for ($k = $#functionArray-1; $k >= 0; $k--) { my $fn2 = $functionArray[$k]; my $fn3 = $functionArray[$k+1]; if(!($gwi->can($fn2))) { $self->{errorString} .= "\nError (${class}::_loadRelationFile()) - "; $self->{errorString} .= "Undefined function ($functionArray[$k]) in relations file."; $self->{error} = 2; close(RELATIONS); return; } ($input, $dummy) = $gwi->$fn2($dummy, 1); ($dummy, $output) = $gwi->$fn3($dummy, 1); if($input != $output) { $self->{errorString} .= "\nError (${class}::_loadRelationFile()) - "; $self->{errorString} .= "Invalid function combination - $functionArray[$k]($functionArray[$k+1])."; $self->{error} = 2; close(RELATIONS); return; } $self->{functions}->[$index]->[$l]->[$j++] = $functionArray[$k]; } # if the output of the outermost function is synset array (1) # wrap a glos around it my $xfn = $functionArray[0]; ($dummy, $output) = $gwi->$xfn($dummy, 1); if($output == 1) { $self->{functions}->[$index]->[$l]->[$j++] = "glos"; } } $index++; } } else { $self->{errorString} .= "\nError (${class}::_loadRelationFile()) - "; $self->{errorString} .= "Bad file format ($self->{relation})."; $self->{error} = 2; close(RELATIONS); return; } close(RELATIONS); } else { $self->{errorString} .= "\nError (${class}::_loadRelationFile()) - "; $self->{errorString} .= "Unable to open $self->{relation}."; $self->{error} = 2; return; } } else { $self->{weights}->[0] = 1; $self->{functions}->[0]->[0]->[0] = "glosexample"; $self->{functions}->[0]->[1]->[0] = "glosexample"; return; } } 1; __END__ =head2 Discussion Many of the methods in this module can work with either offsets or wps strings internally. There are several interesting consequences of each mode. =over =item 1. An offset is not a unique identifier for a synset, but neither is a wps string. An offset only indicates a byte offset in one of the WordNet data files (data.noun, data.verb, etc. on Unix-like systems). An offset along with a part of speech, however, does uniquely identify a synset. A word#pos#sense string, on the other hand, is the opposite extreme. A word#pos#sense string is an identifier for a unique word sense. A synset can have several word senses in it (i.e., a synset is a set of word senses that are synonymous). The synset {beer_mug#n#1, stein#n#1} has two word senses. The wps strings 'beer_mug#n#1' and 'stein#n#1' can both be used to refer to the synset. For simplicity, we usually just use the first wps string when referring to the synset. N.B., the wps representation was developed by WordNet::QueryData. =item 2. Early versions of WordNet::Similarity::* used offsets internally for finding paths, hypernym trees, subsumers, etc. The module WordNet::QueryData that is used by Similarity, however, accepts only wps strings as input to its querySense method, which is used to find hypernyms. We have found that it is more efficient (faster) to use wps strings internally. =back =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 BUGS None. =head1 SEE ALSO WordNet::Similarity(3) WordNet::Similarity::vector(3) WordNet::Similarity::lesk(3) =head1 COPYRIGHT Copyright (c) 2005, Ted Pedersen and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/Similarity/ICFinder.pm0000644000175000017500000004323111015336622021527 0ustar sidsid# WordNet::Similarity::ICFinder.pm version 2.04 # (Last updated $Id: ICFinder.pm,v 1.19 2008/03/27 06:21:17 sidz1979 Exp $) # # A generic (and abstract) information content measure--this is not a # real measure. The res, lin, and jcn measures inherit from this class. # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Jason Michelizzi, Univeristy of Minnesota Duluth # mich0212 at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity::ICFinder; =head1 NAME WordNet::Similarity::ICFinder - a module for finding the information content of concepts in WordNet =head1 SYNOPSIS use WordNet::QueryData; my $wn = WordNet::QueryData->new; defined $wn or die "Construction of WordNet::QueryData failed"; use WordNet::Similarity::ICFinder; my $obj = WordNet::Similarity::ICFinder->new ($wn); my ($err, $errString) = $obj->getError (); $err and die $errString; my $wps1 = 'cat#n#1'; my $wps2 = 'feline#n#1'; my $offset1 = $wn -> offset ($wps1); my $offset2 = $wn -> offset ($wps2); # using the wps mode my $ic = $obj->IC ($wps1, 'n', 'wps'); my $prob = $obj->probability ($wps1, 'n', 'wps'); my $freq = $obj->getFrequency ($wps1, 'n', 'wps'); print "$wps1 has frequency $freq, probability $prob, and IC $ic\n"; my $ic = $obj->IC ($wps2, 'n', 'wps'); my $prob = $obj->probability ($wps2, 'n', 'wps'); my $freq = $obj->getFrequency ($wps2, 'n', 'wps'); print "$wps2 has frequency $freq, probability $prob, and IC $ic\n"; my @lcsbyic = $obj -> getLCSbyIC($wps1,$wps2,'n','wps'); print "$wps1 and $wps2 have LCS $lcsbyic[0]->[0] with IC $lcsbyic[0]->[1]\n"; # doing the same thing in the offset mode my $ic = $obj->IC ($offset1, 'n', 'offset'); my $prob = $obj->probability ($offset1, 'n', 'offset'); my $freq = $obj->getFrequency ($offset1, 'n', 'offset'); print "$offset1 has frequency $freq, probability $prob, and IC $ic\n"; my $ic = $obj->IC ($offset2, 'n', 'offset'); my $prob = $obj->probability ($offset2, 'n', 'offset'); my $freq = $obj->getFrequency ($offset2, 'n', 'offset'); print "$offset2 has frequency $freq, probability $prob, and IC $ic\n"; my @lcsbyic = $obj -> getLCSbyIC($offset1,$offset2,'n','wps'); print "$offset1 and $offset2 have LCS $lcsbyic[0]->[0] with IC $lcsbyic[0]->[1]\n"; =head1 DESCRIPTION =head2 Introduction Three of the measures provided within the package require information content values of concepts (WordNet synsets) for computing the semantic relatedness of concepts. Resnik (1995) describes a method for computing the information content of concepts from large corpora of text. In order to compute information content of concepts, according to the method described in the paper, we require the frequency of occurrence of every concept in a large corpus of text. We provide these frequency counts to the three measures (Resnik, Jiang-Conrath and Lin measures) in files that we call information content files. These files contain a list of WordNet synset offsets along with their part of speech and frequency count. The files are also used to determine the topmost nodes of the noun and verb 'is-a' hierarchies in WordNet. The information content file to be used is specified in the configuration file for the measure. If no information content file is specified, then the default information content file, generated at the time of the installation of the WordNet::Similarity modules, is used. A description of the format of these files follows. The FIRST LINE of this file must contain the hash-code of WordNet the the file was created with. This should be present as a string of the form wnver:: For example, if WordNet version 2.1 with the hash-code LL1BZMsWkr0YOuiewfbiL656+Q4 was used for creation of the information content file, the following line would be present at the start of the information content file. wnver::LL1BZMsWkr0YOuiewfbiL656+Q4 The rest of the file contains on each line, a WordNet synset offset, part-of-speech and a frequency count, of the form [ROOT] without any leading or trailing spaces. For example, one of the lines of an information content file may be as follows. 63723n 667 where '63723' is a noun synset offset and 667 is its frequency count. Suppose the noun synset with offset 1740 is the root node of one of the noun taxonomies and has a frequency count of 17625. Then this synset would appear in an information content file as follows: 1740n 17625 ROOT The ROOT tags are extremely significant in determining the top of the hierarchies and must not be omitted. Typically, frequency counts for the noun and verb hierarchies are present in each information content file. A number of support programs to generate these files from various corpora are present in the '/utils' directory of the package. A sample information content file has been provided in the '/samples' directory of the package. =head2 Methods The following methodes are provided by this module. =head3 Public Methods =over =cut use strict; use warnings; use WordNet::Similarity::PathFinder; our @ISA = qw/WordNet::Similarity::PathFinder/; our $VERSION = '2.04'; WordNet::Similarity::addConfigOption ('infocontent', 0, 'p', undef); =item $module->traceOptions (Z<>) Prints status of configuration options specific to this module to the trace string. This module has only one such options: infocontent. =cut sub traceOptions { my $self = shift; $self->{traceString} .= "infocontent :: $self->{infocontent}\n"; $self->SUPER::traceOptions; } =item $module->probability ($synset, $pos, $mode) Returns the probability of $synset in a corpus (using frequency values from whatever information content file is being used). If $synset is a wps string, then $mode must be 'wps'; if $synset is an offset, then $mode must be 'offset'. =cut sub probability { my $self = shift; my $wn = $self->{wn}; my ($offset, $pos, $mode) = @_; $offset = $wn->offset($offset) if(defined($mode) && $mode eq 'wps'); my $class = ref $self || $self; my $rootFreq = $self->{offsetFreq}->{$pos}->{0}; my $offFreq = $self->{offsetFreq}->{$pos}->{$offset}; if($rootFreq && defined $offFreq) { if($offFreq <= $rootFreq) { return $offFreq / $rootFreq; } $self->{errorString} .= "\nError (${class}::probability()) - "; $self->{errorString} .= "Probability greater than 1? (Check information content file)"; $self->{error} = 2; } return 0; } =item $module->IC ($synset, $pos, $mode) Returns the information content of $synset. If $synset is a wps string, then $mode must be 'wps'; if $synset is an offset, then $mode must be 'offset'. =cut sub IC { my $self = shift; my $wn = $self->{wn}; my ($offset, $pos, $mode) = @_; $offset = $wn->offset($offset) if(defined($mode) && $mode eq 'wps'); if($pos =~ /[nv]/) { my $prob = $self->probability($offset, $pos, 'offset'); return ($prob > 0) ? -log($prob) : 0; } return 0; } =item $module->getFrequency ($synset, $pos, $mode) Returns the frequency of $synset in whatever information content file is currently being used. If $synset is a wps string, then the mode must be 'wps'; if $synset is an offset, then $mode must be 'offset'. Usually the C and C methods will be more useful than this method. This method is useful in determining if the frequency of a synset was 0. =cut sub getFrequency { my $self = shift; my $wn = $self->{wn}; my ($synset, $pos, $mode) = @_; my $offset; if ($mode eq 'wps') { $offset = $wn->offset ($synset); } else { $offset = $synset; } my $freq = $self->{offsetFreq}->{$pos}->{$offset}; return $freq; } =item getLCSbyIC($synset1, $synset2, $pos, $mode) Given two input synsets, finds the least common subsumer (LCS) of them. If there are multiple candidates for the LCS, the the candidate with the greatest information content. Parameters: two synsets, a part of speech, and a mode. Returns: a list of the form ($lcs, $ic) where $lcs is the LCS and $ic is the information content of the LCS. =cut sub getLCSbyIC { my $self = shift; my $synset1 = shift; my $synset2 = shift; my $pos = shift; my $mode = shift; my $class = ref $self || $self; my $wn = $self->{wn}; my @paths = $self->getAllPaths ($synset1, $synset2, $pos, $mode); # check to see if any paths were found unless (defined $paths[0]) { $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::getLCSbyIC()) - "; my $wps1 = $mode eq 'wps' ? $synset1 : $wn->getSense ($synset1, $pos); my $wps2 = $mode eq 'wps' ? $synset2 : $wn->getSense ($synset2, $pos); $self->{errorString} .= "No LCS found for $wps1 and $wps2."; if ($self->{trace}) { $self->{traceString} .= "\nNo LCS found for "; $self->printSet ($pos, $mode, $synset1); $self->{traceString} .= ", "; $self->printSet ($pos, $mode, $synset2); $self->{traceString} .= "."; } return undef; } my %IC; # get the IC of each subsumer, put it in a hash foreach (@paths) { # the "O + $off" below is a hack to cope with an unfortunate problem: # The offsets in the WordNet data files are zero-padded, eight-digit # decimal numbers. Sometimes these numbers get stripped off (QueryData's # offset() method does this). As a result, it is much easier to compare # the offsets as numbers rather than as strings: # '00001740' ne '1740', BUT 0 + '00001740' == 0 + '1740' my $off; if ($mode eq 'offset') { $off = $_->[0]; } else { $off = (index ($_->[0], '*Root*') < $[) ? $wn->offset ($_->[0]) : 0; } next if defined $IC{$_->[0]}; $IC{$_->[0]} = $self->IC (0 + $off, $pos) || 0; } # sort lcs by info content my @array = sort {$b->[1] <=> $a->[1]} map {[$_, $IC{$_}]} keys %IC; if ($self->{trace}) { $self->{traceString} .= "Lowest Common Subsumer(s): "; } my @return; # determine which subsumers have the highest info content; do some # tracing as well foreach my $ref (@array) { if ($self->{trace}) { $self->printSet ($pos, $mode, $ref->[0]); $self->{traceString} .= " (IC="; $self->{traceString} .= sprintf ("%.6f", $ref->[1]); $self->{traceString} .= ") "; } if ($ref->[1] == $array[0]->[1]) { push @return, $ref; } } $self->{trace} and $self->{traceString} .= "\n"; return @return; } =item $module->configure (Z<>) Overrides the configure method of WordNet::Similarity to process the information content file (also calles WordNet::Similarity::configure() so that all the work done by that method is still accomplished). =cut sub configure { my $self = shift; $self->SUPER::configure (@_); my $wn = $self->{wn}; my $wntools = $self->{wntools}; my $class = ref $self || $self; unless (defined $self->{infocontent}) { # look for info content file my $path; my $wnver; my @possiblePaths = (); # Look for all possible default data files installed. foreach $path (@INC) { if(-e $path."/WordNet/ic-semcor.dat") { push @possiblePaths, $path."/WordNet/ic-semcor.dat"; } elsif(-e $path."\\WordNet\\ic-semcor.dat") { push @possiblePaths, $path."\\WordNet\\ic-semcor.dat"; } } # If there are multiple possibilities, get the one that matches the # the installed version (hash-code) of WordNet. foreach $path (@possiblePaths) { if (open (ICF, $path)) { my $wnver = ; $wnver =~ s/[\r\f\n\t ]+//g; if ($wnver =~ /wnver::(.*)/) { $wnver = $1; if (defined $wnver && $wnver eq $wntools->hashCode()) { $self->{infocontent} = $path; close (ICF); last; } } close (ICF); } } } unless (defined $self->{infocontent}) { $self->{errorString} .= "Error (${class}::configure()) - "; $self->{errorString} .= "Could not find a default information content file\n"; $self->{error} = 2; return; } unless (open ICF, $self->{infocontent}) { $self->{errorString} .= "Error (${class}::configure()) - "; $self->{errorString} .= "Could not open information content file $self->{infocontent}\n"; $self->{error} = 2; return; } # load the info content file data my $wnver = ; $wnver =~ s/[\r\f\n\t ]+//g; if($wnver =~ /wnver::(.*)/) { $wnver = $1; if(defined $wnver && $wnver eq $wntools->hashCode()) { $self->{offsetFreq}->{n}->{0} = 0; $self->{offsetFreq}->{v}->{0} = 0; while() { s/[\r\f\n]//g; s/^\s+//; s/\s+$//; my ($offsetPOS, $frequency, $topmost) = split /\s+/, $_, 3; if($offsetPOS =~ /([0-9]+)([nvar])/) { my $curOffset; my $curPOS; $curOffset = $1; $curPOS = $2; $self->{offsetFreq}->{$curPOS}->{$curOffset} = $frequency; if(defined $topmost && $topmost =~ /ROOT/) { $self->{offsetFreq}->{$curPOS}->{0} += $self->{offsetFreq}->{$curPOS}->{$curOffset}; } } else { $self->{errorString} .= "\nError (${class}::configure()) - "; $self->{errorString} .= "Bad file format ($self->{infocontent})."; $self->{error} = 2; return; } } } else { $self->{errorString} .= "\nError (${class}::configure()) - "; $self->{errorString} .= "WordNet version does not match data file."; $self->{error} = 2; return; } } else { $self->{errorString} .= "\nError (${class}::configure()) - "; $self->{errorString} .= "Bad file format ($self->{infocontent})."; $self->{error} = 2; return; } close (ICF); } =back =head3 Private Methods =over =item $module->_loadInfoContentFile ($file) Subroutine to load frequency counts from an information content file. =cut sub _loadInfoContentFile { my $self = shift; my $infoContentFile = shift; my $wn = $self->{'wn'}; my $wntools = $self->{'wntools'}; my $wnver; my $offsetPOS; my $frequency; my $topmost; my $localFreq = {}; if(open(INFOCONTENT, $infoContentFile)) { $wnver = ; $wnver =~ s/[\r\f\n]//g; $wnver =~ s/\s+//g; if($wnver =~ /wnver::(.*)/) { $wnver = $1; if(defined $wnver && $wnver eq $wntools->hashCode()) { $localFreq->{"n"}->{0} = 0; $localFreq->{"v"}->{0} = 0; while() { s/[\r\f\n]//g; s/^\s+//; s/\s+$//; ($offsetPOS, $frequency, $topmost) = split /\s+/, $_, 3; if($offsetPOS =~ /([0-9]+)([nvar])/) { my $curOffset; my $curPOS; $curOffset = $1; $curPOS = $2; $localFreq->{$curPOS}->{$curOffset} = $frequency; if(defined $topmost && $topmost =~ /ROOT/) { $localFreq->{$curPOS}->{0} += $localFreq->{$curPOS}->{$curOffset}; } } else { return "Bad file format ($infoContentFile)."; } } } else { return "WordNet version does not match data file."; } } else { return "Bad file format ($infoContentFile)."; } close(INFOCONTENT); } else { return "Unable to open '$infoContentFile'."; } $self->{'offsetFreq'} = $localFreq; return ""; } =item $module->_isValidInfoContentFile ($filename) Subroutine that checks the validity of an information content file. =cut sub _isValidInfoContentFile { my $self = shift; my $path = shift; my $wn = $self->{'wn'}; my $wntools = $self->{'wntools'}; my $wnver; if(open(INFOCONTENT, $path)) { $wnver = ; $wnver =~ s/[\r\f\n]//g; $wnver =~ s/\s+//g; if($wnver =~ /wnver::(.*)/) { $wnver = $1; if(defined $wnver && $wnver eq $wntools->hashCode()) { close(INFOCONTENT); return 1; } } close(INFOCONTENT); } return 0; } 1; __END__ =back =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Jason Michelizzi, Univeristy of Minnesota Duluth mich0212 at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 BUGS None. To report a bug e-mail tpederse at d.umn.edu or go to http://groups.yahoo.com/group/wn-similarity/. =head1 SEE ALSO WordNet::Similarity(3) WordNet::Similarity::res(3) WordNet::Similarity::lin(3) WordNet::Similarity::jcn(3) =head1 COPYRIGHT Copyright (c) 2005, Ted Pedersen, Jason Michelizzi and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/Similarity/random.pm0000644000175000017500000002432011015336622021362 0ustar sidsid# WordNet::Similarity::random.pm version 2.04 # (Last updated $Id: random.pm,v 1.15 2008/03/27 06:21:17 sidz1979 Exp $) # # Random semantic distance generator module. # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity::random; =head1 NAME WordNet::Similarity::random - Perl module for computing semantic relatedness of word senses using a random measure. =head1 SYNOPSIS use WordNet::Similarity::random; use WordNet::QueryData; my $wn = WordNet::QueryData->new(); my $random = WordNet::Similarity::random->new($wn); my $value = $random->getRelatedness("car#n#1", "bus#n#2"); ($error, $errorString) = $random->getError(); die "$errorString\n" if($error); print "car (sense 1) <-> bus (sense 2) = $value\n"; =head1 DESCRIPTION This module generates random numbers as a measure of semantic relatedness of word senses. It is possible to assign a random value for a word sense pair and return the same value if the same word sense pair is passed as input. It is also possible to generate a new random value for the same word sense pair every time. =head2 Methods =over =cut use strict; use WordNet::Similarity; use vars qw($VERSION @ISA @EXPORT @EXPORT_OK %EXPORT_TAGS); @ISA = qw(WordNet::Similarity); %EXPORT_TAGS = (); @EXPORT_OK = (); @EXPORT = (); $VERSION = '2.04'; WordNet::Similarity::addConfigOption ('maxrand', 1, 'f', 1.0); =item $random->setPosList() This method is internally called to determine the parts of speech this measure is capable of dealing with. Parameters: none. Returns: none. =cut sub setPosList { my $self = shift; $self->{n} = 1; $self->{v} = 1; $self->{a} = 1; $self->{r} = 1; } =item $random->initialize($file) Overrides the initialize method in the parent class. This method essentially initializes the measure for use. Parameters: $file -- configuration file. Returns: none. =cut # Initialization of the WordNet::Similarity::random object... parses the config file and sets up # global variables, or sets them to default values. # INPUT PARAMS : $paramFile .. File containing the module specific params. # RETURN VALUES : (none) sub initialize { my $self = shift; $self->SUPER::initialize (@_); $self->{maxCacheSize} = WordNet::Similarity::UNLIMITED_CACHE; } =item $random->getRelatedness ($synset1, $synset2) Returns a value for the relatedness of the two synsets. This value is a random number greater-than or equal-to zero and less-than 'maxrand'. If the synsets are not properly formed word#pos#sense strings, or if they are not found in WordNet, then the error level will be set to non-zero and an error string will be generated. =cut sub getRelatedness { my $self = shift; my $wps1 = shift; my $wps2 = shift; my $wn = $self->{wn}; my $class = ref $self || $self; # check if the synsets are well-formed and are found in WordNet my $ref = $self->parseWps ($wps1, $wps2); ref $ref or return $ref; # Initialize traces. $self->{traceString} = ""; # Now check if the similarity value for these two synsets is in # fact in the cache... if so return the cached value. my $relatedness = $self->{doCache} ? $self->fetchFromCache ($wps1, $wps2) : undef; defined $relatedness and return $relatedness; # Now get down to really finding the relatedness of these two. my $score = rand ($self->{maxrand}); $score = sprintf ("%.3f", $score); $self->{doCache} and $self->storeToCache ($wps1, $wps2, $score); return $score; } =item $random->traceOptions() This method is internally called to determine the extra options specified by this measure (apart from the default options specified in the WordNet::Similarity base class). Parameters: none. Returns: none. =cut # 12/5/03 JM (#1) # show all config options specific to this module sub traceOptions { my $self = shift; $self->{traceString} .= "maxrand :: $self->{maxrand}\n"; $self->SUPER::traceOptions(); } 1; __END__ =back =head2 Usage The semantic relatedness modules in this distribution are built as classes that define the following methods: new() getRelatedness() getError() getTraceString() See the WordNet::Similarity(3) documentation for details of these methods. =head3 Typical Usage Examples To create an object of the random measure, we would have the following lines of code in the Perl program. use WordNet::Similarity::random; $measure = WordNet::Similarity::random->new($wn, '/home/sid/random.conf'); The reference of the initialized object is stored in the scalar variable '$measure'. '$wn' contains a WordNet::QueryData object that should have been created earlier in the program. The second parameter to the 'new' method is the path of the configuration file for the random measure. If the 'new' method is unable to create the object, '$measure' would be undefined. This, as well as any other error/warning may be tested. die "Unable to create object.\n" if(!defined $measure); ($err, $errString) = $measure->getError(); die $errString."\n" if($err); To find the semantic relatedness of the first sense of the noun 'car' and the second sense of the noun 'bus' using the measure, we would write the following piece of code: $relatedness = $measure->getRelatedness('car#n#1', 'bus#n#2'); To get traces for the above computation: print $measure->getTraceString(); However, traces must be enabled using configuration files. By default traces are turned off. =head1 CONFIGURATION FILE The behavior of the measures of semantic relatedness can be controlled by using configuration files. These configuration files specify how certain parameters are initialized within the object. A configuration file may be specified as a parameter during the creation of an object using the new method. The configuration files must follow a fixed format. Every configuration file starts with the name of the module ON THE FIRST LINE of the file. For example, a configuration file for the random module will have on the first line 'WordNet::Similarity::random'. This is followed by the various parameters, each on a new line and having the form 'name::value'. The 'value' of a parameter is optional (in case of boolean parameters). In case 'value' is omitted, we would have just 'name::' on that line. Comments are supported in the configuration file. Anything following a '#' is ignored till the end of the line. The module parses the configuration file and recognizes the following parameters: =over =item trace The value of this parameter specifies the level of tracing that should be employed for generating the traces. This value is an integer equal to 0, 1, or 2. If the value is omitted, then the default value, 0, is used. A value of 0 switches tracing off. A value of 1 or 2 switches tracing on, but the random measure is so simple, that generating traces makes little senses, so this option has no effect. =item cache The value of this parameter specifies whether or not caching of the relatedness values should be performed. This value is an integer equal to 0 or 1. If the value is omitted, then the default value, 1, is used. A value of 0 switches caching 'off', and a value of 1 switches caching 'on'. =item maxCacheSize The value of this parameter indicates the size of the cache, used for storing the computed relatedness value. The specified value must be a non-negative integer. If the value is omitted, then the default value, 5,000, is used. Setting maxCacheSize to zero has the same effect as setting cache to zero, but setting cache to zero is likely to be more efficient. Caching and tracing at the same time can result in excessive memory usage because the trace strings are also cached. If you intend to perform a large number of relatedness queries, then you might want to turn tracing off. =item maxrand The value of this option is the maximum random number that will be generated. The value of this option must be a positive floating-point number. The default value is 1.0. All random numbers generated will be in the range [0, maxrand). =back =head1 SEE ALSO perl(1), WordNet::Similarity(3), WordNet::QueryData(3) http://www.cs.utah.edu/~sidd http://wordnet.princeton.edu http://www.ai.mit.edu/~jrennie/WordNet http://groups.yahoo.com/group/wn-similarity =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 BUGS None. To report a bug, go to http://groups.yahoo.com/group/wn-similarity/ or e-mail "S". =head1 COPYRIGHT AND LICENSE Copyright (c) 2005, Ted Pedersen and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/Similarity/FrequencyCounter.pm0000644000175000017500000002111511020104755023376 0ustar sidsid# WordNet::Similarity::FrequencyCounter.pm version 2.05 # (Last updated $Id: FrequencyCounter.pm,v 1.1 2008/05/30 23:12:45 sidz1979 Exp $) # # Module providing support functions for frequency counting # programs used to estimate the information content of concepts. # # Copyright (c) 2008, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity::FrequencyCounter; =head1 NAME WordNet::Similarity::FrequencyCounter - Support functions for frequency counting programs used to estimate the information content of concepts. =head1 METHODS =over =cut use strict; use Carp; use Exporter; our ($VERSION, @ISA, @EXPORT, @EXPORT_OK, %EXPORT_TAGS); @ISA = qw(Exporter); %EXPORT_TAGS = (); @EXPORT_OK = (); @EXPORT = (); $VERSION = '2.05'; =item createTopHash ($wn) Creates and loads the topmost nodes hash from WordNet. returns: $topHash, a reference to a hash containing the topmost nodes of the noun and verb hierarchies in WordNet. =cut # Creates and loads the topmost nodes hash. sub createTopHash { my $wn = shift; return undef if(!defined($wn)); my $topHash = {}; my $datapath = $wn->dataPath(); my $unixfile_n = "${datapath}/data.noun"; my $windozefile_n = "${datapath}\\noun.dat"; my $nounfile = -e $windozefile_n ? $windozefile_n : $unixfile_n; open(NFH, '<', $nounfile) or die "Cannot open '$nounfile': $!"; while () { next if " " eq substr $_, 0, 2; next if / \@i? \d\d\d\d\d\d\d\d /; next unless /^(\d\d\d\d\d\d\d\d) /; my $offset = $1 + 0; # QueryData::getSense will die() if the $offset and pos are not # found. Putting this in an eval will catch the exception. See # perldoc -f eval my $wps; eval {$wps = $wn->getSense($offset, 'n')}; if($@) { die "(offset '$offset' not found) $@"; } $topHash->{n}->{$offset} = 1; } close NFH; my $unixfile_v = "${datapath}/data.verb"; my $windozefile_v = "${datapath}\\verb.dat"; my $verbfile = -e $windozefile_v ? $windozefile_v : $unixfile_v; open(VFH, '<', $verbfile) or die "Cannot open '$verbfile': $!"; while () { next if " " eq substr($_, 0, 2); next if / \@i? \d\d\d\d\d\d\d\d /; next unless /^(\d\d\d\d\d\d\d\d) /; my $offset = $1 + 0; $topHash->{v}->{$offset} = 1; } close VFH; return $topHash; } =item updateWordFrequency ($word, $offsetFreq, $wn [, $opt_resnik]) Updates the counts of a given word in a given offset-frequency hash. It finds the offsets of all concepts corresponding to the given word in WordNet, and increments the frequency counts of these in the hash. returns: nothing. =cut # Subroutine to update frequency tokens on "seeing" a word in text sub updateWordFrequency { my $word = shift; my $offsetFreq = shift; my $wn = shift; my $opt_resnik = shift; die "Input word not defined.\n" if(!defined($word)); die "Offset-frequency hash reference not provided.\n" if(!defined($offsetFreq)); die "WordNet::QueryData reference not provided.\n" if(!defined($wn)); foreach my $pos ("n", "v") { my @forms = $wn->validForms($word."\#".$pos); my @senses = (); foreach my $form (@forms) { push @senses, $wn->querySense($form); } foreach (@senses) { if(defined $opt_resnik) { $offsetFreq->{$pos}->{$wn->offset($_)} += (1/($#senses + 1)); } else { $offsetFreq->{$pos}->{$wn->offset($_)}++; } } } } =item propagateFrequency ($offsetFreq, $wn, $topHash) This function propagates the frequencies up the is-a hierarchies in WordNet, and return a hash with the new frequency counts. returns: $newFreq, hash containing updated counts. =cut # Subroutine that propagates the frequencies up through WordNet sub propagateFrequency { my $offsetFreq = shift; my $wn = shift; my $topHash = shift; die "Offset-frequency hash not defined.\n" if(!defined($offsetFreq)); die "WordNet::QueryData object not provided.\n" if(!defined($wn)); die "Topmost nodes hash not provided.\n" if(!defined($topHash)); my $newFreq = {}; $offsetFreq->{n}->{0} = 0 if(!defined($offsetFreq->{n}->{0})); $offsetFreq->{v}->{0} = 0 if(!defined($offsetFreq->{v}->{0})); _propagateFrequency($newFreq, 0, "n", $topHash, $wn, $offsetFreq); _propagateFrequency($newFreq, 0, "v", $topHash, $wn, $offsetFreq); delete $newFreq->{n}->{0}; delete $newFreq->{v}->{0}; return $newFreq; } # Recursive subroutine that propagates the frequencies up through WordNet sub _propagateFrequency { my $newFreq = shift; my $node = shift; my $pos = shift; my $topHash = shift; my $wn = shift; my $offsetFreq = shift; die "Offset-frequency hash not defined.\n" if(!defined($offsetFreq)); die "WordNet::QueryData object not provided.\n" if(!defined($wn)); die "Topmost nodes hash not provided.\n" if(!defined($topHash)); die "Curent frequency hash not defined.\n" if(!defined($newFreq)); return $newFreq->{$pos}->{$node} if(defined($newFreq->{$pos}->{$node})); my $retValue = &_getHyponymOffsets($node, $pos, $topHash, $wn); if(!defined($retValue)) { $newFreq->{$pos}->{$node} = $offsetFreq->{$pos}->{$node} ? $offsetFreq->{$pos}->{$node} : 0; return $offsetFreq->{$pos}->{$node} ? $offsetFreq->{$pos}->{$node} : 0; } my $sum = 0; if($#{$retValue} >= 0) { foreach my $hyponym (@{$retValue}) { $sum += &_propagateFrequency($newFreq, $hyponym, $pos, $topHash, $wn, $offsetFreq); } } $newFreq->{$pos}->{$node} = ( $offsetFreq->{$pos}->{$node} ? $offsetFreq->{$pos}->{$node} : 0 ) + $sum; return ( $offsetFreq->{$pos}->{$node} ? $offsetFreq->{$pos}->{$node} : 0 ) + $sum; } # Subroutine that returns the hyponyms of a given synset. sub _getHyponymOffsets { my $offset = shift; my $pos = shift; my $topHash = shift; my $wn = shift; die "WordNet::QueryData object not provided.\n" if(!defined($wn)); die "Topmost nodes hash not provided.\n" if(!defined($topHash)); if($offset == 0) { my @retVal = keys %{$topHash->{$pos}}; return [@retVal]; } my $wordForm = $wn->getSense($offset, $pos); my @hyponyms = $wn->querySense($wordForm, "hypos"); if(!@hyponyms || $#hyponyms < 0) { return undef; } my @retVal = (); foreach my $hyponym (@hyponyms) { $offset = $wn->offset($hyponym); push @retVal, $offset; } return [@retVal]; } 1; __END__ =back =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 BUGS None. To submit a bug report, go to http://groups.yahoo.com/group/wn-similarity or send e-mail to tpederse I d.umn.edu. =head1 SEE ALSO perl(1), WordNet::Similarity(3) http://www.cs.utah.edu/~sidd http://wordnet.princeton.edu http://www.ai.mit.edu/~jrennie/WordNet http://groups.yahoo.com/group/wn-similarity =head1 COPYRIGHT Copyright (c) 2008, Ted Pedersen and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/Similarity/PathFinder.pm0000644000175000017500000006145111015336622022134 0ustar sidsid# WordNet::Similarity::PathFinder version 2.04 # (Last updated $Id: PathFinder.pm,v 1.39 2008/03/27 06:21:17 sidz1979 Exp $) # # Module containing path-finding code for the various measures of semantic # relatedness. # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Jason Michelizzi, Univeristy of Minnesota Duluth # mich0212 at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity::PathFinder; =head1 NAME WordNet::Similarity::PathFinder - module to implement path finding methods (by node counting) for WordNet::Similarity measures of semantic relatedness =head1 SYNOPSIS use WordNet::QueryData; my $wn = WordNet::QueryData->new; use WordNet::Similarity::PathFinder; my $obj = WordNet::Similarity::PathFinder->new ($wn); my $wps1 = 'winston_churchill#n#1'; my $wps2 = 'england#n#1'; # parseWps returns reference to an array that contains # word1 pos1 sense1 offset1 word2 pos2 sense2 offset2 my $result = $obj->parseWps($wps1, $wps2); print "@$result\n"; # path is a reference to an array that contains the path between # wps1 and wps2 expressed as a series of wps values my @paths = $obj->getShortestPath($wps1, $wps2, 'n', 'wps'); my ($length, $path) = @{shift @paths}; defined $path or die "No path between synsets"; print "shortest path between $wps1 and $wps2 is $length edges long\n"; print "@$path\n"; my $offset1 = $wn -> offset($wps1); my $offset2 = $wn -> offset($wps2); # path is a reference to an array that contains the path between # offset1 and offset2 expressed as a series of offset values my @paths = $obj->getShortestPath($offset1, $offset2, 'n', 'offset'); my ($length, $path) = @{shift @paths}; defined $path or die "No path between synsets"; print "shortest path between $offset1 and $offset2 is $length edges long\n"; print "@$path\n"; =head1 DESCRIPTION =head2 Introduction This class is derived from (i.e., is a sub-class of) WordNet::Similarity. The methods in this module are useful for finding paths between concepts in WordNet's 'is-a' taxonomies. Concept A is-a concept B if, and only if, B is a hypernym of A or A is in the hypernym tree of B. N.B., only nouns and verbs have hypernyms. The methods that find path lengths (such as C and C compute the lengths using node-counting not edge-counting. In general, the length of a path using node-counting will always be one more than the length using edge-counting. For example, if concept A is a hyponym of concept B, then the path length between A and B using node-counting is 2, but the length using edge-counting is 1. Likewise, the path between A and A is 1 using node-counting and 0 using edge-counting. =head2 Methods This module inherits all the methods of WordNet::Similarity. Additionally, the following methods are also defined. =head3 Public methods =over =cut use strict; use warnings; use WordNet::Similarity; use File::Spec; our @ISA = qw/WordNet::Similarity/; our $VERSION = '2.04'; WordNet::Similarity::addConfigOption ('rootNode', 0, 'i', 1); =item $measure->setPosList(Z<>) Specifies the parts of speech that measures derived from this module support (namely, nouns and verbs). parameters: none returns: true =cut sub setPosList { my $self = shift; $self->{n} = 1; $self->{v} = 1; return 1; } =item $self->traceOptions(Z<>) Overrides method of same name in WordNet::Similarity. Prints module-specific configuration options to the trace string (if tracing is on). PathFinder supports one module specific option: rootNode. Parameters: none returns: nothing =cut sub traceOptions { my $self = shift; $self->{traceString} .= "root node :: $self->{rootNode}\n"; $self->SUPER::traceOptions(); } =item $measure->parseWps($synset1, $synset2) parameters: synset1, synset2 -- two synsets in wps format returns: a reference to an array, WordNet::Similarity::UNRELATED, or undef Overrides the parseWps() method in WordNet::Similarity in order to run additional checks, but calls WordNet::Similarity::parseWps() to get those checks accomplished as well. Thus, this method does everything that WordNet::Similarity::parseWps does. =over =item quote from WordNet::Similarity::parseWps: This method checks the format of the two input synsets by calling validateSynset() for each synset. If the synsets are in wps format, a reference to an array will be returned. This array has the form [$word1, $pos1, $sense1, $offset1, $word2, $pos2, $sense2, $offset2] where $word1 is the word part of $wps1, $pos1, is the part of speech of $wps1, $sense1 is the sense from $wps. $offset1 is the offset for $wps1. If an error occurs (such as a synset being poorly-formed), then undef is returned, the error level is set to non-zero, and an error message is appended to the error string. =back In addition, if the two synsets are from different parts of speech, then WordNet::Similarity::UNRELATED is returned, the error level is set to 1, and a message is appended to the error string. If either synset is not a noun or a verb, then the error level is set to 1, a message is appended to the error string, and undef is returned. If the synsets are in wps format, a reference to an array will be returned. This array has the form [$word1, $pos1, $sense1, $offset1, $word2, $pos2, $sense2, $offset2]. =cut sub parseWps { my $self = shift; my $ret = $self->SUPER::parseWps (@_); my $class = ref $self || $self; ref $ret or return $ret; my ($w1, $pos1, $s1, $off1, $w2, $pos2, $s2, $off2) = @{$ret}; # check to make sure both input words are of the same part of speech if ($pos1 ne $pos2) { $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::parseWps()) - "; $self->{errorString} .= "$w1#$pos1 and $w2#$pos2 belong to different parts of speech."; if ($self->{trace}) { $self->{traceString} .= "\n"; $self->printSet ($pos1, 'wps', "$w1#$pos1#$s1"); $self->{traceString} .= " and "; $self->printSet ($pos2, 'wps', "$w2#$pos2#$s2"); $self->{traceString} .= " belong to different parts of speech."; } return $self->UNRELATED; } # check to make sure that the pos is a noun or verb if (index ("nv", $pos1) < $[) { if ($self->{trace}) { $self->{traceString} .= "Only verbs and nouns have hypernym trees ($w1#$pos1, $w2#$pos2).\n"; } $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::parseWps()) - "; $self->{errorString} .= "Only verbs and nouns have hypernym trees ($w1#$pos1, $w2#$pos2)."; return undef; } return $ret; } =item $measure->getShortestPath($synset1, $synset2, $pos, $mode) Given two input synsets, returns the shortest path between the two synsets. Parameters: two synsets, a part-of-speech, and a mode indicator (i.e., the string 'offset' or 'wps'). If the mode is 'offset', then the synsets should be WordNet offsets. If the mode is 'wps', then the synsets should be in word#pos#sense format. Returns: a list of references to arrays. Each array has the form C<($path_length, $path_ref)> where $path_ref is a reference to an array whose elements are the synsets along the shortest path between the two input synsets. There will be as many array references returned as there are shortest paths between the synsets. That is, there will be no arrays returned if there is no path between the synsets, and there will be at least one array returned if there is a path between the synsets. If there are multiple paths tied for being shortest in length, then all those paths are returned (hence, this is why multiple array references can be returned). Upon error, returns undef, sets the error level to non-zero, and appends a message to the error string. =cut sub getShortestPath { my $self = shift; my $synset1 = shift; my $synset2 = shift; my $pos = shift; my $mode = shift; my $class = ref $self || $self; my $wn = $self->{wn}; # JM 2/9/04 - we do this in validateSynset() now #if ($mode eq 'wps') { # # this prevents problems when the two input words are different word # # senses from the same synset (e.g., car#n#1 and auto#n#1) # ($synset1) = $wn->querySense ($synset1, "syns"); # ($synset2) = $wn->querySense ($synset2, "syns"); #} my @paths = $self->getAllPaths ($synset1, $synset2, $pos, $mode); # check to see if any paths were found; if none were found, then # $paths[0] will be undefined unless (defined $paths[0]) { $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::getShortestPath()) - "; my ($wps1, $wps2) = ($synset1, $synset2); if ($mode eq 'offset') { $wps1 = $wn->getSense ($synset1, $pos); $wps2 = $wn->getSense ($synset2, $pos); } $self->{errorString} .= "No path between synsets $wps1 and $wps2 found."; if ($self->{trace}) { $self->{traceString} .= "\nNo path between synsets "; $self->printSet ($pos, 'wps', $wps1); $self->{traceString} .= " and "; $self->printSet ($pos, 'wps', $wps2); $self->{traceString} .= " found."; } return undef; } my $best_length = $paths[0]->[1]; my @return = ([$paths[0]->[1], $paths[0]->[2]]); foreach (1..$#paths) { last if $paths[$_]->[1] > $best_length; push @return, [$paths[$_]->[1], $paths[$_]->[2]]; } #my $length = $paths[0]->[1]; #my $path = $paths[0]->[2]; if ($self->{trace}) { for (@return) { $self->{traceString} .= "\nShortest path: "; $self->printSet ($pos, $mode, @{$_->[1]}); $self->{traceString} .= "\nPath length = " . $_->[0]; } } return @return; } =item $measure->getAllPaths($synset1, $synset2, $pos, $mode) Given two input synsets, returns all the paths between the two synsets. Parameters: a reference to the object, two synsets, a part-of-speech, and a mode indicator (the string 'offset' or 'wps'). If the mode is 'offset', then the synsets should be WordNet offsets. If the mode is 'wps', then they should be strings in word#pos#sense format. Returns: A list of all paths, sorted by path length in ascending order. The format for each item in the list is a reference to an array that has the format: [$top, $length, [@synsets_list]] where @synset_list is a list of synsets along the path (including the two input synsets) Returns undef on error. =cut sub getAllPaths { my $self = shift; my $class = ref $self || $self; my $synset1 = shift; my $synset2 = shift; my $pos = shift; my $mode = shift; if (($mode ne 'offset') && ($mode ne 'wps')) { $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::getAllPaths()) - "; $self->{errorString} .= "Mode must be either 'offset' or 'wps'"; return undef; } my @lTrees = $self->_getHypernymTrees ($synset1, $pos, $mode); my @rTrees = $self->_getHypernymTrees ($synset2, $pos, $mode); # [trace] if($self->{trace}) { foreach my $lTree (@lTrees) { $self->{traceString} .= "HyperTree: "; $self->printSet ($pos, $mode, @$lTree); $self->{traceString} .= "\n"; } foreach my $rTree (@rTrees) { $self->{traceString} .= "HyperTree: "; $self->printSet ($pos, $mode, @$rTree); $self->{traceString} .= "\n"; } } # [/trace] # Find the length of each path in these trees. my @return; # my $root = $mode eq 'offset' # ? 0 # : ($pos eq 'n') ? $self->ROOT_N : $self->ROOT_V; LTREE: foreach my $lTree (@lTrees) { RTREE: foreach my $rTree (@rTrees) { my $subsumer; $subsumer = $self->_getSubsumerFromTrees ($lTree, $rTree, $mode); next RTREE unless defined $subsumer; #next RTREE if ($subsumer eq $root) and !$self->{rootNode}; my $lCount = 0; my @lpath; foreach my $offset (reverse @{$lTree}) { $lCount++; last if($offset eq $subsumer); push @lpath, $offset; } my $rCount = 0; my @rpath; foreach my $offset (reverse @{$rTree}) { $rCount++; last if($offset eq $subsumer); unshift @rpath, $offset; } my $path = [@lpath, $subsumer, @rpath]; push @return, [$subsumer, $rCount + $lCount - 1, $path]; } } return sort {$a->[1] <=> $b->[1]} @return; } =item $measure->validateSynset($synset) parameters: synset -- a string in word#pos#sense format returns: a list or undef on error This method overrides the method of the same name in WordNet::Similarity to provide additional behavior but calls WordNet::Similarity::validateSynset to accomplish that method's behavior. Thus, this method does everything that WordNet::Similarity::validateSynset does. =over =item quote from WordNet::Similarity::validateSynset: This method does the following: =over =item 1. Verifies that the synset is well-formed (i.e., that it consists of three parts separated by #s, the pos is one of {n, v, a, r} and that sense is a natural number). A synset that matches the pattern '[^\#]+\#[nvar]\#\d+' is considered well-formed. =item 2. Checks if the synset exists by trying to find the offset for the synset =back =back This method, however, has a slightly different return value. Instead of merely breaking the synset into three parts, it returns the "safe" form of the synset. That is, if a synset has multiple word senses, this method returns the first word sense in that synset (this is so that other path-finding methods work properly). For example, if the input to this method is auto#n#1, the return value is ('car', 'n', 1, 2853224) since the sense 'car#n#1' is the first member of the synset to which 'auto#n#1' belongs. If any of these tests fails, then the error level is set to non-zero, a message is appended to the error string, and undef is returned. =cut sub validateSynset { my $self = shift; my $synset = shift; my ($word, $pos, $sense, $offset) = $self->SUPER::validateSynset ($synset); my $class = ref $self || $self; # check to see if previous call encountered an error: return undef if $self->{error}; my @synset = $self->{wn}->querySense ($synset, "syns"); my $safewps = shift @synset; unless (defined $safewps) { # safety check--we shouldn't ever get here. querySense shouldn't # return undef unless the input synset is bad, but we've already # checked that synset $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::validateSynset()) - "; $self->{errorString} .= "No synset appears to exist for $synset."; return undef; } unless ($safewps =~ /^([^\s\#]+)\#([nvar])\#(\d+)$/) { # we should never get here -- if QueryData doesn't return word senses # in the right format, then we're in a lot of trouble... nevertheless, # we check just to be sure $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::validateSynset()) - "; $self->{errorString} .= "Internal error: $safewps is not well-formed. Has WordNet or WordNet::QueryData changed format?"; return undef; } return ($1, $2, $3, $offset); } =back =head3 Private methods =over =item $measure->_getHypernymTrees($synset, $pos, $mode) This method takes as input a synset and returns a list of references to arrays where these arrays are paths from the input synset to the top of the taxonomy (*Root*#[nv]#1 if the root node is on). Parameters: a synset, a part-of-speech, and a mode. The mode must be either the string 'wps' or 'offset'. If the mode is 'wps', then the synset must be in wps format; otherwise, it must be an offset. Returns: a list of references to arrays. These arrays are paths (hypernym trees). =cut # Suroutine that returns an array of hypernym trees, given the offset of # the synset. Each hypernym tree is an array of offsets. # INPUT PARAMS : $offset .. Offset of the synset. # : $pos .. Part of speech. # RETURN VALUES : (@tree1, @tree2, ...) .. an array of Hypernym trees (offsets) sub _getHypernymTrees { my $self = shift; my $wn = $self->{wn}; my $synset = shift; my $pos = shift; my $mode = shift; my $curPath = shift; $curPath = {} if(!defined($curPath)); $curPath->{$synset} = 1; my $wordForm = $synset; if ($mode eq 'offset') { # check if the input synset is one of the imaginary root nodes if ($synset == 0) { return ([0]); } $wordForm = $wn->getSense($synset, $pos); } else { # check for root node if ($synset =~ /\*ROOT\*/i) { return ([$synset]); } } my @hypernyms = $wn->querySense($wordForm, "hypes"); my @returnArray = (); if($#hypernyms < 0) { my @tmpArray = $synset; if ($self->{rootNode}) { if ($mode eq 'offset') { unshift @tmpArray, 0; } else { unshift @tmpArray, ($pos eq 'n') ? $self->ROOT_N : $self->ROOT_V; } } push @returnArray, [@tmpArray]; } else { foreach my $hypernym (@hypernyms) { my $hypesynset = $mode eq 'offset' ? $wn->offset ($hypernym) : $hypernym; if(!defined($curPath->{$hypesynset})) { my %localCopy = %{$curPath}; my @tmpArray = $self->_getHypernymTrees ($hypesynset, $pos, $mode, \%localCopy); foreach my $element (@tmpArray) { push @$element, $synset; push @returnArray, [@$element]; } } if(scalar(@returnArray) <= 0) { my @tmpArray = $synset; if ($self->{rootNode}) { if ($mode eq 'offset') { unshift @tmpArray, 0; } else { unshift @tmpArray, ($pos eq 'n') ? $self->ROOT_N : $self->ROOT_V; } } push @returnArray, [@tmpArray]; } } } return @returnArray; } =item getLCSbyPath($synset1, $synset2, $pos, $mode) Given two input synsets, finds the least common subsumer (LCS) of them. If there are multiple candidates for the LCS (due to multiple inheritance), the LCS that results in the shortest path between in input concepts is chosen. Parameters: two synsets, a part of speech, and a mode. Returns: a list of references to arrays where each array has the from C<($lcs, $pathlength)>. $pathlength is the length of the path between the two input concepts. There can be multiple LCSs returned if there are ties for the shortest path between the two synsets. Returns undef on error. =cut sub getLCSbyPath { my $self = shift; my $synset1 = shift; my $synset2 = shift; my $pos = shift; my $mode = shift; my $class = ref $self || $self; my @paths = $self->getAllPaths ($synset1, $synset2, $pos, $mode); # if no paths were found, $paths[0] should be undefined unless (defined $paths[0]) { $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::getLCSbyPath()) - "; $self->{errorString} .= "No LCS found."; return undef; } if ($self->{trace}) { $self->{traceString} .= "Lowest Common Subsumer(s): "; } my @return; # put the best LCS(s) into @return; do some tracing at the same time. foreach my $pathref (@paths) { if ($self->{trace}) { # print path to trace string $self->printSet ($pos, $mode, $pathref->[0]); $self->{traceString} .= " (Length=".$pathref->[1].")\n"; } # push onto return array if this path length is tied for best if ($pathref->[1] <= $paths[0]->[1]) { push @return, [$pathref->[0], $pathref->[1]]; } } if ($self->{trace}) { $self->{traceString} .= "\n\n"; } return @return; } =item $measure->_getSubsumerFromTrees($treeref1, $treeref2, $mode) This subroutine returns takes two trees as produced by getHypernymTrees and returns the most specific subsumer from them. Parameters: two references to arrays, and a string indicating mode ('wps' or 'offset'). Returns: the subsumer or undef =cut sub _getSubsumerFromTrees { my $self = shift; my $array1 = shift; my $array2 = shift; my $mode = shift; my @tree1 = reverse @{$array1}; my @tree2 = reverse @{$array2}; my $class = ref $self || $self; my $tmpString = " " . join (" ", @tree1) . " "; foreach my $element (@tree2) { my $pattern = ($mode eq 'offset') ? qr/ 0*$element / : qr/ \Q$element\E /; if ($tmpString =~ /$pattern/) { return $element; } } # no common subsumer found, check to see if we are using a root node return undef unless $self->{rootNode}; $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{error} .= "\nWarning (${class}::getSubsumerFromTrees()) - "; $self->{errorString} .= "root node 'on' but no subsumer found."; return undef; } =item getDepth() This method is non-functional and likely to be moved to a different module soon. =cut sub getDepth { use Carp; croak "This method is non-functional"; my $self = shift; my $synset = shift; my $pos = shift; my $mode = shift; my $class = ref $self || $self; my $offset; if ($mode eq 'offset') { $offset = $synset; return 1 if $offset == 0; } elsif ($mode eq 'wps') { $offset = $self->{wn}->offset ($synset); return 1 if $synset =~ /^\*Root\*/i; } else { $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::getAllPaths()) - "; $self->{errorString} .= "Mode must be either 'offset' or 'wps'"; return undef; } my $depth = $self->{depths}->{$pos}->{$offset}; defined $depth and return $depth; $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::getDepth) - "; $self->{errorString} .= "$synset appears to have undefined depth."; return undef; } 1; __END__ =back =head2 Discussion Many of the methods in this module can work with either offsets or wps strings internally. There are several interesting consequences of each mode. =over =item 1. An offset is not a unique identifier for a synset, but neither is a wps string. An offset only indicates a byte offset in one of the WordNet data files (data.noun, data.verb, etc. on Unix-like systems). An offset along with a part of speech, however, does uniquely identify a synset. A word#pos#sense string, on the other hand, is the opposite extreme. A word#pos#sense string is an identifier for a unique word sense. A synset can have several word senses in it (i.e., a synset is a set of word senses that are synonymous). The synset {beer_mug#n#1, stein#n#1} has two word senses. The wps strings 'beer_mug#n#1' and 'stein#n#1' can both be used to refer to the synset. For simplicity, we usually just use the first wps string when referring to the synset. N.B., the wps representation was developed by WordNet::QueryData. =item 2. Early versions of WordNet::Similarity::* used offsets internally for finding paths, hypernym trees, subsumers, etc. The module WordNet::QueryData that is used by Similarity, however, accepts only wps strings as input to its querySense method, which is used to find hypernyms. We have found that it is more efficient (faster) to use wps strings internally. =back =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Jason Michelizzi, University of Minnesota Duluth mich0212 at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 BUGS None. =head1 SEE ALSO WordNet::Similarity(3) WordNet::Similarity::path(3) WordNet::Similarity::lch(3) WordNet::Similarity::wup(3) =head1 COPYRIGHT Copyright (c) 2005, Ted Pedersen, Siddharth Patwardhan and Jason Michelizzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/Similarity/lesk.pm0000644000175000017500000004371211015336622021046 0ustar sidsid# WordNet::Similarity::lesk.pm version 2.04 # (Last updated $Id: lesk.pm,v 1.29 2008/03/27 06:21:17 sidz1979 Exp $) # # Module to accept two WordNet synsets and to return a floating point # number that indicates how similar those two synsets are, using an # adaptation of the Lesk method as outlined in # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh # banerjee+ at cs.cmu.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity::lesk; =head1 NAME WordNet::Similarity::lesk - Perl module for computing semantic relatedness of word senses using gloss overlaps as described by Banerjee and Pedersen (2002) -- a method that adapts the Lesk approach to WordNet. =head1 SYNOPSIS use WordNet::Similarity::lesk; use WordNet::QueryData; my $wn = WordNet::QueryData->new(); my $lesk = WordNet::Similarity::lesk->new($wn); my $value = $lesk->getRelatedness("car#n#1", "bus#n#2"); ($error, $errorString) = $lesk->getError(); die "$errorString\n" if($error); print "car (sense 1) <-> bus (sense 2) = $value\n"; =head1 DESCRIPTION Lesk (1985) proposed that the relatedness of two words is proportional to to the extent of overlaps of their dictionary definitions. Banerjee and Pedersen (2002) extended this notion to use WordNet as the dictionary for the word definitions. This notion was further extended to use the rich network of relationships between concepts present is WordNet. This adapted lesk measure has been implemented in this module. =head2 Methods =over =cut use strict; use warnings; use Text::OverlapFinder; use WordNet::Similarity; use File::Spec; use WordNet::Similarity::GlossFinder; our @ISA = qw(WordNet::Similarity::GlossFinder); our $VERSION = '2.04'; WordNet::Similarity::addConfigOption ("normalize", 0, "i", 0); =item $measure->initialize($file) Overrides the initialize method in the parent class (GlossFinder.pm). This method essentially initializes the measure for use. Parameters: $file -- configuration file. Returns: none. =cut # Initialization of the WordNet::Similarity::lesk object... parses the config file and sets up # global variables, or sets them to default values. # INPUT PARAMS : $paramFile .. File containing the module specific params. # RETURN VALUES : (none) sub initialize { my $self = shift; my $paramFile; my $wn = $self->{wn}; my $class = ref $self || $self; # Stemming? Normalizing? $self->{stem} = 0; $self->{normalize} = 0; # Look for the default lesk relation file... if(!defined $self->{relationDefault}) { my $path; my $header; my @possiblePaths = (); # Look for all possible default data files installed. foreach $path (@INC) { # JM 1-16-04 -- modified to use File::Spec my $file = File::Spec->catfile($path, 'WordNet', 'lesk-relation.dat'); push @possiblePaths, $file if(-e $file); } # If there are multiple possibilities, get the one in the correct format. foreach $path (@possiblePaths) { next if(!open(RELATIONS, $path)); $header = ; $header =~ s/\s+//g; if($header =~ /RelationFile/) { $self->{relationDefault} = $path; close(RELATIONS); last; } close(RELATIONS); } } # Call the initialize method in the parent... $self->SUPER::initialize (@_); # initialize string compare module. No stemming in string # comparison, so put 0. #&string_compare_initialize(0, %stopHash); my @finder_args = (); if (defined $self->{stop}) { push @finder_args, stoplist => $self->{stop}; } # lesk doesn't use a comp file, so we can ignore that $self->{finder} = Text::OverlapFinder->new (@finder_args); } =item $lesk->traceOptions() This method is internally called to determine the extra options specified by this measure (apart from the default options specified in the WordNet::Similarity base class). Parameters: none. Returns: none. =cut # 12/5/03 JM (#1) # show all config options specific to this module sub traceOptions { my $self = shift; $self->{traceString} .= "normalize :: $self->{normalize}\n"; $self->SUPER::traceOptions(); } =item $lesk->getRelatedness Computes the relatedness of two word senses using the Extended Gloss Overlaps algorithm. Parameters: two word senses in "word#pos#sense" format. Returns: Unless a problem occurs, the return value is the relatedness score, which is greater-than or equal-to 0. If an error occurs, then the error level is set to non-zero and an error string is created (see the description of getError()). =cut sub getRelatedness { my $self = shift; my $wps1 = shift; my $wps2 = shift; my $wn = $self->{wn}; my $class = ref $self || $self; # Check the existence of the WordNet::QueryData object. unless($wn) { $self->{errorString} .= "\nError (${class}::getRelatedness()) - "; $self->{errorString} .= "A WordNet::QueryData object is required."; $self->{error} = 2; return undef; } # Using validation code from parseWps() in a super-class my $ret = $self->parseWps($wps1, $wps2); ref $ret or return undef; # Initialize traces. $self->{traceString} = ""; # Now check if the similarity value for these two synsets is in # fact in the cache... if so return the cached value. my $relatedness = $self->{doCache} ? $self->fetchFromCache ($wps1, $wps2) : undef; defined $relatedness and return $relatedness; # Now get down to really finding the relatedness of these two. # see if any traces reqd. if so, put in the synset arrays. if($self->{trace}) { # ah so we do need SOME traces! put in the synset names. $self->{traceString} = "Synset 1: $wps1\n"; $self->{traceString} .= "Synset 2: $wps2\n"; } # NOTE: Thanks to Wybo Wiersma for contributing optimizations # in the following code. # Get the gloss strings from the get_wn_info module my ($firstStringArray, $secondStringArray, $weightsArray, $functionsStringArray) = $self->getSuperGlosses($wps1, $wps2); my $score = 0; for(my $i = 0; $i < scalar(@{$weightsArray}); $i++) { my $functionsScore = 0; my $funcStringPrinted = 0; # so those are the two strings for this relation pair. get the # string overlaps my ($overlaps, $wc1, $wc2); if(defined($firstStringArray->[$i]) && defined($secondStringArray->[$i]) && $firstStringArray->[$i] ne "" && $secondStringArray->[$i] ne "") { ($overlaps, $wc1, $wc2) = $self->{finder}->getOverlaps($firstStringArray->[$i], $secondStringArray->[$i]); } my $overlapsTraceString = ""; my $key; foreach $key (keys %{$overlaps}) { # find the length of the key, square it, multiply with its # value and finally with the weight associated with this # relation pair to get the score for this particular # overlap. my @tempArray = split(/\s+/, $key); my $value = ($#tempArray + 1) * ($#tempArray + 1) * $overlaps->{$key}; $functionsScore += $value; # put this overlap into the trace string, if necessary if($self->{trace} == 1) { $overlapsTraceString .= "$overlaps->{$key} x \"$key\" "; } } # normalize the function score computed above if required if ($self->{normalize} && defined($wc1) && defined($wc2) && ($wc1 * $wc2)) { $functionsScore /= $wc1 * $wc2; } # weight functionsScore with weight of this function $functionsScore *= $weightsArray->[$i]; # add to main score for this sense $score += $functionsScore; # if we have an overlap, send functionsString, functionsScore # and overlapsTraceString to trace string, if trace string requested if($self->{trace} == 1 && $overlapsTraceString ne "") { $self->{traceString} .= "".($functionsStringArray->[$i]).": $functionsScore\n"; $funcStringPrinted = 1; $self->{traceString} .= "Overlaps: $overlapsTraceString\n"; } # check if the two strings need to be reported in the trace. if ($self->{trace} == 2) { if(!$funcStringPrinted) { $self->{traceString} .= "".($functionsStringArray->[$i])."\n"; $funcStringPrinted = 1; } $self->{traceString} .= "String 1: \"".($firstStringArray->[$i])."\"\n"; $self->{traceString} .= "String 2: \"".($secondStringArray->[$i])."\"\n"; } } # that does all the scoring. Put in cache if doing caching. Then # return the score. $self->{doCache} and $self->storeToCache($wps1, $wps2, $score); return $score; } 1; __END__ =back =head2 Usage The semantic relatedness modules in this distribution are built as classes that define the following methods: new() getRelatedness() getError() getTraceString() See the WordNet::Similarity(3) documentation for details of these methods. =head3 Typical Usage Examples To create an object of the lesk measure, we would have the following lines of code in the Perl program. use WordNet::Similarity::lesk; $measure = WordNet::Similarity::lesk->new($wn, '/home/sid/lesk.conf'); The reference of the initialized object is stored in the scalar variable '$measure'. '$wn' contains a WordNet::QueryData object that should have been created earlier in the program. The second parameter to the 'new' method is the path of the configuration file for the lesk measure. If the 'new' method is unable to create the object, '$measure' would be undefined. This, as well as any other error/warning may be tested. die "Unable to create object.\n" if(!defined $measure); ($err, $errString) = $measure->getError(); die $errString."\n" if($err); To find the semantic relatedness of the first sense of the noun 'car' and the second sense of the noun 'bus' using the measure, we would write the following piece of code: $relatedness = $measure->getRelatedness('car#n#1', 'bus#n#2'); To get traces for the above computation: print $measure->getTraceString(); However, traces must be enabled using configuration files. By default traces are turned off. =head1 CONFIGURATION FILE The behavior of the measures of semantic relatedness can be controlled by using configuration files. These configuration files specify how certain parameters are initialized within the object. A configuration file may be specified as a parameter during the creation of an object using the new method. The configuration files must follow a fixed format. Every configuration file starts with the name of the module ON THE FIRST LINE of the file. For example, a configuration file for the lesk module will have on the first line 'WordNet::Similarity::lesk'. This is followed by the various parameters, each on a new line and having the form 'name::value'. The 'value' of a parameter is optional (in case of boolean parameters). In case 'value' is omitted, we would have just 'name::' on that line. Comments are supported in the configuration file. Anything following a '#' is ignored till the end of the line. The module parses the configuration file and recognizes the following parameters: =over =item trace The value of this parameter specifies the level of tracing that should be employed for generating the traces. This value is an integer equal to 0, 1, or 2. If the value is omitted, then the default value, 0, is used. A value of 0 switches tracing off. A value of 1 or 2 switches tracing on. A value of 1 displays as traces only the gloss overlaps found. A value of 2 displays as traces all the text being compared. =item cache The value of this parameter specifies whether or not caching of the relatedness values should be performed. This value is an integer equal to 0 or 1. If the value is omitted, then the default value, 1, is used. A value of 0 switches caching 'off', and a value of 1 switches caching 'on'. =item maxCacheSize The value of this parameter indicates the size of the cache, used for storing the computed relatedness value. The specified value must be a non-negative integer. If the value is omitted, then the default value, 5,000, is used. Setting maxCacheSize to zero has the same effect as setting cache to zero, but setting cache to zero is likely to be more efficient. Caching and tracing at the same time can result in excessive memory usage because the trace strings are also cached. If you intend to perform a large number of relatedness queries, then you might want to turn tracing off. =item relation The value of this parameter is the path to a file that contains a list of WordNet relations. The path may be either an absolute path or a relative path. The lesk measure combines glosses of synsets related to the target synsets by these relations and then searches for overlaps in these "super-glosses." WARNING: the format of the relation file is different for the vector and lesk measures. =item stop The value of this parameter the path of a file containing a list of stop words that should be ignored in the glosses. The path may be either an absolute path or a relative path. =item stem The value of this parameter indicates whether or not stemming should be performed. The value must be an integer equal to 0 or 1. If the value is omitted, then the default value, 0, is used. A value of 1 switches 'on' stemming, and a value of 0 switches stemming 'off'. When stemming is enabled, all the words of the glosses are stemmed before their vectors are created for the vector measure or their overlaps are compared for the lesk measure. =item normalize The value of this parameter indicates whether or not normalization of scores is performed. The value must be an integer equal to 0 or 1. If the value is omitted, then the default value, 0, is assumed. A value of 1 switches 'on' normalizing of the score, and a value of 0 switches normalizing 'off'. When normalizing is enabled, the score obtained by counting the gloss overlaps is normalized by the size of the glosses. The details are described in Banerjee and Pedersen (2002). =back =head1 RELATION FILE FORMAT The relation file starts with the string "RelationFile" on the first line of the file. Following this, on each consecutive line, a relation is specified in the form -- func(func(func... (func)...))-func(func(func... (func)...)) [weight] Where "func" can be any one of the following functions: hype() = Hypernym of hypo() = Hyponym of holo() = Holonym of mero() = Meronym of attr() = Attribute of also() = Also see sim() = Similar enta() = Entails caus() = Causes part() = Particle pert() = Pertainym of glos = gloss (without example) example = example (from the gloss) glosexample = gloss + example syns = synset of the concept Each of these specifies a WordNet relation. And the outermost function in the nesting can only be one of glos, example, glosexample or syns. The set of functions to the left of the "-" are applied to the first word sense. The functions to the right of the "-" are applied to the second word sense. An optional weight can be specified to weigh the contribution of that relation in the overall score. For example, glos(hype(hypo))-example(hype) 0.5 means that the gloss of the hypernym of the hyponym of the first synset is overlapped with the example of the hypernym of the second synset to get the lesk score. This score is weighted 0.5. If "glos", "example", "glosexample" or "syns" is not provided as the outermost function of the nesting, the measure assumes "glos" as the default. So, glos(hypo(also))-glos(holo(attr)) and hypo(also)-holo(attr) are treated the same by the measure. =head1 SEE ALSO perl(1), WordNet::Similarity(3), WordNet::QueryData(3) http://www.cs.utah.edu/~sidd http://wordnet.princeton.edu http://www.ai.mit.edu/~jrennie/WordNet http://groups.yahoo.com/group/wn-similarity =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 BUGS None. To report bugs, go to http://groups.yahoo.com/group/wn-similarity/ or e-mail "S". =head1 COPYRIGHT AND LICENSE Copyright (c) 2005, Ted Pedersen, Satanjeev Banerjee and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/Similarity/hso.pm0000644000175000017500000006432611015336622020705 0ustar sidsid# WordNet::Similarity::hso.pm version 2.04 # (Last updated $Id: hso.pm,v 1.16 2008/03/27 06:21:17 sidz1979 Exp $) # # Semantic Similarity Measure package implementing the measure # described by Hirst and St-Onge (1998). # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity::hso; =head1 NAME WordNet::Similarity::hso - Perl module for computing semantic relatedness of word senses using the method described by Hirst and St-Onge (1998). =head1 SYNOPSIS use WordNet::Similarity::hso; use WordNet::QueryData; my $wn = WordNet::QueryData->new(); my $object = WordNet::Similarity::hso->new($wn); my $value = $object->getRelatedness("car#n#1", "bus#n#2"); ($error, $errorString) = $object->getError(); die "$errorString\n" if($error); print "car (sense 1) <-> bus (sense 2) = $value\n"; =head1 DESCRIPTION This module computes the semantic relatedness of word senses according to the method described by Hirst and St-Onge (1998). In their paper they describe a method to identify 'lexical chains' in text. They measure the semantic relatedness of words in text to identify the links of the lexical chains. This measure of relatedness has been implemented in this module. =head2 Methods =over =cut use strict; use Exporter; use WordNet::Similarity; use vars qw(@ISA @EXPORT @EXPORT_OK %EXPORT_TAGS); @ISA = qw/WordNet::Similarity/; %EXPORT_TAGS = (); @EXPORT_OK = (); @EXPORT = (); our $VERSION = '2.04'; =item $hso->setPosList() This method is internally called to determine the parts of speech this measure is capable of dealing with. Parameters: none. Returns: none. =cut sub setPosList { my $self = shift; $self->{n} = 1; $self->{v} = 1; $self->{a} = 1; $self->{r} = 1; } # How medium strong relations work (from an e-mail by Sid): # Basically, for the # medium strong relations, I had to start from the given node and explore # all "legal" paths starting from that node and ending on $offset2. # To do this I created a recursive function _medStrong, that is called like # so in line 247: # my $score = $self->_medStrong(0, 0, 0, $offset1, $offset1, $offset2); # The first parameter is the "state" (=0 initially). This parameter keeps # track of what part of the path we are on. For example, one of the legal # paths goes upwards, then horizontally, and then downwards. So along the # upwards section of the path, "state" would be 0. Along the horizontal # section of the path, the "state" becomes 1. Along the downwards section # the "state" is 2. Similarly, the state recognizes different legal paths. # The second parameter is "distance" (=0 initially). This parameter keeps # track of the length of the path that we are at. Since the maximum length # possible is 5, we stop the recursive function when the path length reaches # 5. # The third parameter is "chdir" (=0 initally) counts the number of changes # in direction. This value is required in the formula for the medium-strong # relation, so is updated everytime there is a change in path direction. # The fourth parameter is "from node" (=offset1 initally). The recursive # function uses the from node to decide which are the next possible nodes in # a given path. Each of these are then explored creating n recursive copies. # The fifth parameter is "path". It is a string of the form # "$offset [DUH] $offset [DUH] $offset..." # that stores a string representation of the path. This string is used in # the traces (if the path turns out to be a legal path). A path would turn # out to be a legal path if it is generated by our recursive function and # the first and last nodes in the path are $offset1 and $offset2 of the # relatedness measure. # The sixth parameter to the function is "endOffset". This is basically # "$offset2" throughout and is used by the recursive function to determine # if the current node in the path is the last node of that path. # The basic idea of the recursive function is at each stage, to determine # the next possible nodes in the path, from the current node and state. Then # _medStrong is called on each of these. The recursive function does a # search through a tree of "partially" legal paths, until a completely legal # path is found. Multiple such paths may be found. The highest of the scores # of these is returned. # _medStrong returns the maximum length of a legal path that was found in a # given subtree of the recursive search. These return values are used by # _medStrong and the highest of these is returned. =item $hso->getRelatedness ($synset1, $synset2) Computes the relatedness of two word senses using the method of Hirst & St-Onge. Parameters: two word senses in "word#pos#sense" format. Returns: Unless a problem occurs, the return value is the relatedness score, which is greater-than or equal-to 0 and less-than or equal-to 16. If an error occurs, then the error level is set to non-zero and an error string is created (see the description of getError()). =cut sub getRelatedness { my $self = shift; my $class = ref $self || $self; my $wps1 = shift; my $wps2 = shift; my $wn = $self->{wn}; # Check the existence of the WordNet::QueryData object. if(!$wn) { $self->{errorString} .= "\nError (${class}::getRelatedness()) - "; $self->{errorString} .= "A WordNet::QueryData object is required."; $self->{error} = 2; return undef; } # Initialize traces. $self->{traceString} = ""; # Undefined input cannot go unpunished. if(!$wps1 || !$wps2) { $self->{errorString} .= "\nWarning (${class}::getRelatedness()) - "; $self->{errorString} .= "Undefined input values."; $self->{error} = ($self->{error} < 1) ? 1 : $self->{error}; return undef; } # Security check -- are the input strings in the correct format (word#pos#sense). my ($pos1, $pos2); my ($word1, $word2); if($wps1 =~ /^(\S+)\#([nvar])\#\d+$/) { $word1 = $1; $pos1 = $2; } else { $self->{errorString} .= "\nWarning (${class}::getRelatedness()) - "; $self->{errorString} .= "Input not in word\#pos\#sense format."; $self->{error} = ($self->{error} < 1) ? 1 : $self->{error}; return undef; } if($wps2 =~ /^(\S+)\#([nvar])\#\d+$/) { $word2 = $1; $pos2 = $2; } else { $self->{errorString} .= "\nWarning (${class}::getRelatedness()) - "; $self->{errorString} .= "Input not in word\#pos\#sense format."; $self->{error} = ($self->{error} < 1) ? 1 : $self->{error}; return undef; } # Which parts of speech do we have. if($pos1 !~ /[nvar]/ || $pos2 !~ /[nvar]/) { $self->{errorString} .= "\nWarning (${class}::getRelatedness()) - "; $self->{errorString} .= "Unknown part(s) of speech."; $self->{error} = ($self->{error} < 1) ? 1 : $self->{error}; return 0; } # Now check if the similarity value for these two synsets is in # fact in the cache... if so return the cached value. my $relatedness = $self->{doCache} ? $self->fetchFromCache ($wps1, $wps2) : undef; defined $relatedness and return $relatedness; # Now get down to really finding the relatedness of these two. my $offset1 = $wn->offset($wps1); my $offset2 = $wn->offset($wps2); if(!$offset1 || !$offset2) { $self->{errorString} .= "\nWarning (${class}::getRelatedness()) - "; $self->{errorString} .= "Input senses not found in WordNet."; $self->{error} = ($self->{error} < 1) ? 1 : $self->{error}; return undef; } $offset1 = $offset1.$pos1; $offset2 = $offset2.$pos2; if($offset1 eq $offset2) { # [trace] if($self->{trace}) { $self->{'traceString'} .= "Strong Rel (Synset Match) : "; $self->printSet($pos1, 'offset', $offset1); $self->{'traceString'} .= "\n\n"; } # [/trace] $self->{doCache} and $self->storeToCache ($wps1, $wps2, 16); return 16; } my @horiz1 = &_getHorizontalOffsetsPOS($self->{wn}, $offset1); my @upward1 = &_getUpwardOffsetsPOS($self->{wn}, $offset1); my @downward1 = &_getDownwardOffsetsPOS($self->{wn}, $offset1); my @horiz2 = &_getHorizontalOffsetsPOS($self->{wn}, $offset2); my @upward2 = &_getUpwardOffsetsPOS($self->{wn}, $offset2); my @downward2 = &_getDownwardOffsetsPOS($self->{wn}, $offset2); # [trace] if($self->{trace}) { $self->{traceString} .= "Horizontal Links of "; $self->printSet($pos1, 'offset', $offset1); $self->{traceString} .= ": "; $self->printSet($pos1, 'offset', @horiz1); $self->{traceString} .= "\nUpward Links of "; $self->printSet($pos1, 'offset', $offset1); $self->{traceString} .= ": "; $self->printSet($pos1, 'offset', @upward1); $self->{traceString} .= "\nDownward Links of "; $self->printSet($pos1, 'offset', $offset1); $self->{traceString} .= ": "; $self->printSet($pos1, 'offset', @downward1); $self->{traceString} .= "\nHorizontal Links of "; $self->printSet($pos2, 'offset', $offset2); $self->{traceString} .= ": "; $self->printSet($pos2, 'offset', @horiz2); $self->{traceString} .= "\nUpward Links of "; $self->printSet($pos2, 'offset', $offset2); $self->{traceString} .= ": "; $self->printSet($pos2, 'offset', @upward2); $self->{traceString} .= "\nDownward Links of "; $self->printSet($pos2, 'offset', $offset2); $self->{traceString} .= ": "; $self->printSet($pos2, 'offset', @downward2); $self->{traceString} .= "\n\n"; } # [/trace] if(&_isIn($offset1, @horiz2) || &_isIn($offset2, @horiz1)) { # [trace] if($self->{trace}) { $self->{traceString} .= "Strong Rel (Horizontal Match) : \n"; $self->{traceString} .= "Horizontal Links of "; $self->printSet($pos1, 'offset', $offset1); $self->{traceString} .= ": "; $self->printSet($pos1, 'offset', @horiz1); $self->{traceString} .= "\nHorizontal Links of "; $self->printSet($pos2, 'offset', $offset2); $self->{traceString} .= ": "; $self->printSet($pos2, 'offset', @horiz2); $self->{traceString} .= "\n\n"; } # [/trace] $self->{doCache} and $self->storeToCache ($wps1, $wps2, 16); return 16; } if($word1 =~ /$word2/ || $word2 =~ /$word1/) { if(&_isIn($offset1, @upward2) || &_isIn($offset1, @downward2)) { # [trace] if($self->{trace}) { $self->{traceString} .= "Strong Rel (Compound Word Match) : \n"; $self->{traceString} .= "All Links of $word1: "; $self->printSet($pos1, 'offset', @horiz1, @upward1, @downward1); $self->{traceString} .= "\nAll Links of $word2: "; $self->printSet($pos2, 'offset', @horiz2, @upward2, @downward2); $self->{traceString} .= "\n\n"; } # [/trace] $self->{doCache} and $self->storeToCache ($wps1, $wps2, 16); return 16; } if(&_isIn($offset2, @upward1) || &_isIn($offset2, @downward1)) { # [trace] if($self->{trace}) { $self->{traceString} .= "Strong Rel (Compound Word Match) : \n"; $self->{traceString} .= "All Links of $word1: "; $self->printSet($pos1, 'offset', @horiz1, @upward1, @downward1); $self->{traceString} .= "\nAll Links of $word2: "; $self->printSet($pos2, 'offset', @horiz2, @upward2, @downward2); $self->{traceString} .= "\n\n"; } # [/trace] $self->{doCache} and $self->storeToCache ($wps1, $wps2, 16); } } # Conditions for Medium-Strong relations ... my $score = $self->_medStrong(0, 0, 0, $offset1, $offset1, $offset2); $self->{doCache} and $self->storeToCache ($wps1, $wps2, $score); return $score; } # Subroutine to get offsets(POS) of all horizontal links from a given # word (offset(POS)). All horizontal links specified are -- # Also See, Antonymy, Attribute, Pertinence, Similarity. # INPUT PARAMS : $wn .. WordNet::QueryData object. # $offset .. An offset-pos (e.g. 637554v) # RETURN VALUES : @offsets .. Array of offset-pos (e.g. 736438n) sub _getHorizontalOffsetsPOS { my $wn; my $offset; my $synset; my $pos; my $wordForm; my @partsOfSpeech; my @synsets; my @offsets; $wn = shift; $offset = shift; @offsets = (); if($offset =~ /^([0-9]+)([a-z])$/) { $offset = $1; $pos = $2; } else { return @offsets; } $wordForm = $wn->getSense($offset,$pos); @synsets = $wn->queryWord($wordForm, "also"); push @synsets, $wn->queryWord($wordForm, "ants"); push @synsets, $wn->querySense($wordForm, "attr"); push @synsets, $wn->queryWord($wordForm, "pert"); push @synsets, $wn->querySense($wordForm, "sim"); foreach $synset (@synsets) { $pos = $synset; if($pos =~ /.*\#([a-z])\#.*/) { $pos = $1; push @offsets, $wn->offset($synset).$pos; } } return @offsets; } # Subroutine that returns all offsetPOSs that are linked # to a given synset by upward links. Upward link types -- # Hypernymy, Meronymy # INPUT PARAMS : $wn .. WordNet::QueryData object. # $offset .. OffsetPOS of the synset. # RETURN VALUES : @offsets .. Array of offsetPOSs. sub _getUpwardOffsetsPOS { my $wn; my $offset; my $synset; my $pos; my $wordForm; my @partsOfSpeech; my @synsets; my @offsets; $wn = shift; $offset = shift; @offsets = (); if($offset =~ /^([0-9]+)([a-z])$/) { $offset = $1; $pos = $2; } else { return @offsets; } $wordForm = $wn->getSense($offset,$pos); @synsets = $wn->querySense($wordForm, "hypes"); push @synsets, $wn->querySense($wordForm, "mero"); foreach $synset (@synsets) { $pos = $synset; if($pos =~ /.*\#([a-z])\#.*/) { $pos = $1; push @offsets, $wn->offset($synset).$pos; } } return @offsets; } # Subroutine that returns all offsetPOSs that are linked # to a given synset by downward links. Downward link types -- # Cause, Entailment, Holonymy, Hyponymy. # INPUT PARAMS : $wn .. WordNet::QueryData object. # $offset .. OffsetPOS of the synset. # RETURN VALUES : @offsets .. Array of offsetPOSs. sub _getDownwardOffsetsPOS { my $wn; my $offset; my $synset; my $pos; my $wordForm; my @partsOfSpeech; my @synsets; my @offsets; $wn = shift; $offset = shift; @offsets = (); if($offset =~ /^([0-9]+)([a-z])$/) { $offset = $1; $pos = $2; } else { return @offsets; } $wordForm = $wn->getSense($offset,$pos); @synsets = $wn->querySense($wordForm, "holo"); push @synsets, $wn->querySense($wordForm, "hypos"); push @synsets, $wn->querySense($wordForm, "enta"); push @synsets, $wn->querySense($wordForm, "caus"); foreach $synset (@synsets) { $pos = $synset; if($pos =~ /.*\#([a-z])\#.*/) { $pos = $1; push @offsets, $wn->offset($synset).$pos; } } return @offsets; } # Subroutine that checks if an offset is in a given # set of offsets. # INPUT PARAMS : $offset, @offsets .. The offset and the set of # offsets. # RETURN VALUES : 0 or 1. sub _isIn { my $op1; my @op2; my $line; $op1 = shift; @op2 = @_; $line = " ".join(" ", @op2)." "; if($line =~ / $op1 /) { return 1; } return 0; } # Recursive subroutine to check the existence of a Medium-Strong # relation between two synsets. # INPUT PARAMS : $state, $distance, $chdir, $offset, $path, $endOffset # .. The state of the state machine. # Similarity (links) covered thus far. # Number of changes in direction thus far. # Current node. # Path so far. # Last offset. # RETURN VALUES : $weight .. weight of the path found. sub _medStrong { my $self; my $state; my $distance; my $chdir; my $from; my $path; my $endOffset; my $retT; my $retH; my $retU; my $retD; my $synset; my $maxVal; my @horiz; my @upward; my @downward; $self = shift; $state = shift; $distance = shift; $chdir = shift; $from = shift; $path = shift; $endOffset = shift; if($from eq $endOffset && $distance > 1) { # [trace] if($self->{'trace'}) { $self->{'traceString'} .= "MedStrong relation path... \n"; while($path =~ /([0-9]+)([nvar]?)\s*(\[[DUH]\])?\s*/g) { $self->printSet($2, 'offset', $1); $self->{traceString} .= " $3 " if($3); } $self->{traceString} .= "\n"; } # [/trace] return 8 - $distance - $chdir; } if($distance >= 5) { return 0; } if($state == 0) { @horiz = &_getHorizontalOffsetsPOS($self->{'wn'}, $from); @upward = &_getUpwardOffsetsPOS($self->{'wn'}, $from); @downward = &_getDownwardOffsetsPOS($self->{'wn'}, $from); $retU = 0; foreach $synset (@upward) { $retT = $self->_medStrong(1, $distance+1, 0, $synset, $path." [U] ".$synset, $endOffset); $retU = $retT if($retT > $retU); } $retD = 0; foreach $synset (@downward) { $retT = $self->_medStrong(2, $distance+1, 0, $synset, $path." [D] ".$synset, $endOffset); $retD = $retT if($retT > $retD); } $retH = 0; foreach $synset (@horiz) { $retT = $self->_medStrong(3, $distance+1, 0, $synset, $path." [H] ".$synset, $endOffset); $retH = $retT if($retT > $retH); } return $retU if($retU > $retD && $retU > $retH); return $retD if($retD > $retH); return $retH; } if($state == 1) { @horiz = &_getHorizontalOffsetsPOS($self->{'wn'}, $from); @upward = &_getUpwardOffsetsPOS($self->{'wn'}, $from); @downward = &_getDownwardOffsetsPOS($self->{'wn'}, $from); $retU = 0; foreach $synset (@upward) { $retT = $self->_medStrong(1, $distance+1, 0, $synset, $path." [U] ".$synset, $endOffset); $retU = $retT if($retT > $retU); } $retD = 0; foreach $synset (@downward) { $retT = $self->_medStrong(4, $distance+1, 1, $synset, $path." [D] ".$synset, $endOffset); $retD = $retT if($retT > $retD); } $retH = 0; foreach $synset (@horiz) { $retT = $self->_medStrong(5, $distance+1, 1, $synset, $path." [H] ".$synset, $endOffset); $retH = $retT if($retT > $retH); } return $retU if($retU > $retD && $retU > $retH); return $retD if($retD > $retH); return $retH; } if($state == 2) { @horiz = &_getHorizontalOffsetsPOS($self->{'wn'}, $from); @downward = &_getDownwardOffsetsPOS($self->{'wn'}, $from); $retD = 0; foreach $synset (@downward) { $retT = $self->_medStrong(2, $distance+1, 0, $synset, $path." [D] ".$synset, $endOffset); $retD = $retT if($retT > $retD); } $retH = 0; foreach $synset (@horiz) { $retT = $self->_medStrong(6, $distance+1, 0, $synset, $path." [H] ".$synset, $endOffset); $retH = $retT if($retT > $retH); } return ($retD > $retH) ? $retD : $retH; } if($state == 3) { @horiz = &_getHorizontalOffsetsPOS($self->{'wn'}, $from); @downward = &_getDownwardOffsetsPOS($self->{'wn'}, $from); $retD = 0; foreach $synset (@downward) { $retT = $self->_medStrong(7, $distance+1, 0, $synset, $path." [D] ".$synset, $endOffset); $retD = $retT if($retT > $retD); } $retH = 0; foreach $synset (@horiz) { $retT = $self->_medStrong(3, $distance+1, 0, $synset, $path." [H] ".$synset, $endOffset); $retH = $retT if($retT > $retH); } return ($retD > $retH) ? $retD : $retH; } if($state == 4) { @downward = &_getDownwardOffsetsPOS($self->{'wn'}, $from); $retD = 0; foreach $synset (@downward) { $retT = $self->_medStrong(4, $distance+1, 1, $synset, $path." [D] ".$synset, $endOffset); $retD = $retT if($retT > $retD); } return $retD; } if($state == 5) { @horiz = &_getHorizontalOffsetsPOS($self->{'wn'}, $from); @downward = &_getDownwardOffsetsPOS($self->{'wn'}, $from); $retD = 0; foreach $synset (@downward) { $retT = $self->_medStrong(4, $distance+1, 2, $synset, $path." [D] ".$synset, $endOffset); $retD = $retT if($retT > $retD); } $retH = 0; foreach $synset (@horiz) { $retT = $self->_medStrong(5, $distance+1, 1, $synset, $path." [H] ".$synset, $endOffset); $retH = $retT if($retT > $retH); } return ($retD > $retH) ? $retD : $retH; } if($state == 6) { @horiz = &_getHorizontalOffsetsPOS($self->{'wn'}, $from); $retH = 0; foreach $synset (@horiz) { $retT = $self->_medStrong(6, $distance+1, 1, $synset, $path." [H] ".$synset, $endOffset); $retH = $retT if($retT > $retH); } return $retH; } if($state == 7) { @downward = &_getDownwardOffsetsPOS($self->{'wn'}, $from); $retD = 0; foreach $synset (@downward) { $retT = $self->_medStrong(7, $distance+1, 1, $synset, $path." [D] ".$synset, $endOffset); $retD = $retT if($retT > $retD); } return $retD; } return 0; } 1; __END__ =back =head2 Usage The semantic relatedness modules in this distribution are built as classes that define the following methods: new() getRelatedness() getError() getTraceString() See the WordNet::Similarity(3) documentation for details of these methods. =head3 Typical Usage Examples To create an object of the hso measure, we would have the following lines of code in the Perl program. use WordNet::Similarity::hso; $measure = WordNet::Similarity::hso->new($wn, '/home/sid/hso.conf'); The reference of the initialized object is stored in the scalar variable '$measure'. '$wn' contains a WordNet::QueryData object that should have been created earlier in the program. The second parameter to the 'new' method is the path of the configuration file for the hso measure. If the 'new' method is unable to create the object, '$measure' would be undefined. This, as well as any other error/warning may be tested. die "Unable to create object.\n" if(!defined $measure); ($err, $errString) = $measure->getError(); die $errString."\n" if($err); To find the semantic relatedness of the first sense of the noun 'car' and the second sense of the noun 'bus' using the measure, we would write the following piece of code: $relatedness = $measure->getRelatedness('car#n#1', 'bus#n#2'); To get traces for the above computation: print $measure->getTraceString(); However, traces must be enabled using configuration files. By default traces are turned off. =head1 CONFIGURATION FILE The behavior of the measures of semantic relatedness can be controlled by using configuration files. These configuration files specify how certain parameters are initialized within the object. A configuration file may be specififed as a parameter during the creation of an object using the new method. The configuration files must follow a fixed format. Every configuration file starts with the name of the module ON THE FIRST LINE of the file. For example, a configuration file for the hso module will have on the first line 'WordNet::Similarity::hso'. This is followed by the various parameters, each on a new line and having the form 'name::value'. The 'value' of a parameter is optional (in case of boolean parameters). In case 'value' is omitted, we would have just 'name::' on that line. Comments are supported in the configuration file. Anything following a '#' is ignored till the end of the line. The module parses the configuration file and recognizes the following parameters: =over =item trace The value of this parameter specifies the level of tracing that should be employed for generating the traces. This value is an integer equal to 0, 1, or 2. If the value is omitted, then the default value, 0, is used. A value of 0 switches tracing off. A value of 1 or 2 switches tracing on. A trace of level 1 means the synsets are represented as word#pos#sense strings, while for level 2, the synsets are represented as word#pos#offset strings. =item cache The value of this parameter specifies whether or not caching of the relatedness values should be performed. This value is an integer equal to 0 or 1. If the value is omitted, then the default value, 1, is used. A value of 0 switches caching 'off', and a value of 1 switches caching 'on'. =item maxCacheSize The value of this parameter indicates the size of the cache, used for storing the computed relatedness value. The specified value must be a non-negative integer. If the value is omitted, then the default value, 5,000, is used. Setting maxCacheSize to zero has the same effect as setting cache to zero, but setting cache to zero is likely to be more efficient. Caching and tracing at the same time can result in excessive memory usage because the trace strings are also cached. If you intend to perform a large number of relatedness queries, then you might want to turn tracing off. =back =head1 SEE ALSO perl(1), WordNet::Similarity(3), WordNet::QueryData(3) http://www.cs.utah.edu/~sidd http://wordnet.princeton.edu http://www.ai.mit.edu/~jrennie/WordNet http://groups.yahoo.com/group/wn-similarity =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 BUGS None. =head1 COPYRIGHT AND LICENSE Copyright (c) 2005, Ted Pedersen and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/Similarity/path.pm0000644000175000017500000003214011015336622021035 0ustar sidsid# WordNet::Similarity::path.pm version 2.04 # (Last updated $Id: path.pm,v 1.19 2008/03/27 06:21:17 sidz1979 Exp $) # # N.B., this module was formerly named edge.pm # # Semantic Similarity Measure package implementing a simple # path-length (node-counting) semantic relatedness measure. # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # Jason Michelizzi, Univeristy of Minnesota Duluth # mich0212 at d.umn.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity::path; =head1 NAME WordNet::Similarity::path - Perl module for computing semantic relatedness of word senses by counting nodes in the noun and verb WordNet 'is-a' hierarchies. =head1 SYNOPSIS use WordNet::Similarity::path; use WordNet::QueryData; my $wn = WordNet::QueryData->new(); my $path = WordNet::Similarity::path->new($wn); my $value = $measure->getRelatedness("car#n#1", "bus#n#2"); my ($error, $errorString) = $measure->getError(); die "$errorString\n" if($error); print "car (sense 1) <-> bus (sense 2) = $value\n"; =head1 DESCRIPTION This module computes the semantic relatedness of word senses by counting the number of nodes along the shortest path between the senses in the 'is-a' hierarchies of WordNet. The path lengths include the end nodes. For example, the path between shrub#n#1 and tree#n#1 is S. Since a longer path length indicate less relatedness, the relatedness value returned is the multiplicative inverse of the path length (distance) between the two concepts: S. If the two concepts are identical, then the distance between them is one; therefore, their relatedness is also 1. If no path is found, then a large negative number is returned and an error is generated (see C). The following methods are defined: =over =cut use strict; use WordNet::Similarity::PathFinder; use constant MAX_DIST => 100; our @ISA = qw(WordNet::Similarity::PathFinder); our $VERSION = '2.04'; =item $path->getRelatedness() Computes the relatedness of two word senses using a node counting scheme. The relatedness score is inversely proportional to the number of nodes along the shortest path between the two word senses. Parameters: two word senses in "word#pos#sense" format. Returns: Unless a problem occurs, the return value is the relatedness score, which belongs to the interval (0, 1]. If no path exists between the two word senses, then a large negative number is returned. If an error occurs, then the error level is set to non-zero and an error string is created (see the description of getError()). Note: the error level will also be set to 1 and an error string will be created if no path exists between the words. =cut sub getRelatedness { my $self = shift; my $wps1 = shift; my $wps2 = shift; my $wn = $self->{wn}; my $class = ref $self || $self; # Initialize traces. $self->{traceString} = "" if($self->{'trace'}); # JM 1-21-04 # moved input validation code to WordNet::Similarity::parseInput() my $ret = $self->parseWps ($wps1, $wps2); ref $ret or return $ret; my ($word1, $pos1, $sense1, $offset1, $word2, $pos2, $sense2, $offset2) = @{$ret}; $wps1 = "$word1#$pos1#$sense1"; $wps2 = "$word2#$pos2#$sense2"; # Now check if the similarity value for these two synsets is in # fact in the cache... if so return the cached value. my $relatedness = $self->{doCache} ? $self->fetchFromCache ($wps1, $wps2) : undef; defined $relatedness and return $relatedness; # JM 1/23/04 # Most of the code that does the work of finding the LCS and # hypernym trees has been moved into LCSFinder and PathFinder #my ($dist, $pathref) = $self->getShortestPath ($offset1, $offset2, # $pos1, 'offset'); # there can be multiple shortest paths (i.e., paths of the same length) my @paths = $self->getShortestPath ($offset1, $offset2, $pos1, 'offset'); my $path = shift @paths; # $path will be undef if no path was found (error messages already generated) unless (defined $path) { return $self->UNRELATED; } my $dist = $path->[0]; # JM 1-29-04 # most of the code that does path-finding is now in PathFinder if ($dist > 0) { my $score = 1.0 / $dist; $self->{doCache} and $self->storeToCache ($wps1, $wps2, $score); return $score; } else { $self->{errorString} .= "\nWarning (${class}::getRelatedness()) - "; $self->{errorString} .= "Internal error while finding relatedness."; $self->{error} = ($self->{error} < 1) ? 1 : $self->{error}; return undef; } } # JM # Much of the code that was shared between measures has been relocated. # WordNet::Similarity contains code common to (almost) all measures, # WordNet::Similarity::PathFinder has code common among path finding # measures. 1; __END__ =back =head2 Discussion If the two synsets being compared are the same, then the resulting relatedness score will be 1. For exaple, the score for car#n#1 and car#n#1 is 1. Due to multiple inheritance in the WordNet taxonomies, it is possible for there to be a tie for the shortest path between synsets. If such a tie occurs, then all of the paths that are tied will be printed to the trace string. The relatedness value returned by C is the multiplicative inverse of the path length between the two synsets (1/path_length). This has a slightly subtle effect: it shifts the relative magnitude of scores. For example, if we have the following pairs of synsets with the given path lengths: synset1 synset2: 3 synset3 synset4: 4 synset5 synset6: 5 We observe that the difference in the score for synset1-synset2 and synset3-synset4 is the same as for synset3-synset4 and synset5-synset6. When we take the multiplicative inverse of them, we get: synset1 synset2: .333 synset3 synset4: .25 synset5 synset6: .2 Now the difference between the scores for synset3-synset4 is less than the difference for synset1-synset2 and synset3-synset4. This can have negative consequences when computing correlation coefficients. It might be useful to compute relatedness as S, where max_distance is the longest possible shortest path between two synsets. The original path length can be easily determined by taking the multiplicative inverse of the returned relatedness score: S<1/score = 1/(1/path_length) = path_length>. The computation of max_distance is left as an exercise for the reader. If two different word senses (wps strings) are given as input to getRelatedness, but both word senses belong to the same synset, then 1 is returned (e.g., car#n#1 and auto#n#1 both belong to the same synset). =head2 Usage The semantic relatedness modules in this distribution are built as classes that define the following methods: new() getRelatedness() getError() getTraceString() See the WordNet::Similarity(3) documentation for details of these methods. =head3 Typical Usage Examples To create an object of the path measure, we would have the following lines of code in the Perl program. use WordNet::Similarity::path; $measure = WordNet::Similarity::path->new($wn, '/home/sid/path.conf'); The reference of the initialized object is stored in the scalar variable '$measure'. '$wn' contains a WordNet::QueryData object that should have been created earlier in the program. The second parameter to the 'new' method is the path of the configuration file for the path measure. If the 'new' method is unable to create the object, '$measure' would be undefined. This, as well as any other error/warning may be tested. die "Unable to create object.\n" if(!defined $measure); ($err, $errString) = $measure->getError(); die $errString."\n" if($err); To find the semantic relatedness of the first sense of the noun 'car' and the second sense of the noun 'bus' using the measure, we would write the following piece of code: $relatedness = $measure->getRelatedness('car#n#1', 'bus#n#2'); To get traces for the above computation: print $measure->getTraceString(); However, traces must be enabled using configuration files. By default traces are turned off. =head1 CONFIGURATION FILE The behavior of the measures of semantic relatedness can be controlled by using configuration files. These configuration files specify how certain parameters are initialized within the object. A configuration file may be specified as a parameter during the creation of an object using the new method. The configuration files must follow a fixed format. Every configuration file starts with the name of the module ON THE FIRST LINE of the file. For example, a configuration file for the path module will have on the first line 'WordNet::Similarity::path'. This is followed by the various parameters, each on a new line and having the form 'name::value'. The 'value' of a parameter is optional (in case of boolean parameters). In case 'value' is omitted, we would have just 'name::' on that line. Comments are supported in the configuration file. Anything following a '#' is ignored. The module parses the configuration file and recognizes the following parameters: =over =item trace The value of this parameter specifies the level of tracing that should be employed for generating the traces. This value is an integer equal to 0, 1, or 2. If the value is omitted, then the default value, 0, is used. A value of 0 switches tracing off. A value of 1 or 2 switches tracing on. A trace level of 1 means the synsets are represented as word#pos#sense strings, while for level 2, the synsets are represented as word#pos#offset strings. =item cache The value of this parameter specifies whether or not caching of the relatedness values should be performed. This value is an integer equal to 0 or 1. If the value is omitted, then the default value, 1, is used. A value of 0 switches caching 'off', and a value of 1 switches caching 'on'. =item maxCacheSize The value of this parameter indicates the size of the cache, used for storing the computed relatedness value. The specified value must be a non-negative integer. If the value is omitted, then the default value, 5,000, is used. Setting maxCacheSize to zero has the same effect as setting cache to zero, but setting cache to zero is likely to be more efficient. Caching and tracing at the same time can result in excessive memory usage because the trace strings are also cached. If you intend to perform a large number of relatedness queries, then you might want to turn tracing off. =item rootNode The value of this parameter indicates whether or not a unique root node should be used. In WordNet, there is no unique root node for the noun and verb taxonomies. If this parameter is set to 1 (or if the value is omitted), then certain measures (wup, path, lch, res, lin, and jcn) will "fake" a unique root node. If the value is set to 0, then no unique root node will be used. If the value is omitted, then the default value, 1, is used. =back =head1 SEE ALSO perl(1), WordNet::Similarity(3), WordNet::QueryData(3) http://www.cs.utah.edu/~sidd http://wordnet.princeton.edu http://www.ai.mit.edu/~jrennie/WordNet http://groups.yahoo.com/group/wn-similarity =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Jason Michelizzi, University of Minnesota Duluth mich0212 at d.umn.edu =head1 BUGS None. To report bugs, go to L. =head1 COPYRIGHT AND LICENSE Copyright (c) 2005, Ted Pedersen, Siddharth Patwardhan and Jason Michelizzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/Similarity/vector.pm0000644000175000017500000007444711015336622021423 0ustar sidsid# WordNet::Similarity::vector.pm version 2.04 # (Last updated $Id: vector.pm,v 1.24 2008/03/27 06:21:17 sidz1979 Exp $) # # Module accepts two WordNet synsets and returns a floating point # number that indicates how similar those two synsets are, using a # gloss vector overlap measure based on "context vectors" described by # Schütze (1998). # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh # banerjee+ at cs.cmu.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity::vector; =head1 NAME WordNet::Similarity::vector - Perl module for computing semantic relatedness of word senses using second order co-occurrence vectors of glosses of the word senses. =head1 SYNOPSIS use WordNet::Similarity::vector; use WordNet::QueryData; my $wn = WordNet::QueryData->new(); my $vector = WordNet::Similarity::vector->new($wn); my $value = $vector->getRelatedness("car#n#1", "bus#n#2"); ($error, $errorString) = $vector->getError(); die "$errorString\n" if($error); print "car (sense 1) <-> bus (sense 2) = $value\n"; =head1 DESCRIPTION SchEtze (1998) creates what he calls context vectors (second order co-occurrence vectors) of pieces of text for the purpose of Word Sense Discrimination. This idea is adopted by Patwardhan and Pedersen to represent the word senses by second-order co-occurrence vectors of their dictionary (WordNet) definitions. The relatedness of two senses is then computed as the cosine of their representative gloss vectors. =over =cut use strict; use WordNet::get_wn_info; use WordNet::stem; use WordNet::vectorFile; use WordNet::Similarity; use File::Spec; use vars qw($VERSION @ISA); @ISA = qw(WordNet::Similarity); $VERSION = '2.04'; WordNet::Similarity::addConfigOption("relation", 0, "p", undef); WordNet::Similarity::addConfigOption("vectordb", 0, "p", undef); WordNet::Similarity::addConfigOption("stop", 0, "p", undef); WordNet::Similarity::addConfigOption("stem", 0, "i", 0); WordNet::Similarity::addConfigOption("textsize", 0, "i", "-1"); =item $vector->setPosList() This method is internally called to determine the parts of speech this measure is capable of dealing with. Parameters: none. Returns: none. =cut sub setPosList { my $self = shift; $self->{n} = 1; $self->{v} = 1; $self->{a} = 1; $self->{r} = 1; return 1; } =item $vector->initialize($file) Overrides the initialize method in the parent class (GlossFinder.pm). This method essentially initializes the measure for use. Parameters: $file -- configuration file. Returns: none. =cut # Initialization of the WordNet::Similarity::vector object... parses the config file and sets up # global variables, or sets them to default values. # INPUT PARAMS : $paramFile .. File containing the module specific params. # RETURN VALUES : (none) sub initialize { my $self = shift; my $vectorDB; my $documentCount; my $wn = $self->{wn}; my $gwi; my $readDims; my $readVectors; my %stopHash = (); # Stemming? Compounds? StopWords? $self->{stem} = 0; $self->{stopHash} = {}; # Call the initialize method of the super-class. $self->SUPER::initialize(@_); # Initialize the vector cache. $self->{vCache} = (); $self->{vCacheQ} = (); $self->{vCacheSize} = 80; # Load the stop list. if(defined $self->{stop}) { my $line; my $stopFile = $self->{stop}; if(open(STOP, $stopFile)) { while($line = ) { $line =~ s/[\r\f\n]//g; $line =~ s/^\s+//; $line =~ s/\s+$//; $line =~ s/\s+/_/g; $stopHash{$line} = 1; $self->{stopHash}->{$line} = 1; } close(STOP); } else { $self->{errorString} .= "\nWarning (WordNet::Similarity::vector->initialize()) - "; $self->{errorString} .= "Unable to open $stopFile."; $self->{error} = 1 if($self->{error} < 1); } } # so now we are ready to initialize the get_wn_info package with # the wordnet object, 0/1 depending on if stemming is required and # the stop hash if($self->{stem}) { $gwi = WordNet::get_wn_info->new($wn, 1, %stopHash); $self->{gwi} = $gwi; } else { $gwi = WordNet::get_wn_info->new($wn, 0, %stopHash); $self->{gwi} = $gwi; } # Initialize the word vector database interface... if(!defined $self->{vectordb} || $self->{vectordb} eq "") { my $path; my $header; my @possiblePaths = (); $vectorDB = ""; # Look for all possible default data files installed. foreach $path (@INC) { # JM 1-16-04 -- modified to use File::Spec my $file = File::Spec->catfile($path, 'WordNet', 'wordvectors.dat'); push @possiblePaths, $file if(-e $file); } # If there are multiple possibilities, get the one in the correct format. foreach $path (@possiblePaths) { next if(!open(VECTORS, $path)); $header = ; $header =~ s/\s+//g; if($header =~ /DOCUMENTCOUNT/) { $vectorDB = $path; $self->{vectordb} = $path; close(VECTORS); last; } close(VECTORS); } } else { $vectorDB = $self->{vectordb}; } # Initialize the word vector database interface... if(!defined $vectorDB || $vectorDB eq "") { $self->{errorString} .= "\nError (WordNet::Similarity::vector->initialize()) - "; $self->{errorString} .= "No usable Word Vector database found. Use configuration file."; $self->{error} = 2; return; } # Get the documentCount, dimensions and vectors... ($documentCount, $readDims, $readVectors) = WordNet::vectorFile->readVectors($vectorDB); if(!defined $documentCount || !defined $readDims || !defined $readVectors) { $self->{errorString} .= "\nError (WordNet::Similarity::vector->initialize()) - "; $self->{errorString} .= "Error reading the vector database file."; $self->{error} = 2; return; } # Load the word vector dimensions... my $key; $self->{numberOfDimensions} = scalar(keys(%{$readDims})); foreach $key (keys %{$readDims}) { my $ans = $readDims->{$key}; my @prts = split(/\s+/, $ans); $self->{wordIndex}->{$key} = $prts[0]; $self->{indexWord}->[$prts[0]] = $key; } # Set up the interface to the word vectors... foreach $key (keys %{$readVectors}) { my $vec = $readVectors->{$key}; if(defined $vec) { $self->{table}->{$key} = $vec; } } # If relation file not specified... manually add the relations to # be used... Look for the default vector relation file... if(!defined $self->{relation}) { my $path; my $header; my @possiblePaths = (); # Look for all possible default data files installed. foreach $path (@INC) { # JM 1-16-04 -- modified to use File::Spec my $file = File::Spec->catfile($path, 'WordNet', 'vector-relation.dat'); push @possiblePaths, $file if(-e $file); } # If there are multiple possibilities, get the one in the correct format. foreach $path (@possiblePaths) { next if(!open(RELATIONS, $path)); $header = ; $header =~ s/\s+//g; if($header =~ /VectorRelationFile/) { $self->{relation} = $path; close(RELATIONS); last; } close(RELATIONS); } } if(!(defined $self->{relation})) { $self->{weights}->[0] = 1; $self->{functions}->[0]->[0] = "glosexample"; } else { # Load the relations data my $header; my $relation; my $relationFile = $self->{relation}; if(open(RELATIONS, $relationFile)) { $header = ; $header =~ s/[\r\f\n]//g; $header =~ s/\s+//g; if($header =~ /VectorRelationFile/) { my $index = 0; $self->{functions} = (); $self->{weights} = (); while($relation = ) { $relation =~ s/[\r\f\n]//g; # now for each line in the file, extract the # nested functions if any, check if they are defined, # if it makes sense to nest them, and then finally put # them into the @functions triple dimensioned array! # remove leading/trailing spaces from the relation $relation =~ s/^\s+//; $relation =~ s/\s+$//; # now extract the weight if any. if no weight, assume 1 if($relation =~ /(\S+)\s+(\S+)/) { $relation = $1; $self->{weights}->[$index] = $2; } else { $self->{weights}->[$index] = 1; } # Need to remove strict for this block. { no strict; $relation =~ s/[\s\)]//g; my @functionArray = split(/\(/, $relation); my $j = 0; my $fn = $functionArray[$#functionArray]; if(!($gwi->can($fn))) { $self->{errorString} .= "\nError (WordNet::Similarity::vector->initialize()) - "; $self->{errorString} .= "Undefined function ($functionArray[$#functionArray]) in relations file."; $self->{error} = 2; close(RELATIONS); return; } $self->{functions}->[$index]->[$j++] = $functionArray[$#functionArray]; my $input; my $output; my $dummy; my $k; for ($k = $#functionArray-1; $k >= 0; $k--) { my $fn2 = $functionArray[$k]; my $fn3 = $functionArray[$k+1]; if(!($gwi->can($fn2))) { $self->{errorString} .= "\nError (WordNet::Similarity::vector->initialize()) - "; $self->{errorString} .= "Undefined function ($functionArray[$k]) in relations file."; $self->{error} = 2; close(RELATIONS); return; } ($input, $dummy) = $gwi->$fn2($dummy, 1); ($dummy, $output) = $gwi->$fn3($dummy, 1); if($input != $output) { $self->{errorString} .= "\nError (WordNet::Similarity::vector->initialize()) - "; $self->{errorString} .= "Invalid function combination - $functionArray[$k]($functionArray[$k+1])."; $self->{error} = 2; close(RELATIONS); return; } $self->{functions}->[$index]->[$j++] = $functionArray[$k]; } # if the output of the outermost function is synset array (1) # wrap a glosexample around it my $xfn = $functionArray[0]; ($dummy, $output) = $gwi->$xfn($dummy, 1); if($output == 1) { $self->{functions}->[$index]->[$j++] = "glosexample"; } } $index++; } } else { $self->{errorString} .= "\nError (WordNet::Similarity::vector->initialize()) - "; $self->{errorString} .= "Bad file format ($relationFile)."; $self->{error} = 2; close(RELATIONS); return; } close(RELATIONS); } else { $self->{errorString} .= "\nError (WordNet::Similarity::vector->initialize()) - "; $self->{errorString} .= "Unable to open $relationFile."; $self->{error} = 2; return; } } $self->{textsize} = -1 if(!defined $self->{textsize}); } =item $vector->traceOptions() This method is internally called to determine the extra options specified by this measure (apart from the default options specified in the WordNet::Similarity base class). Parameters: none. Returns: none. =cut # show all config options specific to this module sub traceOptions { my $self = shift; $self->{traceString} .= "relation File :: ".((defined $self->{relation})?"$self->{relation}":"")."\n"; $self->{traceString} .= "vectorDB File :: ".((defined $self->{vectordb})?"$self->{vectordb}":"")."\n"; $self->{traceString} .= "stop File :: ".((defined $self->{stop})?"$self->{stop}":"")."\n"; $self->{traceString} .= "stem :: $self->{stem}\n"; $self->{traceString} .= "textsize :: $self->{textsize}\n"; } =item $vector->getRelatedness Computes the relatedness of two word senses using the Vector Algorithm. Parameters: two word senses in "word#pos#sense" format. Returns: Unless a problem occurs, the return value is the relatedness score, which is greater-than or equal-to 0. If an error occurs, then the error level is set to non-zero and an error string is created (see the description of getError()). =cut sub getRelatedness { my $self = shift; my $wps1 = shift; my $wps2 = shift; my $wn = $self->{wn}; my $wntools = $self->{wntools}; my $gwi = $self->{gwi}; # Check the existence of the WordNet::QueryData object. if(!$wn) { $self->{errorString} .= "\nError (WordNet::Similarity::vector->getRelatedness()) - "; $self->{errorString} .= "A WordNet::QueryData object is required."; $self->{error} = 2; return undef; } # Check the existence of the WordNet::Tools object. if(!$wntools) { $self->{errorString} .= "\nError (WordNet::Similarity::vector->getRelatedness()) - "; $self->{errorString} .= "A WordNet::Tools object is required."; $self->{error} = 2; return undef; } # Initialize traces. $self->{traceString} = "" if($self->{trace}); # Undefined input cannot go unpunished. if(!$wps1 || !$wps2) { $self->{errorString} .= "\nWarning (WordNet::Similarity::vector->getRelatedness()) - Undefined input values."; $self->{error} = 1 if($self->{error} < 1); return undef; } # Security check -- are the input strings in the correct format (word#pos#sense). if($wps1 !~ /^\S+\#([nvar])\#\d+$/) { $self->{errorString} .= "\nWarning (WordNet::Similarity::vector->getRelatedness()) - "; $self->{errorString} .= "Input not in word\#pos\#sense format."; $self->{error} = ($self->{error} < 1) ? 1 : $self->{error}; return undef; } if($wps2 !~ /^\S+\#([nvar])\#\d+$/) { $self->{errorString} .= "\nWarning (WordNet::Similarity::vector->getRelatedness()) - "; $self->{errorString} .= "Input not in word\#pos\#sense format."; $self->{error} = ($self->{error} < 1) ? 1 : $self->{error}; return undef; } # Now check if the similarity value for these two synsets is in # fact in the cache... if so return the cached value. my $relatedness = $self->{doCache} ? $self->fetchFromCache($wps1, $wps2) : undef; defined $relatedness and return $relatedness; # Are the gloss vectors present in the cache... if(defined $self->{vCache}->{$wps1} && defined $self->{vCache}->{$wps2}) { if($self->{trace}) { # ah so we do need SOME traces! put in the synset names. $self->{traceString} .= "Synset 1: $wps1 (Gloss Vector found in Cache)\n"; $self->{traceString} .= "Synset 2: $wps2 (Gloss Vector found in Cache)\n"; } my $a = $self->{vCache}->{$wps1}; my $b = $self->{vCache}->{$wps2}; my $score = &_inner($a, $b); # that does all the scoring. Put in cache if doing cacheing. Then # return the score. $self->{doCache} and $self->storeToCache($wps1, $wps2, $score); return $score; } # we shall put the first synset in a "set" of itself, and the # second synset in another "set" of itself. These sets may # increase in size as the functions are applied (since some # relations have a one to many mapping). # initialize the score my $score = 0; # and now go thru the functions array, get the strings and do the scoring my $i = 0; my %overlaps; my $firstString = ""; my $secondString = ""; while(defined $self->{functions}->[$i]) { my $functionsString = ""; my $funcStringPrinted = 0; my $functionsScore = 0; # see if any traces reqd. if so, create the functions string # however don't send it to the trace string immediately - will # print it only if there are any overlaps for this rel if($self->{trace}) { $functionsString = "Functions: "; my $j = 0; while(defined $self->{functions}->[$i]->[$j]) { $functionsString .= ($self->{functions}->[$i]->[$j])." "; $j++; } } # now get the string for the first set of synsets my %seth1 = (); $seth1{$wps1} = 1; my @arguments = \%seth1; # apply the functions to the arguments, passing the output of # the inner functions to the inputs of the outer ones my $j = 0; no strict; while(defined $self->{functions}->[$i]->[$j]) { my $fn = $self->{functions}->[$i]->[$j]; @arguments = $gwi->$fn(@arguments); $j++; } # finally we should have one cute little string! $firstString .= $arguments[0]; # next do all this for the string for the second set my %seth2 = (); $seth2{$wps2} = 1; @arguments = \%seth2; $j = 0; while(defined $self->{functions}->[$i]->[$j]) { my $fn = $self->{functions}->[$i]->[$j]; @arguments = $gwi->$fn(@arguments); $j++; } $secondString .= $arguments[0]; # check if the two strings need to be reported in the trace. if($self->{trace}) { if(!$funcStringPrinted) { $self->{traceString} .= "$functionsString\n"; $funcStringPrinted = 1; } } $i++; } # Preprocess... $firstString =~ s/\'//g; $firstString =~ s/[^a-z0-9]+/ /g; $firstString =~ s/^\s+//; $firstString =~ s/\s+$//; $firstString = $wntools->compoundify($firstString); $secondString =~ s/\'//g; $secondString =~ s/[^a-z0-9]+/ /g; $secondString =~ s/^\s+//; $secondString =~ s/\s+$//; $secondString = $wntools->compoundify($secondString); # Get vectors... score... my $a; my $maga; my $sizea; my $b; my $magb; my $sizeb; my $trr; # see if any traces reqd. if so, put in the synset arrays. if($self->{trace}) { # ah so we do need SOME traces! put in the synset names. $self->{traceString} .= "Synset 1: $wps1"; } $sizea = 0; if(defined $self->{vCache}->{$wps1}) { $a = $self->{vCache}->{$wps1}; $self->{traceString} .= " (Gloss vector found in cache)\n" if($self->{trace}); } else { ($a, $trr, $maga, $sizea) = $self->_getVector($firstString); $self->{traceString} .= "\nString: \"$firstString\"\n$trr\n" if($self->{trace}); &_norm($a, $maga); $self->{vCache}->{$wps1} = $a; push(@{$self->{vCacheQ}}, $wps1); while(scalar(@{$self->{vCacheQ}}) > $self->{vCacheSize}) { my $wps = shift(@{$self->{vCacheQ}}); delete $self->{vCache}->{$wps} } } if($self->{trace}) { # ah so we do need SOME traces! put in the synset names. $self->{traceString} .= "Synset 2: $wps2"; } $sizeb = 0; if(defined $self->{vCache}->{$wps2}) { $b = $self->{vCache}->{$wps2}; $self->{traceString} .= " (Gloss vector found in cache)\n" if($self->{trace}); } else { ($b, $trr, $magb, $sizeb) = $self->_getVector($secondString); $self->{traceString} .= "\nString: \"$secondString\"\n$trr\n" if($self->{trace}); &_norm($b, $magb); $self->{vCache}->{$wps2} = $b; push(@{$self->{vCacheQ}}, $wps2); while(scalar(@{$self->{vCacheQ}}) > $self->{vCacheSize}) { my $wps = shift(@{$self->{vCacheQ}}); delete $self->{vCache}->{$wps} } } $score = &_inner($a, $b); # that does all the scoring. Put in cache if doing cacheing. Then # return the score. $self->{doCache} and $self->storeToCache($wps1, $wps2, $score); return $score; } # Method to compute a context vector from a given body of text... sub _getVector { my $self = shift; my $text = shift; my $ret = {}; return ($ret, "", 0, 0) if(!defined $text); my @words = split(/\s+/, $text); my $word; my %types; my $fstFlag = 1; my $localTraces = ""; my $kk; my $mag; my $count = 0; # [trace] if($self->{trace}) { $localTraces .= "Word Vectors for: "; } # [/trace] foreach $word (@words) { if($word !~ /[XGES]{3}\d{5}[XGES]{3}/) { $types{$word} = 1; $count++; last if($self->{textsize} >= 0 && $count > $self->{textsize}); } } foreach $word (keys %types) { if(defined $self->{table}->{$word} && !defined $self->{stopHash}->{$word}) { my %pieces = split(/\s+/, $self->{table}->{$word}); # [trace] if($self->{trace}) { $localTraces .= ", " if(!$fstFlag); $localTraces .= "$word"; $fstFlag = 0; } # [/trace] foreach $kk (keys %pieces) { $ret->{$kk} = ((defined $ret->{$kk})?($ret->{$kk}):0) + $pieces{$kk}; } } } $mag = 0; foreach $kk (keys %{$ret}) { $mag += ($ret->{$kk} * $ret->{$kk}); } return ($ret, $localTraces, sqrt($mag), $count); } # Normalizes the sparse vector. sub _norm { my $vec = shift; my $mag = shift; if(defined $vec && defined $mag && $mag != 0) { my $key; foreach $key (keys %{$vec}) { $vec->{$key} /= $mag; } } } # Inner product of two sparse vectors. sub _inner { my $vec1 = shift; my $vec2 = shift; my ($size1, $size2); my $prod = 0; return 0 if(!defined $vec1 || !defined $vec2); $size1 = scalar(keys(%{$vec1})); $size2 = scalar(keys(%{$vec2})); if(defined $size1 && defined $size2 && $size1 < $size2) { my $key; foreach $key (keys %{$vec1}) { $prod += ($vec1->{$key} * $vec2->{$key}) if(defined $vec2->{$key}); } } else { my $key; foreach $key (keys %{$vec2}) { $prod += ($vec1->{$key} * $vec2->{$key}) if(defined $vec1->{$key}); } } return $prod; } 1; __END__ =back =head2 Usage The semantic relatedness modules in this distribution are built as classes that define the following methods: new() getRelatedness() getError() getTraceString() See the WordNet::Similarity(3) documentation for details of these methods. =head3 Typical Usage Examples To create an object of the vector measure, we would have the following lines of code in the Perl program. use WordNet::Similarity::vector; $measure = WordNet::Similarity::vector->new($wn, '/home/sid/vector.conf'); The reference of the initialized object is stored in the scalar variable '$measure'. '$wn' contains a WordNet::QueryData object that should have been created earlier in the program. The second parameter to the 'new' method is the path of the configuration file for the vector measure. If the 'new' method is unable to create the object, '$measure' would be undefined. This, as well as any other error/warning may be tested. die "Unable to create object.\n" if(!defined $measure); ($err, $errString) = $measure->getError(); die $errString."\n" if($err); To find the semantic relatedness of the first sense of the noun 'car' and the second sense of the noun 'bus' using the measure, we would write the following piece of code: $relatedness = $measure->getRelatedness('car#n#1', 'bus#n#2'); To get traces for the above computation: print $measure->getTraceString(); However, traces must be enabled using configuration files. By default traces are turned off. =head1 CONFIGURATION FILE The behavior of the measures of semantic relatedness can be controlled by using configuration files. These configuration files specify how certain parameters are initialized within the object. A configuration file may be specified as a parameter during the creation of an object using the new method. The configuration files must follow a fixed format. Every configuration file starts with the name of the module ON THE FIRST LINE of the file. For example, a configuration file for the vector module will have on the first line 'WordNet::Similarity::vector'. This is followed by the various parameters, each on a new line and having the form 'name::value'. The 'value' of a parameter is optional (in case of boolean parameters). In case 'value' is omitted, we would have just 'name::' on that line. Comments are supported in the configuration file. Anything following a '#' is ignored till the end of the line. The module parses the configuration file and recognizes the following parameters: =over =item trace The value of this parameter specifies the level of tracing that should be employed for generating the traces. This value is an integer equal to 0, 1, or 2. If the value is omitted, then the default value, 0, is used. A value of 0 switches tracing off. A value of 1 or 2 switches tracing on. A value of 1 displays as traces only the gloss overlaps found. A value of 2 displays as traces all the text being compared. =item cache The value of this parameter specifies whether or not caching of the relatedness values should be performed. This value is an integer equal to 0 or 1. If the value is omitted, then the default value, 1, is used. A value of 0 switches caching 'off', and a value of 1 switches caching 'on'. =item maxCacheSize The value of this parameter indicates the size of the cache, used for storing the computed relatedness value. The specified value must be a non-negative integer. If the value is omitted, then the default value, 5,000, is used. Setting maxCacheSize to zero has the same effect as setting cache to zero, but setting cache to zero is likely to be more efficient. Caching and tracing at the same time can result in excessive memory usage because the trace strings are also cached. If you intend to perform a large number of relatedness queries, then you might want to turn tracing off. =item relation The value of this parameter is the path to a file that contains a list of WordNet relations. The path may be either an absolute path or a relative path. The vector module combines the glosses of synsets related to the target synsets by these relations and forms the gloss-vector from this combined gloss. WARNING: the format of the relation file is different for the vector and lesk measures. =item stop The value of this parameter the path of a file containing a list of stop words that should be ignored in the glosses. The path may be either an absolute path or a relative path. =item stem The value of this parameter indicates whether or not stemming should be performed. The value must be an integer equal to 0 or 1. If the value is omitted, then the default value, 0, is used. A value of 1 switches 'on' stemming, and a value of 0 switches stemming 'off'. When stemming is enabled, all the words of the glosses are stemmed before their vectors are created for the vector measure or their overlaps are compared for the lesk measure. =item vectordb The value of this parameter is the path to a file containing word vectors, i.e. co-occurrence vectors for all the words in the WordNet glosses. The value of this parameter may not be omitted, and the vector measure will not run without a vectors file being specified in a configuration file. =back =head1 RELATION FILE FORMAT The relation file starts with the string "VectorRelationFile" on the first line of the file. Following this, on each consecutive line, a relation is specified in the form -- func(func(func... (func)...)) [weight] Where "func" can be any one of the following functions: hype() = Hypernym of hypo() = Hyponym of holo() = Holonym of mero() = Meronym of attr() = Attribute of also() = Also see sim() = Similar enta() = Entails caus() = Causes part() = Particle pert() = Pertainym of glos = gloss (without example) example = example (from the gloss) glosexample = gloss + example syns = the synset of the concept Each of these specifies a WordNet relation. And the outermost function in the nesting can only be one of glos, example, glosexample or syns. The functions specify which glosses to use for forming the gloss vector of the synset. An optional weight can be specified to weigh the contribution of that relation in the overall score. For example, glos(hype(hypo)) 0.5 means that the gloss of the hypernym of the hyponym of the synset is used to form the gloss vector of the synset, and the values in this vector are weighted by 0.5. If one of "glos", "example", "glosexample" or "syns" is not specified as the outermost function in the nesting, then "glosexample" is assumed by default. This implies that glosexample(hypo(also)) and hypo(also) are equivalent as far as the measure is concerned. =head1 SEE ALSO perl(1), WordNet::Similarity(3), WordNet::QueryData(3) http://www.cs.utah.edu/~sidd http://wordnet.princeton.edu http://www.ai.mit.edu/~jrennie/WordNet http://groups.yahoo.com/group/wn-similarity =head1 AUTHORS Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu =head1 BUGS To report bugs, go to http://groups.yahoo.com/group/wn-similarity/ or send an e-mail to "S". =head1 COPYRIGHT AND LICENSE Copyright (c) 2005, Ted Pedersen, Siddharth Patwardhan and Satanjeev Banerjee This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/Similarity/jcn.pm0000644000175000017500000004021511015336622020655 0ustar sidsid# WordNet::Similarity::jcn.pm version 2.04 # (Last updated $Id: jcn.pm,v 1.23 2008/03/27 06:21:17 sidz1979 Exp $) # # Semantic Similarity Measure package implementing the measure # described by Jiang and Conrath (1997). # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # Jason Michelizzi, Univeristy of Minnesota Duluth # mich0212 at d.umn.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity::jcn; =head1 NAME WordNet::Similarity::jcn - Perl module for computing semantic relatedness of word senses according to the method described by Jiang and Conrath (1997). =head1 SYNOPSIS use WordNet::Similarity::jcn; use WordNet::QueryData; my $wn = WordNet::QueryData->new(); my $rel = WordNet::Similarity::jcn->new($wn); my $value = $rel->getRelatedness("car#n#1", "bus#n#2"); ($error, $errorString) = $rel->getError(); die "$errorString\n" if($error); print "car (sense 1) <-> bus (sense 2) = $value\n"; =head1 DESCRIPTION This module computes the semantic relatedness of word senses according to the method described by Jiang and Conrath (1997). This measure is based on a combination of using edge counts in the WordNet 'is-a' hierarchy and using the information content values of the WordNet concepts, as described in the paper by Jiang and Conrath. Their measure, however, computes values that indicate the semantic distance between words (as opposed to their semantic relatedness). In this implementation of the measure we invert the value so as to obtain a measure of semantic relatedness. Other issues that arise due to this inversion (such as handling of zero values in the denominator) have been taken care of as special cases. =over =cut use strict; use warnings; use Exporter; use WordNet::Similarity::ICFinder; our (@ISA, @EXPORT, @EXPORT_OK, %EXPORT_TAGS); @ISA = qw(WordNet::Similarity::ICFinder); %EXPORT_TAGS = (); @EXPORT_OK = (); @EXPORT = (); our $VERSION = '2.04'; # the 'new' method is supplied by WordNet::Similarity =item $jcn->getRelatedness ($synset1, $synset2) Computes the relatedness of two word senses using an information content scheme. See the discussion section below for detailed information on how the jcn measure calculates relatedness. Parameters: two word senses in "word#pos#sense" format. Returns: Unless a problem occurs, the return value is the relatedness score. If no path exists between the two word senses, then a large negative number is returned. If an error occures, then the error level is set to non-zero and an error string is created (see the description of getError()). Note: the error level will also be set to 1 and an an error string will be created if no path exists between the words. =cut sub getRelatedness { my $self = shift; my $wps1 = shift; my $wps2 = shift; my $wn = $self->{wn}; my $class = ref $self || $self; # Check the existence of the WordNet::QueryData object. unless ($wn) { $self->{errorString} .= "\nError (${class}::getRelatedness()) - "; $self->{errorString} .= "A WordNet::QueryData object is required."; $self->{error} = 2; return undef; } # Initialize traces. $self->{traceString} = ""; # JM 1-21-04 # moved input validation code to parseInput() in a super-class my $ret = $self->parseWps ($wps1, $wps2); ref $ret or return $ret; my ($word1, $pos1, undef, $offset1, $word2, $pos2, undef, $offset2) = @{$ret}; my $pos = $pos1; # Now check if the similarity value for these two synsets is in # fact in the cache... if so return the cached value. my $relatedness = $self->{doCache} ? $self->fetchFromCache ($wps1, $wps2) : undef; defined $relatedness and return $relatedness; # Now get down to really finding the relatedness of these two. my $mode = 'offset'; my @LCSs = $self->getLCSbyIC ($offset1, $offset2, $pos, 'offset'); my $ref = shift @LCSs; # check if $ref is a reference, if not, then return undefined # $ref will not be a reference if no LCS was found unless (ref $ref) { return $self->UNRELATED; } my ($lcs, $lcsic) = @{$ref}; my $lcsfreq = $self->getFrequency ($lcs, $pos, 'offset'); # Check for the rare possibility of the root node having 0 # frequency count... # If normal (i.e. freqCount(root) > 0)... Set the minimum distance to the # greatest distance possible + 1... (my replacement for infinity)... # If zero root frequency count... return 0 relatedness, with a warning... my $maxScore; my $rootFreq = $self->getFrequency (0, $pos, 'offset'); if($rootFreq) { # $minDist = (2*(-log(0.001/($self->{offsetFreq}->{$pos}->{0})))) + 1; $maxScore = 2 * -log (0.001 / $rootFreq) + 1; } else { $self->{errorString} .= "\nWarning (${class}::getRelatedness()) - "; $self->{errorString} .= "Root node has a zero frequency count."; $self->{error} = ($self->{error} < 1) ? 1 : $self->{error}; return 0; } # Foreach lowest common subsumer... # Find the minimum jcn distance between the two subsuming concepts... # Making sure that neither of the 2 concepts have 0 infocontent my $ic1 = $self->IC($offset1, $pos); my $ic2 = $self->IC($offset2, $pos); if ($self->{trace}) { $self->{traceString} .= "Concept1: "; $self->printSet ($pos, $mode, $offset1); $self->{traceString} .= " (IC="; $self->{traceString} .= sprintf ("%.6f", $ic1); $self->{traceString} .= ")\n"; $self->{traceString} .= "Concept2: "; $self->printSet ($pos, $mode, $offset2); $self->{traceString} .= " (IC="; $self->{traceString} .= sprintf ("%.6f", $ic2); $self->{traceString} .= ")\n"; } my $distance; # If either of the two concepts have a zero information content... # return 0, for lack of data... if($ic1 && $ic2) { my $ic3 = $self->IC($lcs, $pos); $distance = $ic1 + $ic2 - (2 * $ic3); } else { return 0; } # Now if distance turns out to be 0... # implies ic1 == ic2 == ic3 (most probably all three represent # the same concept)... i.e. maximum relatedness... i.e. infinity... # We'll return the maximum possible value ("Our infinity"). # Here's how we got our infinity... # distance = ic1 + ic2 - (2 x ic3) # Largest possible value for (1/distance) is infinity, when distance = 0. # That won't work for us... Whats the next value on the list... # the smallest value of distance greater than 0... # Consider the formula again... distance = ic1 + ic2 - (2 x ic3) # We want the value of distance when ic1 or ic2 have information content # slightly more than that of the root (ic3)... (let ic2 == ic3 == 0) # Assume frequency counts of 0.01 less than the frequency count of the # root for computing ic1... # sim = 1/ic1 # sim = 1/(-log((freq(root) - 0.01)/freq(root))) my $score; if ($distance == 0) { if ($rootFreq > 0.01) { $score = 1 / -log (($rootFreq - 0.01) / $rootFreq); } else { # root frequency is 0 return 0; } } else { # distance is non-zero $score = 1 / $distance } $self->{doCache} and $self->storeToCache ($wps1, $wps2, $score); return $score; } # JM 1-16-04 # moved subroutine _getLeastCommonSubsumers to Infocontent.pm 1; __END__ =back =head2 Discussion The relatedness value returned by the jcn measure is equal to S<1 / jcn_distance>, where jcn_distance is equal to S. The original metric proposed by Jiang and Conrath was this distance measure. By taking the multiplicative inverse of it, we have converted it to a measure of similarity, but by so doing, we have shifted the distribution of scores. For example, if we have the following pairs of synsets with the given jcn distances: synset1 synset2: 3 synset3 synset4: 4 synset5 synset6: 5 We observe that the difference in the score for synset1-synset2 and synset3-synset4 is the same as for synset3-synset4 and synset5-synset6. When we take the multiplicative inverse of them, we get: synset1 synset2: .333 synset3 synset4: .25 synset5 synset6: .2 Now the difference between the scores for synset3-synset4 is less than the difference for synset1-synset2 and synset3-synset4. This can have negative consequences when computing correlation coefficients. It might be useful to compute relatedness as S, where max_distance is the maximum possible jcn distance between any two synsets. The original jcn distance can easily be determined by taking the inverse of the value returned: S<1/score = 1/1/jcn_distance = jcn_distance>. There are two special cases that need to be handled carefully when computing relatedness; both of these involve the case when jcn_distance is zero. In the first case, we have S. In an ideal world, this would only happen when all three concepts, viz. synset1, synset2, and lcs, are the root node. However, when a synset has a frequency count of zero, we use the value 0 for the information content. In this first case, we return 0 due to lack of data. In the second case, we have S. This is almost always found when S (i.e., the two input synsets are the same). Intuitively this is the case of maximum relatedness, which would be infinity, but it is impossible to return infinity. Insteady we find the smallest possible distance greater than zero and return the multiplicative inverse of that distance. =head2 Usage The semantic relatedness modules in this distribution are built as classes that define the following methods: new() getRelatedness() getError() getTraceString() See the WordNet::Similarity(3) documentation for details of these methods. =head3 Typical Usage Examples To create an object of the jcn measure, we would have the following lines of code in the Perl program. use WordNet::Similarity::jcn; $measure = WordNet::Similarity::jcn->new($wn, '/home/sid/jcn.conf'); The reference of the initialized object is stored in the scalar variable '$measure'. '$wn' contains a WordNet::QueryData object that should have been created earlier in the program. The second parameter to the 'new' method is the path of the configuration file for the jcn measure. If the 'new' method is unable to create the object, '$measure' would be undefined. This, as well as any other error/warning may be tested. die "Unable to create object.\n" if(!defined $measure); ($err, $errString) = $measure->getError(); die $errString."\n" if($err); To find the semantic relatedness of the first sense of the noun 'car' and the second sense of the noun 'bus' using the measure, we would write the following piece of code: $relatedness = $measure->getRelatedness('car#n#1', 'bus#n#2'); To get traces for the above computation: print $measure->getTraceString(); However, traces must be enabled using configuration files. By default traces are turned off. =head1 CONFIGURATION FILE The behavior of the measures of semantic relatedness can be controlled by using configuration files. These configuration files specify how certain parameters are initialized within the object. A configuration file may be specified as a parameter during the creation of an object using the new method. The configuration files must follow a fixed format. Every configuration file starts with the name of the module ON THE FIRST LINE of the file. For example, a configuration file for the jcn module will have on the first line 'WordNet::Similarity::jcn'. This is followed by the various parameters, each on a new line and having the form 'name::value'. The 'value' of a parameter is optional (in case of boolean parameters). In case 'value' is omitted, we would have just 'name::' on that line. Comments are supported in the configuration file. Anything following a '#' is ignored till the end of the line. The module parses the configuration file and recognizes the following parameters: =over =item trace The value of this parameter specifies the level of tracing that should be employed for generating the traces. This value is an integer equal to 0, 1, or 2. If the value is omitted, then the default value, 0, is used. A value of 0 switches tracing off. A value of 1 or 2 switches tracing on. A trace level of 1 means the synsets are represented as word#pos#sense strings, while for level 2, the synsets are represented as word#pos#offset strings. =item cache The value of this parameter specifies whether or not caching of the relatedness values should be performed. This value is an integer equal to 0 or 1. If the value is omitted, then the default value, 1, is used. A value of 0 switches caching 'off', and a value of 1 switches caching 'on'. =item maxCacheSize The value of this parameter indicates the size of the cache, used for storing the computed relatedness value. The specified value must be a non-negative integer. If the value is omitted, then the default value, 5,000, is used. Setting maxCacheSize to zero has the same effect as setting cache to zero, but setting cache to zero is likely to be more efficient. Caching and tracing at the same time can result in excessive memory usage because the trace strings are also cached. If you intend to perform a large number of relatedness queries, then you might want to turn tracing off. =item rootNode The value of this parameter indicates whether or not a unique root node should be used. In WordNet, there is no unique root node for the noun and verb taxonomies. If this parameter is set to 1 (or if the value is omitted), then certain measures (wup, path, lch, res, lin, and jcn) will "fake" a unique root node. If the value is set to 0, then no unique root node will be used. If the value is omitted, then the default value, 1, is used. =item infocontent The value for this parameter should be a string that specifies the path of an information content file containing the frequency of occurrence of every WordNet concept in a large corpus. A number of utility programs are included in this distribution that can be used to generate an infocontent file (see utils.pod). If no path is specified, then the default infocontent file is used, which was generated from SemCor using the sense-tags. =back =head1 SEE ALSO perl(1), WordNet::Similarity(3), WordNet::QueryData(3) http://www.cs.utah.edu/~sidd http://wordnet.princeton.edu http://www.ai.mit.edu/~jrennie/WordNet http://groups.yahoo.com/group/wn-similarity =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah sidd at cs.utah.edu Jason Michelizzi, University of Minnesota Duluth mich0212 at d.umn.edu =head1 COPYRIGHT AND LICENSE Copyright (c) 2005, Ted Pedersen, Siddharth Patwardhan and Jason Michelizzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/Similarity/res.pm0000644000175000017500000002700611015336622020677 0ustar sidsid# WordNet::Similarity::res.pm version 2.04 # (Last updated $Id: res.pm,v 1.21 2008/03/27 06:21:17 sidz1979 Exp $) # # Semantic Similarity Measure package implementing the measure # described by Resnik (1995). # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # Jason Michelizzi, Univeristy of Minnesota Duluth # mich0212 at d.umn.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity::res; =head1 NAME WordNet::Similarity::res - Perl module for computing semantic relatedness of word senses using an information content based measure described by Resnik (1995). =head1 SYNOPSIS use WordNet::Similarity::res; use WordNet::QueryData; my $wn = WordNet::QueryData->new(); my $object = WordNet::Similarity::res->new($wn); my $value = $object->getRelatedness("car#n#1", "bus#n#2"); ($error, $errorString) = $object->getError(); die "$errorString\n" if($error); print "car (sense 1) <-> bus (sense 2) = $value\n"; =head1 DESCRIPTION Resnik (1995) uses the information content of concepts, computed from their frequency of occurrence in a large corpus, to determine the semantic relatedness of word senses. This module implements this measure of semantic relatedness. The following methods are defined: =over =cut use strict; use WordNet::Similarity::ICFinder; our @ISA = qw/WordNet::Similarity::ICFinder/; our $VERSION = '2.04'; # The 'new' method for this class is supplied by WordNet::Similarity =item $res->getRelatedness ($synset1, $synset2) Computes the relatedness of two word senses using an information content scheme. The relatedness is equal to the information content of the least common subsumer of the input synsets. Parameters: two word senses in "word#pos#sense" format. Returns: Unless a problem occurs, the return value is the relatedness score. If no path exists between the two word senses, then a large negative number is returned. If an error occurs, then the error level is set to non-zero and an error string is created (see the description of getError()). Note: the error level will also be set to 1 and an error string will be created if no path exists between the words. =cut sub getRelatedness { my $self = shift; my $wps1 = shift; my $wps2 = shift; my $wn = $self->{wn}; my $class = ref $self || $self; # Check the existence of the WordNet::QueryData object. unless ($wn) { $self->{errorString} .= "\nError (${class}::getRelatedness()) - "; $self->{errorString} .= "A WordNet::QueryData object is required."; $self->{error} = 2; return undef; } # JM 1-21-04 # moved input validation code to parseWps() in a super-class my $ret = $self->parseWps ($wps1, $wps2); ref $ret or return $ret; my ($word1, $pos1, undef, $offset1, $word2, $pos2, undef, $offset2) = @{$ret}; # Initialize traces. $self->{traceString} = ""; my $pos = $pos1; # Now check if the similarity value for these two synsets is in # fact in the cache... if so return the cached value. my $relatedness = $self->{doCache} ? $self->fetchFromCache ($wps1, $wps2) : undef; defined $relatedness and return $relatedness; # Now get down to really finding the relatedness of these two. $self->{traceString} = ""; unless ($offset1 and $offset2) { $self->{errorString} .= "\nWarning (${class}::getRelatedness()) - "; $self->{errorString} .= "Input senses not found in WordNet."; $self->{error} = ($self->{'error'} < 1) ? 1 : $self->{'error'}; return undef; } my @LCSs = $self->getLCSbyIC ($offset1, $offset2, $pos1, "offset"); my $ref = shift @LCSs; unless (defined $ref) { return $self->UNRELATED; } my ($lcs, $ic) = @{$ref}; my $score = $ic; $self->{doCache} and $self->storeToCache ($wps1, $wps2, $score); return $score; } # JM 1-16-04 # moved subroutine _getLeastCommonSubsumers to ICFinder.pm 1; __END__ =back =head2 Discussion The relatedness value returned by the res measure is equal to the information content of the Least Common Subsumer (LCS) of the two input synsets. This means that the value will be greater-than or equal-to zero. The upper bound on the value is generally quite large and varies depending upon the information content file being used. To be precise, the upper bound is S, where N is the sum of the frequencies of all the synsets in the information content files. The Resnick measure is sometimes considered a "coarse" measure. Since the relatedness of two synsets depends only upon the information content of their LCS, all pairs of synsets that have the same LCS will have exactly the same relatedness. For example, the pairs dog#n#1-monkey#n#1 and canine#n#1-primate#n#2. =head2 Usage The semantic relatedness modules in this distribution are built as classes that define the following methods: new() getRelatedness() getError() getTraceString() See the WordNet::Similarity(3) documentation for details of these methods. =head3 Typical Usage Examples To create an object of the res measure, we would have the following lines of code in the Perl program. use WordNet::Similarity::res; $measure = WordNet::Similarity::res->new($wn, '/home/sid/res.conf'); The reference of the initialized object is stored in the scalar variable '$measure'. '$wn' contains a WordNet::QueryData object that should have been created earlier in the program. The second parameter to the 'new' method is the path of the configuration file for the res measure. If the 'new' method is unable to create the object, '$measure' would be undefined. This, as well as any other error/warning may be tested. die "Unable to create object.\n" if(!defined $measure); ($err, $errString) = $measure->getError(); die $errString."\n" if($err); To find the semantic relatedness of the first sense of the noun 'car' and the second sense of the noun 'bus' using the measure, we would write the following piece of code: $relatedness = $measure->getRelatedness('car#n#1', 'bus#n#2'); To get traces for the above computation: print $measure->getTraceString(); However, traces must be enabled using configuration files. By default traces are turned off. =head1 CONFIGURATION FILE The behavior of the measures of semantic relatedness can be controlled by using configuration files. These configuration files specify how certain parameters are initialized within the object. A configuration file may be specified as a parameter during the creation of an object using the new method. The configuration files must follow a fixed format. Every configuration file starts with the name of the module ON THE FIRST LINE of the file. For example, a configuration file for the res module will have on the first line 'WordNet::Similarity::res'. This is followed by the various parameters, each on a new line and having the form 'name::value'. The 'value' of a parameter is optional (in case of boolean parameters). In case 'value' is omitted, we would have just 'name::' on that line. Comments are supported in the configuration file. Anything following a '#' is ignored till the end of the line. The module parses the configuration file and recognizes the following parameters: =over =item trace The value of this parameter specifies the level of tracing that should be employed for generating the traces. This value is an integer equal to 0, 1, or 2. If the value is omitted, then the default value, 0, is used. A value of 0 switches tracing off. A value of 1 or 2 switches tracing on. A trace level of 1 means the synsets are represented as word#pos#sense strings, while for level 2, the synsets are represented as word#pos#offset strings. =item cache The value of this parameter specifies whether or not caching of the relatedness values should be performed. This value is an integer equal to 0 or 1. If the value is omitted, then the default value, 1, is used. A value of 0 switches caching 'off', and a value of 1 switches caching 'on'. =item maxCacheSize The value of this parameter indicates the size of the cache, used for storing the computed relatedness value. The specified value must be a non-negative integer. If the value is omitted, then the default value, 5,000, is used. Setting maxCacheSize to zero has the same effect as setting cache to zero, but setting cache to zero is likely to be more efficient. Caching and tracing at the same time can result in excessive memory usage because the trace strings are also cached. If you intend to perform a large number of relatedness queries, then you might want to turn tracing off. =item rootNode The value of this parameter indicates whether or not a unique root node should be used. In WordNet, there is no unique root node for the noun and verb taxonomies. If this parameter is set to 1 (or if the value is omitted), then certain measures (wup, path, lch, res, lin, and jcn) will "fake" a unique root node. If the value is set to 0, then no unique root node will be used. If the value is omitted, then the default value, 1, is used. =item infocontent The value for this parameter should be a string that specifies the path of an information content file containing the frequency of occurrence of every WordNet concept in a large corpus. A number of utility programs are included in this distribution that can be used to generate an infocontent file (see utils.pod). If no path is specified, then the default infocontent file is used, which was generated from SemCor using the sense-tags. =back =head1 SEE ALSO perl(1), WordNet::Similarity(3), WordNet::QueryData(3) http://www.cs.utah.edu/~sidd http://wordnet.princeton.edu http://www.ai.mit.edu/~jrennie/WordNet http://groups.yahoo.com/group/wn-similarity =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Jason Michelizzi, University of Minnesota Duluth mich0212 at d.umn.edu =head1 COPYRIGHT AND LICENSE Copyright (c) 2005, Ted Pedersen, Siddharth Patwardhan and Jason Michelizzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/Similarity/DepthFinder.pm0000644000175000017500000003411711015336622022303 0ustar sidsid# WordNet::Similarity::DepthFinder version 2.04 # (Last updated $Id: DepthFinder.pm,v 1.20 2008/03/27 06:21:17 sidz1979 Exp $) # # Module containing code to find the depths of (noun and verb) synsets in # the WordNet 'is-a' taxonomies # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Jason Michelizzi, Univeristy of Minnesota Duluth # mich0212 at d.umn.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity::DepthFinder; =head1 NAME WordNet::Similarity::DepthFinder - methods to find the depth of synsets in WordNet taxonomies =head1 SYNOPSIS use WordNet::QueryData; my $wn = WordNet::QueryData->new; defined $wn or die "Construction of WordNet::QueryData failed"; use WordNet::Similarity::DepthFinder; my $obj = WordNet::Similarity::DepthFinder->new ($wn); my ($err, $errString) = $obj->getError (); $err and die $errString; my $wps1 = 'car#n#4'; my $wps2 = 'oil#n#1'; my $offset1 = $wn -> offset ($wps1); my $offset2= $wn -> offset ($wps2); my @roots = $obj->getTaxonomies ($offset1, 'n'); my $taxonomy_depth = $obj->getTaxonomyDepth ($roots[0], 'n'); print "The maximum depth of the taxonomy where $wps1 is found is $taxonomy_depth\n"; my @depths = $obj->getSynsetDepth ($offset1, 'n'); print "The depth of $offset1 is $depths[0]->[0]\n"; my @lcsbyic = $obj -> getLCSbyDepth($wps1,$wps2,'n','wps'); print "$wps1 and $wps2 have LCS $lcsbyic[0]->[0] with Depth $lcsbyic[0]->[1]\n"; my @lcsbyic = $obj -> getLCSbyDepth($offset1,$offset2,'n','offset'); print "$offset1 and $offset2 have LCS $lcsbyic[0]->[0] with Depth $lcsbyic[0]->[1]\n"; =head1 DESCRIPTION The following methods are provided by this module: =over =cut use strict; use warnings; use WordNet::Similarity::PathFinder; our @ISA = qw/WordNet::Similarity::PathFinder/; our $VERSION = '2.04'; WordNet::Similarity::addConfigOption ("taxonomyDepthsFile", 1, "p", undef); WordNet::Similarity::addConfigOption ("synsetDepthsFile", 1, "p", undef); =item $obj->initialize ($configfile) Overrides the initialize method in WordNet::Similarity to look for and process depths files. The initialize method of the superclass is also called. =cut sub initialize { my $self = shift; my $class = ref $self || $self; my $wn = $self->{wn}; my $defaultdepths = "synsetdepths.dat"; my $defaultroots = "treedepths.dat"; $self->SUPER::initialize (@_); my $depthsfile = $self->{synsetDepthsFile}; unless (defined $depthsfile) { DEPTHS_SEARCH: foreach (@INC) { my $file = File::Spec->catfile ($_, 'WordNet', $defaultdepths); if (-e $file) { if (-r $file) { $depthsfile = $file; last DEPTHS_SEARCH; } else { # The file not readable--is this an error? # I suppose we shouldn't punish people for having # unreadable files lying around; let's do nothing. } } } } unless (defined $depthsfile) { $self->{error} = 2; $self->{errorString} .= "\nError (${class}::initialize()) - "; $self->{errorString} .= "No depths file found."; return undef; } $self->_processSynsetsFile ($depthsfile) or return undef; my $rootsfile = $self->{treeDepthsFile}; unless (defined $rootsfile) { TAXONOMY_SEARCH: foreach (@INC) { my $file = File::Spec->catfile ($_, 'WordNet', $defaultroots); if (-e $file) { if (-r $file) { $rootsfile = $file; last TAXONOMY_SEARCH; } else { # The file not readable--is this an error? # I suppose we shouldn't punish people for having # unreadable files lying around; let's do nothing. } } } } $self->_processTaxonomyFile ($rootsfile) or return undef; return 1; } =item $obj->getSynsetDepth ($offset, $pos) Returns the depth(s) of the synset denoted by $offset and $pos. The return value is a list of references to arrays. Each array has the form S<(depth, root)>. =cut sub getSynsetDepth { my $self = shift; my $class = ref $self || $self; my $offset = shift; my $pos = shift; my $ref = $self->{depths}->{$pos}->{$offset}; my @depths = @$ref; unless (defined $depths[0]) { $self->{errorString} .= "\nWarning (${class}::getSynsetDepth()) - "; $self->{errorString} .= "No depth found for '$offset#$pos'."; $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; return undef; } return @depths; } =item $obj->getTaxonomyDepth ($offset, $pos) Returns the maximum depth of the taxonomy rooted at the synset identified by $offset and $pos. If $offset and $pos does not identify a root of a taxonomy, then undef is returned and an error is raised. =cut sub getTaxonomyDepth { my $self = shift; my $class = ref $self || $self; my $synset = shift; my $pos = shift; my $depth = $self->{taxonomyDepths}->{$pos}->{$synset}; unless (defined $depth) { $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::getTaxonomyDepth()) - "; $self->{errorString} .= "No taxonomy is rooted at $synset#$pos."; return undef; } return $depth; } =item $obj->getTaxonomies ($offset, $pos) Returns a list of the roots of the taxonomies to which the synset identified by $offset and $pos belongs. =cut sub getTaxonomies { my $self = shift; my $offset = shift; my $pos = shift; my $class = ref $self || $self; my $ref = $self->{depths}->{$pos}->{$offset}; my @tmp = @$ref; my %tmp; foreach (@tmp) { $tmp{$_->[1]} = 1; } my @rtn = keys %tmp; unless (defined $rtn[0]) { $self->{errorString} .= "\nWarning (${class}::getTaxonomies()) - "; $self->{errorString} .= "No root information for $offset#$pos."; $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; return undef; } return @rtn; } =item getLCSbyDepth($synset1, $synset2, $pos, $mode) Given two input synsets, finds the least common subsumer (LCS) of them. If there are multiple candidates for the LCS (due to multiple inheritance in WordNet), the LCS with the greatest depth is chosen (i.e., the candidate whose shortest path to the root is the longest). Parameters: a blessed reference, two synsets, a part of speech, and a mode. The mode must the either the string 'wps' or 'offset'. If the mode is wps, then the two input synsets must be in word#pos#sense format. If the mode is offset, then the input synsets must be WordNet offsets. Returns: a list of the form ($lcs, $depth) where $lcs is the LCS (in wps format if mode is 'wps' or an offset if mode is 'offset'. $depth is the depth of the LCS in its taxonomy. Returns undef on error. =cut sub getLCSbyDepth { my $self = shift; my $synset1 = shift; my $synset2 = shift; my $pos = shift; my $mode = shift; my $class = ref $self || $self; my @paths = $self->getAllPaths ($synset1, $synset2, $pos, $mode); unless (defined $paths[0]) { # no paths found $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::getLCSbyDepth()) - "; $self->{errorString} .= "No path between synsets found."; return $self->UNRELATED; } my $wn = $self->{wn}; my %depth; # a hash to hold the depth of each LCS candidate # find the depth of each LCS candidate foreach (@paths) { my $offset = $_->[0]; if ($mode eq 'wps') { if (index ($_->[0], "*Root*") >= $[) { $offset = 0; } else { $offset = $wn->offset ($_->[0]); } } my @depths = $self->getSynsetDepth ($offset, $pos); my ($depth, $root) = @{$depths[0]}; unless (defined $depth) { # serious internal error -- possible problem with depths file? $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::getLCSbyDepth()) - "; $self->{errorString} .= "Undefined depth for $_->[0]. "; $self->{errorString} .= "Possible problem with the depths file?"; return undef; } $depth{$_->[0]} = [$depth, $root]; } # sort according to depth (descending order) my @tmp = sort {$b->[1] <=> $a->[1]} map [$_, @{$depth{$_}}], keys %depth; # remove from the array all the subsumers that are not tied for best foreach (0..$#tmp) { if ($tmp[$_]->[1] == $tmp[0]->[1]) { # do nothing } else { # kill the rest of the array and exit the loop $#tmp = $_ - 1; last; } } unless (defined $tmp[0]) { my $wps1 = $synset1; my $wps2 = $synset2; if ($mode eq 'offset') { $wps1 = $synset1 ? $wn->getSense ($synset1, $pos) : "*Root*#$pos#1"; $wps2 = $synset2 ? $wn->getSynse ($synset2, $pos) : "*Root*#$pos#1"; } $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::getLCSbyDepth() - "; $self->{errorString} .= "No LCS found for $wps1 and $wps2."; if ($self->{trace}) { $self->{traceString} .= "\nNo LCS found for "; $self->printSet ($pos, 'wps', $wps1); $self->{traceString} .= ", "; $self->printSet ($pos, 'wps', $wps2); $self->{traceString} .= "."; } return undef; } if ($self->{trace}) { $self->{traceString} .= "\nLowest Common Subsumers: "; foreach (@tmp) { $self->printSet ($pos, $mode, $_->[0]); $self->{traceString} .= " (Depth=$_->[1]) "; } } return @tmp; } =item $obj->_processSynsetsFile ($filename) Reads and processes a synsets file as output by wnDepths.pl =cut sub _processSynsetsFile { my $self = shift; my $file = shift; my $class = ref $self || $self; my $wnver = $self->{wntools}->hashCode (); unless (open FH, '<', $file) { $self->{error} = 2; $self->{errorString} .= "\nError (${class}::_processSynsetsFile()) - "; $self->{errorString} .= "Cannot open $file for reading: $!."; return 0; } my $line = ; unless ($line =~ /^wnver::(\S+)$/) { $self->{errorString} .= "\nError (${class}::_processSynsetsFile()) - "; $self->{errorString} .= "File $file has bad format."; $self->{error} = 2; return 0; } unless ($1 eq $wnver) { $self->{errorString} .= "\nError (${class}::_processSynsetsFile()) - "; $self->{errorString} .= "Bad WordNet hash-code in $file, $1, should be $wnver."; $self->{error} = 2; return 0; } # If we are using a root node, then we need to slightly adjust all # the synset depths. Thus, the correction will be 1 if the root node # is on and 0 otherwise. my $correction = $self->{rootNode} ? 1 : 0; while ($line = ) { my ($pos, $offset, @depths) = split /\s+/, $line; # convert the offset string to a number. When we make the number # into a string again, there won't be any leading zeros. $offset = 0 + $offset; # We assume the the first depth listed is the smallest. # The wnDepths.pl program should guarantee this behavior. my @refs; foreach (@depths) { my ($depth, $root) = split /:/; # make root a number; see above for why. If the root node # is on, then all roots are the root node, so adjust for that. $root = $self->{rootNode} ? 0 : $root + 0; $depth += $correction; push @refs, [$depth, $root]; } $self->{depths}->{$pos}->{$offset} = [@refs]; } if ($self->{rootNode}) { # set the depth of the root nodes to be one $self->{depths}->{n}->{0} = [[1, 0]]; $self->{depths}->{v}->{0} = [[1, 0]]; } return 1; } =item $obj->_processTaxonomyFile ($filename) Reads and processes a taxonomies file as produced by wnDepths.pl =cut sub _processTaxonomyFile { my $self = shift; my $filename = shift; my $class = ref $self || $self; unless (open FH, '<', $filename) { $self->{errorString} .= "Error (${class}::_processTaxonomyFile()) - "; $self->{errorString} .= "Could not open '$filename' for reading: $!."; $self->{error} = 2; return 0; } my $line = ; unless ($line =~ /^wnver::(\S+)$/) { $self->{errorString} .= "Error (${class}::_processTaxonomyFile()) - "; $self->{errorString} .= "Bad file format for $filename."; $self->{error} = 2; return 0; } while ($line = ) { my ($p, $o, $d) = split /\s+/, $line; # add 0 to offset to make it a number; see above for why $o = $o + 0; $self->{taxonomyDepths}->{$p}->{$o} = $d; } close FH; return 1; } 1; __END__ =back =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Jason Michelizzi, University of Minnesota Duluth mich0212 at d.umn.edu =head1 BUGS None. To report bugs, e-mail tpederse at d.umn.edu or go to http://groups.yahoo.com/group/wn-similarity/. =head1 SEE ALSO WordNet::Similarity(3) WordNet::Similarity::wup(3) WordNet::Similarity::lch(3) =head1 COPYRIGHT Copyright (c) 2005, Ted Pedersen and Jason Michelizzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/Similarity/wup.pm0000644000175000017500000002650011015336622020717 0ustar sidsid# WordNet::Similarity::wup.pm version 2.04 # (Last updated $Id: wup.pm,v 1.27 2008/03/27 06:21:17 sidz1979 Exp $) # # Semantic Similarity Measure package implementing the semantic # relatedness measure described by Wu & Palmer (1994) as revised # by Resnik (1999). # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Jason Michelizzi, Univeristy of Minnesota Duluth # mich0212 at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity::wup; =head1 NAME WordNet::Similarity::wup - Perl module for computing semantic relatedness of word senses using the edge counting method of the of Wu & Palmer (1994) =head1 SYNOPSIS use WordNet::Similarity::wup; use WordNet::QueryData; my $wn = WordNet::QueryData->new(); my $wup = WordNet::Similarity::wup->new($wn); my $value = $wup->getRelatedness('dog#n#1', 'cat#n#1'); my ($error, $errorString) = $wup->getError(); die $errorString if $error; print "dog (sense 1) <-> cat (sense 1) = $value\n"; =head1 DESCRIPTION Resnik (1999) revises the Wu & Palmer (1994) method of measuring semantic relatedness. Resnik uses use an edge distance method by taking into account the most specific node subsuming the two concepts. Here we have implemented the original Wu & Palmer method, which uses node-counting. =head2 Methods This module defines the following methods: =over =cut use strict; use warnings; use WordNet::Similarity::DepthFinder; our @ISA = qw/WordNet::Similarity::DepthFinder/; our $VERSION = '2.04'; =item $wup->getRelatedness ($synset1, $synset2) Computes the relatedness of two word senses using a node counting scheme. For details on how relatedness is computed, see the discussion section below. Parameters: two word senses in "word#pos#sense" format. Returns: Unless a problem occurs, the return value is the relatedness score. If no path exists between the two word senses, then a large negative number is returned. If an error occurs, then the error level is set to non-zero and an error string is created (see the description of getError()). Note: the error level will also be set to 1 and an error string will be created if no path exists between the words. =cut sub getRelatedness { my $self = shift; my $in1 = shift; my $in2 = shift; my $class = ref $self || $self; # initialize trace string $self->{traceString} = ""; # JM 1-21-04 # moved input validation code to WordNet::Similarity::parseWps() my $ret = $self->parseWps ($in1, $in2); ref $ret or return $ret; my ($word1, $pos1, $sense1, $offset1, $word2, $pos2, $sense2, $offset2) = @{$ret}; defined $word1 or return undef; my $wps1 = "$word1#$pos1#$sense1"; my $wps2 = "$word2#$pos2#$sense2"; my $score = $self->fetchFromCache ($wps1, $wps2); return $score if defined $score; my @LCSs = $self->getLCSbyDepth ($wps1, $wps2, $pos1, 'wps'); my $temp = shift @LCSs; unless (ref $temp) { return $temp; } my ($lcs, $depth, $root) = @{$temp}; unless (defined $lcs) { # no lcs found, return unrelated (errors already generated) return $self->UNRELATED; } # now find the depth of $wps1 and $wps2 my $trace = $self->{trace}; $self->{trace} = 0; my @paths1 = $self->getShortestPath ($wps1, $lcs, $pos1, 'wps'); my @paths2 = $self->getShortestPath ($wps2, $lcs, $pos1, 'wps'); $self->{trace} = $trace; my ($length1, undef) = @{shift @paths1}; my ($length2, undef) = @{shift @paths2}; if (ref $length1) { die "Length 1 is a ref\n"; } # If we've already found an lcs, then there must be a path, so this # error should never occur, but there's little harm in checking anyways unless (defined $length1) { $self->{errorString} .= "Length 1 is undefined."; $self->{error} = 1; return undef; } unless (defined $length2) { $self->{errorString} .= "Length 2 is undefined."; $self->{error} = 1; return undef; } my $depth1 = $depth + $length1 - 1; my $depth2 = $depth + $length2 - 1; $score = 2 * $depth / ($depth1 + $depth2); $self->storeToCache ($wps1, $wps2, $score) if $self->{doCache}; if ($self->{trace}) { $self->{traceString} .= "\nDepth("; $self->printSet ($pos1, 'wps', $in1); $self->{traceString} .= ") = $depth1\nDepth("; $self->printSet ($pos1, 'wps', $in2); $self->{traceString} .= ") = $depth2\n"; } return $score; } ### # JM 1-16-04 # All of the code that used to follow here has been replaced by code in # PathFinder. ### 1; __END__ =back =head2 Discussion The Wu & Palmer measure calculates relatedness by considering the depths of the two synsets in the WordNet taxonomies, along with the depth of the LCS. The formula is S. This means that S<0 < score <= 1>. The score can never be zero because the depth of the LCS is never zero (the depth of the root of a taxonomy is one). The score is one if the two input synsets are the same. =head1 USAGE The semantic relatedness modules in this distribution are built as classes that define the following methods: new() getRelatedness() getError() getTraceString() See the WordNet::Similarity(3) documentation for details of these methods. =head1 TYPICAL USAGE EXAMPLES use WordNet::Similarity::wup; my $measure->new($wn, 'wup.conf'); '$wn' contains a WordNet::QueryData object that should have been constructed already. The second (and optional) parameter to the 'new' method is the path of a configuration file for the Wu-Palmer measure. If the 'new' method is unable to construct the object, then '$measure' will be undefined. This may be tested. my ($error, $errorString) = $measure->getError (); die $errorString."\n" if $err; To find the sematic relatedness of the first sense of the noun 'car' and the second sense of the noun 'bus' using the measure, we would write the following piece of code: $relatedness = $measure->getRelatedness('car#n#1', 'bus#n#2'); To get traces for the above computation: print $measure->getTraceString(); However, traces must be enabled using configuration files. By default traces are turned off. =head1 CONFIGURATION FILE The behavior of the measures of semantic relatedness can be controlled by using configuration files. These configuration files specify how certain parameters are initialized with the object. A configuration file may be specified as a parameter during the creation of an object using the new method. The configuration files must follow a fixed format. Every configuration file starts with the name of the module ON THE FIRST LINE of the file. For example, a configuration file for the wup module will have on the first line 'WordNet::Similarity::wup'. This is followed by the various parameters, each on a new line and having the form 'name::value'. The 'value' of a parameter is option (in the case of boolean parameters). In case 'value' is omitted, we would have just 'name::' on that line. Comments are allowed in the configuration file. Anything following a '#' is ignored till the end of the line. The module parses the configuration file and recognizes the following parameters: =over =item trace The value of this parameter specifies the level of tracing that should be employed for generating the traces. This value is an integer equal to 0, 1, or 2. If the value is omitted, then the default value, 0, is used. A value of 0 switches tracing off. A value of 1 or 2 switches tracing on. A trace of level 1 means the synsets are represented as word#pos#sense strings, while for level 2, the synsets are represented as word#pos#offset strings. =item cache The value of this parameter specifies whether or not caching of the relatedness values should be performed. This value is an integer equal to 0 or 1. If the value is omitted, then the default value, 1, is used. A value of 0 switches caching 'off', and a value of 1 switches caching 'on'. =item maxCacheSize The value of this parameter indicates the size of the cache, used for storing the computed relatedness value. The specified value must be a non-negative integer. If the value is omitted, then the default value, 5,000, is used. Setting maxCacheSize to zero has the same effect as setting cache to zero, but setting cache to zero is likely to be more efficient. Caching and tracing at the same time can result in excessive memory usage because the trace strings are also cached. If you intend to perform a large number of relatedness queries, then you might want to turn tracing off. =item rootNode The value of this parameter indicates whether or not a unique root node should be used. In WordNet, there is no unique root node for the noun and verb taxonomies. If this parameter is set to 1 (or if the value is omitted), then certain measures (wup, path, lch, res, lin, and jcn) will "fake" a unique root node. If the value is set to 0, then no unique root node will be used. If the value is omitted, then the default value, 1, is used. =item synsetDepthsFile The value for this parameter should be a string that specifies the location of a synset depths file (as generated by wnDepths.pl. If no path is specified, then the default file is used, which was generated when the Similarity package was installed. =back =head1 SEE ALSO perl(1), WordNet::Similarity(3), WordNet::QueryData(3) http://www.d.umn.edu/~mich0212/ http://www.d.umn.edu/~tpederse/similarity.html http://wordnet.princeton.edu http://www.ai.mit.edu/people/jrennie/WordNet/ =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Jason Michelizzi, University of Minnesota Duluth mich0212 at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 COPYRIGHT AND LICENSE Copyright (c) 2005, Ted Pedersen, Jason Michelizzi and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/Similarity/vector_pairs.pm0000644000175000017500000005454111015336622022612 0ustar sidsid# WordNet::Similarity::vector_pairs.pm version 2.04 # (Last updated $Id: vector_pairs.pm,v 1.11 2008/03/27 06:21:17 sidz1979 Exp $) # # Module to accept two WordNet synsets and to return a floating point # number that indicates how similar those two synsets are, using a # gloss vector overlap measure based on "context vectors" described by # Schütze (1998). # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh # banerjee+ at cs.cmu.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity::vector_pairs; =head1 NAME WordNet::Similarity::vector_pairs - module for computing semantic relatedness of word senses using second order co-occurrence vectors of glosses of the word senses. =head1 SYNOPSIS use WordNet::Similarity::vector_pairs; use WordNet::QueryData; my $wn = WordNet::QueryData->new(); my $vector_pairs = WordNet::Similarity::vector_pairs->new($wn); my $value = $vector_pairs->getRelatedness("car#n#1", "bus#n#2"); ($error, $errorString) = $vector_pairs->getError(); die "$errorString\n" if($error); print "car (sense 1) <-> bus (sense 2) = $value\n"; =head1 DESCRIPTION SchEtze (1998) creates what he calls context vectors (second order co-occurrence vectors) of pieces of text for the purpose of Word Sense Discrimination. This idea is adopted by Patwardhan and Pedersen to represent the word senses by second-order co-occurrence vectors of their dictionary (WordNet) definitions. The relatedness of two senses is then computed as the cosine of their representative gloss vectors. A concept is represented by its own gloss, as well as the glosses of the neighboring senses as specified in the vector-relation.dat file. Each gloss is converted into a second order vector by replacing the words in the gloss with co-occurrence vectors for those words. The overall measure of relatedness between two concepts is determined by taking the pairwise cosines between these expanded glosses. If vector-relation.dat consists of: example-example glos-glos hypo-hypo then three pairwise cosine measurements are made to determine the relatedness of concepts A and B. The examples found in the glosses of A and B are expanded and measured, then the glosses themselves are expanded and measured, and then the hyponyms of A and B are expanded and measured. Then, the values of these three pairwise measures are summed to create the overall relatedness score. =over =cut use strict; use WordNet::vectorFile; use WordNet::Similarity::GlossFinder; use File::Spec; use vars qw($VERSION @ISA); @ISA = qw(WordNet::Similarity::GlossFinder); $VERSION = '2.04'; WordNet::Similarity::addConfigOption("vectordb", 0, "p", undef); =item $measure->initialize($file) Overrides the initialize method in the parent class (GlossFinder.pm). This method essentially initializes the measure for use. Parameters: $file -- configuration file. Returns: none. =cut # Initialization of the WordNet::Similarity::vector_pairs object... parses the config file and sets up # global variables, or sets them to default values. # INPUT PARAMS : $paramFile .. File containing the module specific params. # RETURN VALUES : (none) sub initialize { my $self = shift; my $vectorDB; my $documentCount; my $wn = $self->{wn}; my $readDims; my $readVectors; # Look for the default vector relation file... if(!defined $self->{relationDefault}) { my $path; my $header; my @possiblePaths = (); # Look for all possible default data files installed. foreach $path (@INC) { # JM 1-16-04 -- modified to use File::Spec my $file = File::Spec->catfile($path, 'WordNet', 'vector-pairs-relation.dat'); push @possiblePaths, $file if(-e $file); } # If there are multiple possibilities, get the one in the correct format. foreach $path (@possiblePaths) { next if(!open(RELATIONS, $path)); $header = ; $header =~ s/\s+//g; if($header =~ /RelationFile/) { $self->{relationDefault} = $path; close(RELATIONS); last; } close(RELATIONS); } } # Call the initialize method of the super-class. $self->SUPER::initialize(@_); # Initialize the vector cache. $self->{vCache} = (); $self->{vCacheQ} = (); $self->{vCacheSize} = 80; # Initialize the word vector database interface... if(!defined $self->{vectordb} || $self->{vectordb} eq "") { my $path; my $header; my @possiblePaths = (); $vectorDB = ""; # Look for all possible default data files installed. foreach $path (@INC) { # JM 1-16-04 -- modified to use File::Spec my $file = File::Spec->catfile($path, 'WordNet', 'wordvectors.dat'); push @possiblePaths, $file if(-e $file); } # If there are multiple possibilities, get the one in the correct format. foreach $path (@possiblePaths) { next if(!open(VECTORS, $path)); $header = ; $header =~ s/\s+//g; if($header =~ /DOCUMENTCOUNT/) { $vectorDB = $path; close(VECTORS); last; } close(VECTORS); } } else { $vectorDB = $self->{vectordb}; } # If database still not specified... if(!defined $vectorDB || $vectorDB eq "") { $self->{errorString} .= "\nError (WordNet::Similarity::vector_pairs->initialize()) - "; $self->{errorString} .= "Word Vector database file not specified. Use configuration file."; $self->{error} = 2; return; } # Get the documentCount, dimensions and vectors... ($documentCount, $readDims, $readVectors) = WordNet::vectorFile->readVectors($vectorDB); if(!defined $documentCount || !defined $readDims || !defined $readVectors) { $self->{errorString} .= "\nError (WordNet::Similarity::vector_pairs->initialize()) - "; $self->{errorString} .= "Error reading the vector database file."; $self->{error} = 2; return; } # Load the word vector dimensions... my $key; $self->{numberOfDimensions} = scalar(keys(%{$readDims})); foreach $key (keys %{$readDims}) { my $ans = $readDims->{$key}; my @prts = split(/\s+/, $ans); $self->{wordIndex}->{$key} = $prts[0]; $self->{indexWord}->[$prts[0]] = $key; } # Set up the interface to the word vectors... foreach $key (keys %{$readVectors}) { my $vec = $readVectors->{$key}; if(defined $vec) { $self->{table}->{$key} = $vec; } } } =item $measure->traceOptions() This method is internally called to determine the extra options specified by this measure (apart from the default options specified in the WordNet::Similarity base class). Parameters: none. Returns: none. =cut # show all config options specific to this module sub traceOptions { my $self = shift; $self->{traceString} .= "vectorDB File :: ".((defined $self->{vectordb})?"$self->{vectordb}":"")."\n"; $self->SUPER::traceOptions(); } =item $vector_pairs->getRelatedness Computes the relatedness of two word senses using the Vector Algorithm. Parameters: two word senses in "word#pos#sense" format. Returns: Unless a problem occurs, the return value is the relatedness score, which is greater-than or equal-to 0. If an error occurs, then the error level is set to non-zero and an error string is created (see the description of getError()). =cut sub getRelatedness { my $self = shift; my $wps1 = shift; my $wps2 = shift; my $wn = $self->{wn}; my $wntools = $self->{wntools}; my $class = ref $self || $self; # Check the existence of the WordNet::QueryData object. unless($wn) { $self->{errorString} .= "\nError (${class}::getRelatedness()) - "; $self->{errorString} .= "A WordNet::QueryData object is required."; $self->{error} = 2; return undef; } # Check the existence of the WordNet::Tools object. unless($wntools) { $self->{errorString} .= "\nError (${class}::getRelatedness()) - "; $self->{errorString} .= "A WordNet::Tools object is required."; $self->{error} = 2; return undef; } # Using validation code from parseWps() in a super-class my $ret = $self->parseWps($wps1, $wps2); ref $ret or return undef; # Initialize traces. $self->{traceString} = ""; # Now check if the similarity value for these two synsets is in # fact in the cache... if so return the cached value. my $relatedness = $self->{doCache} ? $self->fetchFromCache ($wps1, $wps2) : undef; defined $relatedness and return $relatedness; # Now get down to really finding the relatedness of these two. # see if any traces reqd. if so, put in the synset arrays. if($self->{trace}) { # ah so we do need SOME traces! put in the synset names. $self->{traceString} = "Synset 1: $wps1\n"; $self->{traceString} .= "Synset 2: $wps2\n"; } # initialize the score my $score = 0; my $i = 0; # Get the gloss strings from the get_wn_info module my ($firstStringArray, $secondStringArray, $weightsArray, $functionsStringArray) = $self->getSuperGlosses($wps1, $wps2); for($i = 0; $i < scalar(@{$weightsArray}); $i++) { my $functionsScore = 0; my $funcStringPrinted = 0; my $firstString = $firstStringArray->[$i]; my $secondString = $secondStringArray->[$i]; my $weight = $weightsArray->[$i]; my $functionsString = $functionsStringArray->[$i]; # so those are the two strings for this relation pair. Get the vectors # Preprocess... $firstString =~ s/\'//g; $firstString =~ s/[^a-z0-9]+/ /g; $firstString =~ s/^\s+//; $firstString =~ s/\s+$//; $firstString = $wntools->compoundify($firstString); $secondString =~ s/\'//g; $secondString =~ s/[^a-z0-9]+/ /g; $secondString =~ s/^\s+//; $secondString =~ s/\s+$//; $secondString = $wntools->compoundify($secondString); # Get vectors... score... my $a; my $maga; my $sizea; my $b; my $magb; my $sizeb; my $trr1; my $trr2; # see if any traces reqd. if so, put in the synset arrays. ($a, $trr1, $maga) = $self->_getVector($firstString); &_norm($a, $maga); ($b, $trr2, $magb) = $self->_getVector($secondString); &_norm($b, $magb); $functionsScore = &_inner($a, $b); $score += $functionsScore; # check if the two strings need to be reported in the trace. if($self->{trace}) { if(!$funcStringPrinted) { $self->{traceString} .= "$functionsString: $functionsScore\n"; $self->{traceString} .= "\nString: \"$firstString\"\n$trr1\n"; $self->{traceString} .= "\nString: \"$secondString\"\n$trr2\n"; $funcStringPrinted = 1; } } } # Average the score... $score /= $i if($i > 0); # that does all the scoring. Put in cache if doing cacheing. Then # return the score. $self->{doCache} and $self->storeToCache($wps1, $wps2, $score); return $score; } # Method to compute a context vector from a given body of text... sub _getVector { my $self = shift; my $text = shift; my $ret = {}; return $ret if(!defined $text); my @words = split(/\s+/, $text); my $word; my %types; my $fstFlag = 1; my $localTraces = ""; my $kk; my $mag; # [trace] if($self->{trace}) { $localTraces .= "Word Vectors for: "; } # [/trace] foreach $word (@words) { $types{$word} = 1 if($word !~ /[XGES]{3}\d{5}[XGES]{3}/); } foreach $word (keys %types) { if(defined $self->{table}->{$word} && !defined $self->{stopHash}->{$word}) { my %pieces = split(/\s+/, $self->{table}->{$word}); # [trace] if($self->{trace}) { $localTraces .= ", " if(!$fstFlag); $localTraces .= "$word"; $fstFlag = 0; } # [/trace] foreach $kk (keys %pieces) { $ret->{$kk} = ((defined $ret->{$kk})?($ret->{$kk}):0) + $pieces{$kk}; } } } $mag = 0; foreach $kk (keys %{$ret}) { $mag += ($ret->{$kk} * $ret->{$kk}); } return ($ret, $localTraces, sqrt($mag)); } # Normalizes the sparse vector. sub _norm { my $vec = shift; my $mag = shift; if(defined $vec && defined $mag && $mag != 0) { my $key; foreach $key (keys %{$vec}) { $vec->{$key} /= $mag; } } } # Inner product of two sparse vectors. sub _inner { my $vec1 = shift; my $vec2 = shift; my ($size1, $size2); my $prod = 0; return 0 if(!defined $vec1 || !defined $vec2); $size1 = scalar(keys(%{$vec1})); $size2 = scalar(keys(%{$vec2})); if(defined $size1 && defined $size2 && $size1 < $size2) { my $key; foreach $key (keys %{$vec1}) { $prod += ($vec1->{$key} * $vec2->{$key}) if(defined $vec2->{$key}); } } else { my $key; foreach $key (keys %{$vec2}) { $prod += ($vec1->{$key} * $vec2->{$key}) if(defined $vec1->{$key}); } } return $prod; } 1; __END__ =back =head2 Usage The semantic relatedness modules in this distribution are built as classes that define the following methods: new() getRelatedness() getError() getTraceString() See the WordNet::Similarity(3) documentation for details of these methods. =head3 Typical Usage Examples To create an object of the vector_pairs measure, we would have the following lines of code in the Perl program. use WordNet::Similarity::vector_pairs; $measure = WordNet::Similarity::vector_pairs->new($wn, '/home/sid/vector_pairs.conf'); The reference of the initialized object is stored in the scalar variable '$measure'. '$wn' contains a WordNet::QueryData object that should have been created earlier in the program. The second parameter to the 'new' method is the path of the configuration file for the vector_pairs measure. If the 'new' method is unable to create the object, '$measure' would be undefined. This, as well as any other error/warning may be tested. die "Unable to create object.\n" if(!defined $measure); ($err, $errString) = $measure->getError(); die $errString."\n" if($err); To find the semantic relatedness of the first sense of the noun 'car' and the second sense of the noun 'bus' using the measure, we would write the following piece of code: $relatedness = $measure->getRelatedness('car#n#1', 'bus#n#2'); To get traces for the above computation: print $measure->getTraceString(); However, traces must be enabled using configuration files. By default traces are turned off. =head1 CONFIGURATION FILE The behavior of the measures of semantic relatedness can be controlled by using configuration files. These configuration files specify how certain parameters are initialized within the object. A configuration file may be specified as a parameter during the creation of an object using the new method. The configuration files must follow a fixed format. Every configuration file starts with the name of the module ON THE FIRST LINE of the file. For example, a configuration file for the vector_pairs module will have on the first line 'WordNet::Similarity::vector_pairs'. This is followed by the various parameters, each on a new line and having the form 'name::value'. The 'value' of a parameter is optional (in case of boolean parameters). In case 'value' is omitted, we would have just 'name::' on that line. Comments are supported in the configuration file. Anything following a '#' is ignored till the end of the line. The module parses the configuration file and recognizes the following parameters: =over =item trace The value of this parameter specifies the level of tracing that should be employed for generating the traces. This value is an integer equal to 0, 1, or 2. If the value is omitted, then the default value, 0, is used. A value of 0 switches tracing off. A value of 1 or 2 switches tracing on. A value of 1 displays as traces only the gloss overlaps found. A value of 2 displays as traces all the text being compared. =item cache The value of this parameter specifies whether or not caching of the relatedness values should be performed. This value is an integer equal to 0 or 1. If the value is omitted, then the default value, 1, is used. A value of 0 switches caching 'off', and a value of 1 switches caching 'on'. =item maxCacheSize The value of this parameter indicates the size of the cache, used for storing the computed relatedness value. The specified value must be a non-negative integer. If the value is omitted, then the default value, 5,000, is used. Setting maxCacheSize to zero has the same effect as setting cache to zero, but setting cache to zero is likely to be more efficient. Caching and tracing at the same time can result in excessive memory usage because the trace strings are also cached. If you intend to perform a large number of relatedness queries, then you might want to turn tracing off. =item relation The value of this parameter is the path to a file that contains a list of WordNet relations. The path may be either an absolute path or a relative path. The vector_pairs module combines the glosses of synsets related to the target synsets by these relations and forms the gloss-vector from this combined gloss. WARNING: the format of the relation file is different for the vector_pairs and lesk measures. =item stop The value of this parameter the path of a file containing a list of stop words that should be ignored in the glosses. The path may be either an absolute path or a relative path. =item stem The value of this parameter indicates whether or not stemming should be performed. The value must be an integer equal to 0 or 1. If the value is omitted, then the default value, 0, is used. A value of 1 switches 'on' stemming, and a value of 0 switches stemming 'off'. When stemming is enabled, all the words of the glosses are stemmed before their vectors are created for the vector measure or their overlaps are compared for the lesk measure. =item vectordb The value of this parameter is the path to a file containing word vectors, i.e. co-occurrence vectors for all the words in the WordNet glosses. The value of this parameter may not be omitted, and the vector_pairs measure will not run without a vectors file being specified in a configuration file. =back =head1 RELATION FILE FORMAT The relation file starts with the string "RelationFile" on the first line of the file. Following this, on each consecutive line, a relation is specified in the form -- func(func(func... (func)...))-func(func(func... (func)...)) [weight] Where "func" can be any one of the following functions: hype() = Hypernym of hypo() = Hyponym of holo() = Holonym of mero() = Meronym of attr() = Attribute of also() = Also see sim() = Similar enta() = Entails caus() = Causes part() = Particle pert() = Pertainym of glos = gloss (without example) example = example (from the gloss) glosexample = gloss + example syns = the synset of the concept Each of these specifies a WordNet relation. And the outermost function in the nesting can only be one of glos, example, glosexample or syns. The functions specify which glosses to use for forming the gloss vector of the synset. An optional weight can be specified to weigh the contribution of that relation in the overall score. For example, glos(hype(hypo))-glosexample(hype) 0.5 means that the gloss of the hypernym of the hyponym of the first synset is used to form the gloss vector of the first synset, and the gloss+example of the hypernym of the second synset is used to form the gloss vector of the second synset. The values in these vector are weighted by 0.5. If one of "glos", "example", "glosexample" or "syns" is not specified as the outermost function in the nesting, then "glosexample" is assumed by default. This implies that glosexample(hypo(also))-glosexample(hype) and hypo(also)-hype are equivalent as far as the measure is concerned. =head1 SEE ALSO perl(1), WordNet::Similarity(3), WordNet::QueryData(3) http://www.cs.utah.edu/~sidd http://wordnet.princeton.edu http://www.ai.mit.edu/~jrennie/WordNet http://groups.yahoo.com/group/wn-similarity =head1 AUTHORS Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu =head1 BUGS To report bugs, go to http://groups.yahoo.com/group/wn-similarity/ or send an e-mail to "S". =head1 COPYRIGHT AND LICENSE Copyright (c) 2005, Ted Pedersen, Siddharth Patwardhan and Satanjeev Banerjee This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/Tools.pm0000644000175000017500000001565211021560720017057 0ustar sidsid# WordNet::Tools v2.05 # (Last updated $Id: Tools.pm,v 1.5 2008/06/04 18:38:01 sidz1979 Exp $) # # This module provides some WordNet tools for use with the # WordNet::Similarity modules. # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Tools; =head1 NAME WordNet::Tools - Some tools for use with WordNet. =head1 SYNOPSIS use WordNet::QueryData; use WordNet::Tools; my $wn = WordNet::QueryData->new; my $wntools = WordNet::Tools->new($wn); my $wnHashCode = $wntools->hashCode(); my $newstring = $wntools->compoundify("find compound words like new york city in this text"); =head1 DESCRIPTION This module provides some tools for use with WordNet. For example, the 'compoundify' method detects compound words (as found in WordNet) in a text string and it combines these words into single tokens using underscore separators. Another tool in this module generates a unique hash code corresponding to a WordNet distribution. This hash code is meant to replace the "version" information in WordNet, which is no longer reliable. =head1 METHODS The following methods are defined: =over =cut use strict; use warnings; use Exporter; use WordNet::QueryData; use Digest::SHA1 qw(sha1_base64); use constant MAX_COMPOUND_SIZE => 9; our @ISA = qw(Exporter); our $VERSION = '2.05'; =item WordNet::Tools->new($wn) This is a constructor for this class (and creates a new object of this class). It requires a WordNet::QueryData object as a parameter. Parameters: $wn -- a WordNet::QueryData object. Returns: a new WordNet::Tools object. =cut # Constructor for this module sub new { my $class = shift; my $wn = shift; my $self = {}; # Create the preprocessor object $class = ref $class || $class; bless($self, $class); # Verify the given WordNet::QueryData object return undef if(!defined $wn || !ref $wn || ref($wn) ne "WordNet::QueryData"); $self->{wn} = $wn; # Get the compounds from WordNet foreach my $pos ('n', 'v', 'a', 'r') { foreach my $word ($wn->listAllWords($pos)) { $self->{compounds}->{$word} = 1 if ($word =~ /_/); } } # Compute the WordNet hash-code and store $self->{hashcode} = $self->_computeHashCode(); return undef if(!defined($self->{hashcode})); return $self; } =item $wntools->compoundify($string) This is method identifies all compound words occurring in the given input string. Compound words are multi-word tokens appearing in WordNet. Parameters: $string -- an input text string. Returns: a string with compound words identified. =cut # Detect compounds in a block of text sub compoundify { my $self = shift; my $block = shift; return $block if(!defined $block || !ref $self || !defined $self->{compounds}); my $string; my $done; my $temp; my $firstPointer; my $secondPointer; my @wordsArray; # get all the words into an array @wordsArray = (); while($block =~ /([a-zA-Z0-9_\.\-\/\']+)/g) { push(@wordsArray, $1); } # now compoundify, GREEDILY!! $firstPointer = 0; $string = ""; while($firstPointer <= $#wordsArray) { $secondPointer = (($#wordsArray > ($firstPointer + MAX_COMPOUND_SIZE - 1)) ? ($firstPointer + MAX_COMPOUND_SIZE - 1) : ($#wordsArray)); $done = 0; while(($secondPointer > $firstPointer) && !$done) { $temp = join("_", @wordsArray[$firstPointer .. $secondPointer]); if(defined $self->{compounds}->{$temp}) { $string .= "$temp "; $done = 1; } else { $secondPointer--; } } $string .= "$wordsArray[$firstPointer] " unless($done); $firstPointer = $secondPointer + 1; } $string =~ s/\s+$//; return $string; } =item $wntools->getCompoundsList() This method returns the list of compound words present in WordNet. Parameters: none Returns: reference to an array of compounds. =cut # Return the list of WordNet compounds # Since a deep-copy is performed, this method can be slow. Consequently, # this method should be used sparingly sub getCompoundsList { my $self = shift; my @cList = keys(%{$self->{compounds}}); return \@cList; } =item $wntools->hashCode() This is method returns a unique identifier representing a specific distribution of WordNet. Parameters: none. Returns: a unique identifier (string). =cut # Return the computed hash-code sub hashCode { my $self = shift; return $self->{hashcode}; } # Compute the hash code for the given WordNet distribution # Most of this code was written by Ben Haskell sub _computeHashCode { my $self = shift; my $qd = $self->{wn}; return undef if(!defined($qd)); my $dir = $qd->dataPath(); my $pos = '{noun,verb,adj,adv}'; my @files = sort grep -f, map glob("\Q$dir\E/$_"), "{index,data}.$pos", "$pos.{idx,dat}"; # (stat)[7] returns file size in bytes my $concat = join '.', map { (stat)[7] } @files; return sha1_base64($concat); } 1; __END__ =back =head1 EXPORT None by default. =head1 SEE ALSO perl(1) WordNet::QueryData(3) =head1 AUTHORS Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu =head1 COPYRIGHT AND LICENSE Copyright (c) 2005, Ted Pedersen and Siddharth Patwardhan This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/vector-pairs-relation.dat0000644000175000017500000000014311015336622022336 0ustar sidsidRelationFile example-example example-glos example-syns glos-glos glosexample-glosexample hype-hype WordNet-Similarity-2.05/lib/WordNet/Similarity.pm0000644000175000017500000010710411017752006020104 0ustar sidsid# WordNet::Similarity.pm version 2.05 # (Last updated $Id: Similarity.pm,v 1.50 2008/05/30 10:16:38 sidz1979 Exp $) # # Module containing the version information and pod # for the WordNet::Similarity package, and all measures are # derived from this class. # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Siddharth Patwardhan, University of Utah, Salt Lake City # sidd at cs.utah.edu # # Jason Michelizzi, Univeristy of Minnesota Duluth # mich0212 at d.umn.edu # # Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh # banerjee+ at cs.cmu.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::Similarity; =head1 NAME WordNet::Similarity - Perl modules for computing measures of semantic relatedness. =head1 SYNOPSIS =head2 Basic Usage Example use WordNet::QueryData; use WordNet::Similarity::path; my $wn = WordNet::QueryData->new; my $measure = WordNet::Similarity::path->new ($wn); my $value = $measure->getRelatedness("car#n#1", "bus#n#2"); my ($error, $errorString) = $measure->getError(); die $errorString if $error; print "car (sense 1) <-> bus (sense 2) = $value\n"; =head2 Using a configuration file to initialize the measure use WordNet::Similarity::path; my $sim = WordNet::Similarity::path->new($wn, "mypath.cfg"); my $value = $sim->getRelatedness("dog#n#1", "cat#n#1"); ($error, $errorString) = $sim->getError(); die $errorString if $error; print "dog (sense 1) <-> cat (sense 1) = $value\n"; =head2 Printing traces print "Trace String -> ".($sim->getTraceString())."\n"; =head1 DESCRIPTION =head2 Introduction We observe that humans find it extremely easy to say if two words are related and if one word is more related to a given word than another. For example, if we come across two words, 'car' and 'bicycle', we know they are related as both are means of transport. Also, we easily observe that 'bicycle' is more related to 'car' than 'fork' is. But is there some way to assign a quantitative value to this relatedness? Some ideas have been put forth by researchers to quantify the concept of relatedness of words, with encouraging results. Eight of these different measures of relatedness have been implemented in this software package. A simple edge counting measure and a random measure have also been provided. These measures rely heavily on the vast store of knowledge available in the online electronic dictionary -- WordNet. So, we use a Perl interface for WordNet called WordNet::QueryData to make it easier for us to access WordNet. The modules in this package REQUIRE that the WordNet::QueryData module be installed on the system before these modules are installed. =head2 Function The following function is defined: =over =cut use strict; use Carp; use Exporter; # please use these, but remember that constants are not interpolated: # print "Root: ROOT\n"; # wrong! # print "Root: ".ROOT."\n"; # right # m/ROOT/; # wrong! # $pattern = ROOT; m/$pattern/; # okay use constant ROOT => "*Root*"; use constant ROOT_N => "*Root*#n#1"; use constant ROOT_V => "*Root*#v#1"; # JM 12/9/03 # we would like this to be numeric use constant UNRELATED => -1_000_000; # if we are using an unlimited cache size, it's easier to fake an # unlimited cache with a really big value. use constant UNLIMITED_CACHE => 2_147_483_647; use constant DEFAULT_CACHE => 5_000; use WordNet::Tools; our ($VERSION, @ISA, @EXPORT, @EXPORT_OK, %EXPORT_TAGS); @ISA = qw(Exporter); %EXPORT_TAGS = (); @EXPORT_OK = (); @EXPORT = (); $VERSION = '2.05'; # a hash to contain the module-specific configuration options. our %config_options; =item addConfigOption ($name, $required, $type, $default_val) Adds the configuration option, $name, to the list of known config options (cf. configure()). If $required is true, then the option requires a value; otherwise, the value is optional, and the default value $default_val is used if a value is not specified in the config file. $type is the type of value the option takes. It can be 'i' for integer, 'f' for floating-point, 's' for string, or 'p' for a file name. returns: nothing, but will C on error. You can put the call to this function in an C block to trap the exception (N.B., the C form of C does not significantly degrade performance, unlike the C form of C. See C). =cut sub addConfigOption { my $name = shift; my $required = shift; my $type = shift; my $default = shift; my ($package, $filename, $line) = caller; if ($package eq 'vector') { print "vector\n" } $config_options{$name}->{$package} = [($required ? 1 : 0), $type, $default]; } =back =head2 Methods The following methods are defined in this package: =head3 Public methods =over =item $obj->new ($wn, $config_file) The constructor for WordNet::Similarity::* objects. Parameters: $wn is a WordNet::QueryData object, $config_file is a configuration file (optional). Return value: the new blessed object =cut sub new { my $class = shift; my $this = {}; $class = ref $class || $class; $this->{errorString} = ''; $this->{error} = 0; if ($class eq 'WordNet::Similarity') { $this->{errorString} .= "\nWarning (${class}::new()) - "; $this->{errorString} .= "This class is intended to be an abstract base class for a measure. Your class should override it."; $this->{error} = 1; } $this->{wn} = shift; unless (defined $this->{wn}) { $this->{errorString} .= "\nError (${class}::new()) - "; $this->{errorString} .= "A WordNet::QueryData object is required."; $this->{error} = 2; } else { # queryWord() in older versions of WordNet::QueryData was broken $this->{wn}->VERSION (1.30); my $wntools = WordNet::Tools->new($this->{wn}); unless (defined $wntools) { $this->{errorString} .= "\nError (${class}::new()) - "; $this->{errorString} .= "Error creating WordNet::Tools object."; $this->{error} = 2; } $this->{wntools} = $wntools; } bless $this, $class; $this->initialize (shift) if $this->{error} < 2; $this->setPosList(); # [trace] if ($this->{trace}) { $this->{traceString} = "${class} object created:\n"; $this->{traceString} .= "trace :: $this->{trace}\n"; $this->{traceString} .= "cache :: $this->{doCache}\n"; $this->{traceString} .= "cache size :: $this->{maxCacheSize}\n"; $this->traceOptions (); } # [/trace] return $this; } =item $obj->initialize ($config_file) Performs some initialization on the module. Parameter: the location of a configuration file Returns: nothing =cut sub initialize { my $self = shift; # initialize cache--caching is ON by default $self->{doCache} = 1; $self->{simCache} = (); $self->{traceCache} = (); $self->{cacheQ} = (); # (JM - 11/26/03) # Using unlimited cache can cause simCache and esp. traceCache # to use huge amounts of memory if a lot of queries are performed. # $self->{maxCacheSize} = UNLIMITED_CACHE; $self->{maxCacheSize} = DEFAULT_CACHE; # initialize tracing--tracing is OFF by default $self->{trace} = 0; # JM 1/26/04 # moved option for root node to PathFinder.pm # # use a virtual root node (if applicable) # six of the measures (res, lin, jcn, path, wup, lch) use a virtual # root node in some way, and it is present by default in these cases. # Three of the measures--path, wup, and lch--allow this root node to be # turned off (i.e., the measure would be run without a root node). # $self->{rootNode} = 1; return $self->configure (@_); } =item $obj->configure($config_file) Parses a configuration file. If you write a module and want to add a new configuration option, you can use the addConfigOption function to specify the name and nature of the option. The value of the option is place in "self": $self->{optionname}. parameter: a file name returns: true if parsing of config file was successful, false on error =cut sub configure { my $self = shift; my $file = shift; my $class = ref $self || $self; while (my ($opt, $classHash) = each %config_options) { while (my ($class, $arrayRef) = each %$classHash) { if ($self->isa ($class)) { $self->{$opt} = $arrayRef->[2]; } } } return unless defined $file; # my %options = %config_options; # foreach my $optionstr (@options) { # next unless $optionstr; # my $patternstr = '^(\w+)([=:])([ifps])$'; # unless ($optionstr =~ m/$patternstr/o) { # $self->{errorString} .= "\nWarning (${class}::configure) - "; # $self->{errorString} .= "Bad option string $optionstr: option strings"; # $self->{errorString} .= " must match the pattern ${patternstr}."; # $self->{error} = ($self->{error} > 1) ? $self->{error} : 1; # } # $options{$1} = [$2, $3]; # } unless (open CF, $file) { my $class = ref $self || $self; $self->{errorString} .= "\nError (${class}::configure) - "; $self->{errorString} .= "Unable to open config file $file."; $self->{error} = 2; return undef; } $_ = ; unless (m/^$class/) { close CF; my $class = ref $self || $self; $self->{errorString} .= "\nError (${class}::configure()) - "; $self->{errorString} .= "$file does not appear to be a config file for $class."; $self->{error} = 2; return undef; } # keep track of which options we've already seen my %optionCache; my %rtn; OPTION_READ: while () { s/\s+|\#.*//g; # ignore comments # JM 12/4/03 (#3) # edited the below to # (1) ensure the values for options are valid # (2) handle options without values in a consistent manner if ($_ eq "") { next; } # JM 1/9/04 (#1) # added the following block to check for repeated options my ($option, $value) = m/^(\w+)::(.*)$/; if ($option) { unless (defined $optionCache{$option}) { $optionCache{$option} = defined $value ? $value : 1; } else { # we've already seen this option $self->{errorString} .= "\nWarning (${class}::configure()) - "; $self->{errorString} .= "configuration option '$option' encountered twice in config file"; $self->{error} = ($self->{error} > 1) ? $self->{error} : 1; } } if (m/^trace::(.*)/i) { my $trace = $1; # JM 12/4/03 (#3) # $self->{trace} = 1; # $self->{trace} = $trace if $trace =~ m/^[012]$/; next OPTION_READ if $trace eq ""; if ($trace =~ m/^[012]$/) { $self->{trace} = $trace; } else { $self->{errorString} .= "\nWarning (${class}::configure()) - "; $self->{errorString} .= "$trace is an invalid value for option trace."; $self->{error} = ($self->{error} > 1) ? $self->{error} : 1; } } elsif (m/^cache::(.*)/i) { my $cache = $1; # JM 12/4/03 (#3) # $self->{doCache} = 1; # $self->{doCache} = $cache if $cache =~ m/^[01]$/; next OPTION_READ if $cache eq ""; if ($cache =~ m/^[01]$/) { $self->{doCache} = $cache; } else { $self->{errorString} .= "\nWarning (${class}::configure()) - "; $self->{errorString} .= "$cache is an invalid value for option cache."; $self->{error} = ($self->{error} > 1) ? $self->{error} : 1; } } elsif (m/^maxCacheSize::(.*)/i) { my $mcs = $1; # JM 12/4/03 (#3) # $self->{maxCacheSize} = DEFAULT_CACHE; # if ($mcs =~ /unlimited/i) { # $self->{maxCacheSize} = UNLIMITED_CACHE; # next; # } # $self->{maxCacheSize} = $mcs if defined ($mcs) and $mcs =~ m/^\d+$/; next OPTION_READ if $mcs eq ""; if ($mcs =~ m/^unlimited/i) { $self->{maxCacheSize} = UNLIMITED_CACHE; } elsif ($mcs =~ m/^\d+$/) { $self->{maxCacheSize} = $mcs + 0; } else { $self->{errorString} .= "\nWarning (${class}::configure()) - "; $self->{errorString} .= "$mcs is an invalid value for option maxCacheSize."; $self->{error} = ($self->{error} > 1) ? $self->{error} : 1; } } # JM 1/26/04 # moved code for the rootNode option to PathFinder.pm else { OPTION_LOOP: foreach my $option (keys %config_options) { my $found = 0; CLASS_LOOP: foreach my $class (keys %{$config_options{$option}}) { if ($self->isa ($class) and defined $config_options{$option}->{$class}) { $found = $class; last CLASS_LOOP; } } next OPTION_LOOP unless $found; if (not defined $config_options{$option}->{$found}) { print STDERR "$option $class\n"; } my ($required, $type, $dflt)= @{$config_options{$option}->{$found}}; if (m/^${option}::(.*)$/i) { my $t = $1; if ($t =~ m/^\s*$/) { if ($required) { #error $self->{errorString} .= "\nWarning (${class}::configure()) - "; $self->{errorString} .= "Option $option has no value."; $self->{error} .= $self->{error} > 1 ? $self->{error} : 1; } else { # do nothing $self->{$option} = $dflt } } else { if ($type eq 'i') { # JM 12/4/03 (#3) # $self->{$option} = int ($t); if ($t =~ m/^\d+$/) { $self->{$option} = $t + 0; } else { $self->{errorString} .= "\nWarning (${class}::configure()) - "; $self->{errorString} .= "$t is an invalid value for option $option."; $self->{error} = ($self->{error} > 1) ? $self->{error} : 1; } } elsif ($type eq 'f') { # JM 12/4/03 (#3) # check if this is a float if ($t =~ /^[+-]?(?:\d+\.?\d*|\.\d+)(?:e[+-]?\d+)?$/) { $self->{$option} = $t + 0.0; } else { $self->{errorString} .= "\nWarning (${class}::configure()) - "; $self->{errorString} .= "$t is an invalid value for option $option."; $self->{error} = ($self->{error} > 1) ? $self->{error} : 1; } } elsif ($type eq 'p') { if (-e $t) { $self->{$option} = $t; } else { $self->{errorString} .= "\nWarning (${class}::configure()) - "; $self->{errorString} .= "$t is not a valid filename for option $option."; $self->{error} = ($self->{error} > 1) ? $self->{error} : 1; } } elsif ($type eq 's') { $self->{$option} = $t; } else { $self->{errorString} .= "\nWarning (${class}::configure()) - "; $self->{errorString} .= "Unknown/invalid option type $type.\n"; } } next OPTION_READ; } } # error s/::.*//; my $class = ref $self || $self; $self->{errorString} .="\nWarning (${class}::configure()) - "; $self->{errorString} .= "Ignoring unrecognized option '$_'."; $self->{error} = $self->{error} > 1 ? $self->{error} : 1; } } close CF; return 1; } =item $obj->getTraceString(Z<>) Returns the current trace string and resets the trace string to empty. If tracing is turned off, then an empty string will always be returned. =cut sub getTraceString { my $self = shift; return '' unless $self->{trace} and defined $self->{traceString}; my $str = $self->{traceString}; $self->{traceString} = ''; $str =~ s/\n{2,}$//; return $str; } =item $obj->getError(Z<>) Checks to see if any errors have occurred. Returns a list of the form S<($level, $string)>. If $level is 0, then no errors have occurred; if $level is non-zero, then an error has occurred. A value of 1 is considered a warning, and a value of 2 is considered an error. If $level is non-zero, then $string will have a (hopefully) meaningful error message. =cut sub getError { my $self = shift; my $error = $self->{error}; my $errorString = $self->{errorString}; $self->{error} = 0; $self->{errorString} = ""; $errorString =~ s/^[\r\n\t ]+//; return ($error, $errorString); } =item $obj->traceOptions(Z<>) Prints module-specific options to the trace string. Any module that adds configuration options via addConfigOption should override this method. Options should be printed out using the following format: $self->{traceString} .= "option_name :: $option_value\n" Note that the option name is separated from its current value by a space, two colons, and another space. The string should be terminated by a newline. Since multiple modules may be overriding this method, any module that overrides this method should insure that the superclass' method gets called as well. You do this by putting this line at the end of your method: $self->SUPER::traceOptions(); returns: nothing =cut # JM 12/5/03 (#1) sub traceOptions { # nothing to do here, this is a just a placeholder # subclasses should override this to print all config options to # the traceString } =item $obj->parseWps($synset1, $synset2) parameters: synset1, synset2 returns: a reference to an array [$word1, $pos1, $sense1, $offset1, $word2, $pos2, $sense2, $offset2] or undef This method checks the format of the two input synsets by calling validateSynset() for each synset. If the synsets are in wps format, a reference to an array will be returned. This array has the form [$word1, $pos1, $sense1, $offset1, $word2, $pos2, $sense2, $offset2] where $word1 is the word part of $wps1, $pos1, is the part of speech of $wps1, $sense1 is the sense from $wps. $offset1 is the offset for $wps1. If an error occurs (such as a synset being poorly-formed), then undef is returned, the error level is set to non-zero, and an error message is appended to the error string. =cut sub parseWps { my $self = shift; my $wps1 = shift; my $wps2 = shift; my $class = ref $self || $self; # Undefined input cannot go unpunished. unless (defined $wps1 and defined $wps2 and length $wps1 and length $wps2) { $self->{errorString} .= "\nWarning (${class}::parseWps()) - "; $self->{errorString} .= "Variable for input synset ".(length($wps1) ? 2 : 1)." undefined."; $self->{error} = ($self->{error} < 1) ? 1 : $self->{error}; return undef; } my ($word1, $pos1, $sense1, $offset1) = $self->validateSynset ($wps1); my ($word2, $pos2, $sense2, $offset2) = $self->validateSynset ($wps2); # Check to see if validation of synsets succeeded, if not, then # bail out (error message already set by validateSynset). unless (defined $word1 and defined $pos1 and defined $sense1 and defined $word2 and defined $pos2 and defined $sense2) { return undef; } return [$word1, $pos1, $sense1, $offset1, $word2, $pos2, $sense2, $offset2]; } =item $obj->validateSynset($synset) parameter: synset returns: a list or undef on error synset is a string in word#pos#sense format This method does the following: =over =item 1. Verifies that the synset is well-formed (i.e., that it consists of three parts separated by #s, the pos is one of {n, v, a, r} and that sense is a natural number). A synset that matches the pattern '[^\#]+\#[nvar]\#\d+' is considered well-formed. =item 2. Checks if the synset exists by trying to find the offset for the synset =back If any of these tests fails, then the error level is set to non-zero, a message is appended to the error string, and undef is returned. If the synset is well-formed and exists, then a list is returned that has the format ($word, $pos, $sense, $offset). =cut sub validateSynset { my $self = shift; my $synset = shift; my $class = ref $self || $self; # check to see that synset is in w#p#s format unless (defined ($synset) and length ($synset)) { $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::validateSynset()) - "; $self->{errorString} .= "Variable representing synset is undefined (or an empty string)."; return undef; } my ($word, $pos, $sense) = split (/\#/, $synset); unless (defined $word) { $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::validateSynset()) - "; $self->{errorString} .= "Invalid synset ($synset): word undefined."; return undef; } unless (defined $pos) { no strict 'vars'; $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::validateSynset()) - "; $self->{errorString} .= "Invalid synset ($synset): part of speech undefined."; return undef; } unless (defined $sense) { $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::validateSynset()) - "; $self->{errorString} .= "Invalid synset ($synset): sense number undefined."; return undef; } # check to make sure the word, pos, and sense are well-formed if ($word !~ /^[^\#]+$/) { $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::validateSynset()) - "; $self->{errorString} .= "$synset has a poorly-formed word ($word)."; return undef; } elsif ($pos !~ /^[nvar]$/) { $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::validateSynset()) - "; $self->{errorString} .= "$synset has a bad part of speech ($pos). Part of speech must be one of n, v, a, r."; return undef; } elsif ($sense !~ /^\d+$/) { $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::validateSynset()) - "; $self->{errorString} .= "$synset has a bad sense number ($pos). Sense number must be a natural number."; return undef; } # check to see if synset exists my $offset = $self->{wn}->offset ($synset); unless ($offset) { $self->{error} = $self->{error} < 1 ? 1 : $self->{error}; $self->{errorString} .= "\nWarning (${class}::validateSynset()) - "; $self->{errorString} .= "$synset not found in WordNet."; return undef; } return ($word, $pos, $sense, $offset); } =item $obj->getRelatedness($synset1, $synset2) parameters: synset1, synset2 returns: a relatedness score This is a virtual method. It must be overridden by a module that is derived from this class. This method takes two synsets and returns a numeric value as their score of relatedness. =cut sub getRelatedness { my $self = shift; my $class = ref $self || $self; $self->{errorString} .= "\nError (${class}::getRelatedness()) - "; $self->{errorString} .= "This is a virtual method provided by "; $self->{errorString} .= __PACKAGE__ . " that must be overridden."; $self->{error} = 2; return undef; } # Subroutine that takes as input an array of offsets # or offsets(POS) and for each prints to traceString the # WORD#POS#(/) # INPUT PARAMS : $pos .. Part of speech # : ($offestpos1, $offsetpos2, ...) .. Array of offsetPOS's # or offests # RETURN VALUES : none. sub _printSet { use Carp; carp "This method is deprecated; use printSet instead"; my $self = shift; my $pos = shift; my $wn = $self->{wn}; my @offsets = @_; my $wps; my $opstr = ""; foreach my $offset (@offsets) { $offset =~ m/(\d+)([a-z])?/; $offset = $1; if($offset) { $wps = $wn->getSense($offset, ($2 ? $2 : $pos)); } else { $wps = "*Root*\#". ($2 ? $2 : $pos) ."\#1"; } $wps =~ s/ +/_/g; if($self->{trace} == 2 && defined $offset && $offset != 0) { $wps =~ s/\#[0-9]*$/\#$offset/; } $opstr .= "$wps "; } $opstr =~ s/\s+$//; $self->{traceString} .= $opstr if($self->{trace}); } =item $obj->printSet ($pos, $mode, @synsets) If tracing is turned on, prints the contents of @synsets to the trace string. The contents of @synsets can be either wps strings or offsets. If they are wps strings, then $mode must be the string 'wps'; if they are offsets, then the mode must be 'offset'. Please don't try to mix wps and offsets. Returns the string that was appended to the trace string. =cut sub printSet { my $self = shift; my $pos = shift; my $mode = shift; my @synsets = @_; my $opstr = ''; my $wn = $self->{wn}; my $wps; if ($mode eq 'offset') { foreach my $offset (@synsets) { $offset =~ m/^(\d+)([a-z])?/; $offset = $1; if ($offset) { $wps = $wn->getSense ($offset, (defined $2 ? $2 : $pos)); } else { $wps = "*Root*\#" . (defined $2 ? $2 : $pos) . "\#1"; } $wps =~ tr/ /_/; if ($self->{trace} == 2 && defined $offset) { $wps =~ s/\#[0-9]+$/\#$offset/; } $opstr .= "$wps "; } } elsif ($mode eq 'wps') { WPS: foreach my $wps (@synsets) { unless ($self->{trace} == 2) { $opstr .= "$wps "; next WPS; } my $offset = scalar ($wps =~ /\*Root\*/i) ? 0 : $wn->offset ($wps); my ($word, $p) = $wps =~ /^(\S+)\#([nvar])\#\d+$/; $opstr .= "$word#$p#$offset "; } } $opstr =~ s/\s+$//; $self->{traceString} .= $opstr if $self->{trace}; return $opstr; } # you should only call this if $self->{doCache} is true # nothing bad will happen if you call anyways, but it will slow things down # # NEW! You can specify whether or not relatedness is symmetric: if # relatedness (c1, c2) = relatedness (c2, c1), then relatedness is symmetric. =item $obj->fetchFromCache($wps1, $wps2, $non_symmetric) Looks for the relatedness value of ($wps1, $wps2) in the cache. If $non_symmetric is false (or isn't specified), then the cache is searched for ($wps2, $wps1) if ($wps1, $wps2) isn't found. Returns: a relatedness value or undef if none found in the cache. =cut sub fetchFromCache { my $self = shift; my ($wps1, $wps2, $non_symmetric) = @_; $self->{doCache} or return undef; $non_symmetric = 0 unless defined $non_symmetric; if (defined $self->{simCache}->{"${wps1}::$wps2"}) { if ($self->{traceCache}->{"${wps1}::$wps2"}) { $self->{traceString} .= $self->{traceCache}->{"${wps1}::$wps2"}; } return $self->{simCache}->{"${wps1}::$wps2"}; } elsif (!$non_symmetric and defined $self->{simCache}->{"${wps2}::$wps1"}) { if ($self->{traceCache}->{"${wps2}::$wps1"}) { $self->{traceString} .= $self->{traceCache}->{"${wps2}::$wps1"}; } return $self->{simCache}->{"${wps2}::$wps1"}; } return undef; } =item $obj->storeToCache ($wps1, $wps2, $score) Stores the relatedness value, $score, of ($wps1, $wps2) to the cache. Returns: nothing =cut sub storeToCache { my $self = shift; my ($wps1, $wps2, $score) = @_; $self->{doCache} or return; $self->{simCache}->{"${wps1}::$wps2"} = $score; if ($self->{trace}) { $self->{traceCache}->{"${wps1}::$wps2"} = $self->{traceString} } push (@{$self->{cacheQ}}, "${wps1}::$wps2"); if (($self->{maxCacheSize} >= 0) and ($self->{maxCacheSize} != UNLIMITED_CACHE)) { while (scalar (@{$self->{cacheQ}}) > $self->{maxCacheSize}) { my $delItem = shift(@{$self->{cacheQ}}); delete $self->{simCache}->{$delItem}; delete $self->{traceCache}->{$delItem}; } } } 1; __END__ =back =head2 Discussion This package consists of Perl modules along with supporting Perl programs that implement the semantic relatedness measures described by Leacock Chodorow (1998), Jiang Conrath (1997), Resnik (1995), Lin (1998), Wu Palmer (1993), Hirst St-Onge (1998) the Extended Gloss Overlaps measure by Banerjee and Pedersen (2002) and a Gloss Vector measure recently introduced by Patwardhan and Pedersen. The package contains Perl modules designed as object classes with methods that take as input two word senses. The semantic distance between these word senses is returned by these methods. A quantitative measure of the degree to which two word senses are related has wide ranging applications in numerous areas, such as word sense disambiguation, information retrieval, etc. For example, in order to determine which sense of a given word is being used in a particular context, the sense having the highest relatedness with its context word senses is most likely to be the sense being used. Similarly, in information retrieval, retrieving documents containing highly related concepts are more likely to have higher precision and recall values. A command line interface to these modules is also present in the package. The simple, user-friendly interface simply returns the relatedness measure of two given words. Number of switches and options have been provided to modify the output and enhance it with trace information and other useful output. Support programs for generating information content files from various corpora are also available in the package. The information content files are required by three of the measures for computing the relatedness of concepts. There is also a tool to find the depths of the taxonomies in WordNet. =head3 Configuration files The behavior of the measures of semantic relatedness can be controlled by using configuration files. These configuration files specify how certain parameters are initialized within the object. A configuration file may be specified as a parameter during the creation of an object using the new method. The configuration files must follow a fixed format. Every configuration file starts with the name of the module ON THE FIRST LINE of the file. For example, a configuration file for the res module will have on the first line 'WordNet::Similarity::res'. This is followed by the various parameters, each on a new line and having the form 'name::value'. The 'value' of a parameter is optional (in case of boolean parameters). In case 'value' is omitted, we would have just 'name::' on that line. Comments are supported in the configuration file. Anything following a '#' is ignored in the configuration file. Sample configuration files are present in the '/samples' subdirectory of the package. Each of the modules has specific parameters that can be set/reset using the configuration files. Please read the manpages or the perldocs of the respective modules for details on the parameters specific to each of the modules. For instance, 'man WordNet::Similarity::res' or 'perldoc WordNet::Similarity::res' should display the documentation for the Resnik module. The module parses the configuration file and recognizes the following parameters: =over =item trace This option is supported by all measures. The value of this parameter specifies the level of tracing that should be employed for generating the traces. This value is an integer equal to 0, 1, or 2. If the value is omitted, then the default value, 0, is used. A value of 0 switches tracing off. A value of 1 or 2 switches tracing on. The difference between a value of 1 or 2 depends upon the measure being used. For vector and lesk, a value of 1 displays as traces only the gloss overlaps found. A value of 2 displays as traces all the text being compared. For the res, lin, jcn, wup, lch, path, and hso measures, a trace of level 1 means the synsets are represented as word#pos#sense strings, while for level 2, the synsets are represented as word#pos#offset strings. =item cache This option is supported by all measures. The value of this parameter specifies whether or not caching of the relatedness values should be performed. This value is an integer equal to 0 or 1. If the value is omitted, then the default value, 1, is used. A value of 0 switches caching 'off', and a value of 1 switches caching 'on'. =item maxCacheSize This option is supported by all measures. The value of this parameter indicates the size of the cache, used for storing the computed relatedness value. The specified value must be a non-negative integer. If the value is omitted, then the default value, 5,000, is used. Setting maxCacheSize to zero has the same effect as setting cache to zero, but setting cache to zero is likely to be more efficient. Caching and tracing at the same time can result in excessive memory usage because the trace strings are also cached. If you intend to perform a large number of relatedness queries, then you might want to turn tracing off. =back =head2 Usage The semantic relatedness modules in this distribution are built as classes. The classes define four methods that are useful in finding relatedness values for pairs of synsets. new() getRelatedness() getError() getTraceString() =head3 Typical Usage Examples To create an object of the Resnik measure, we would have the following lines of code in the Perl program. use WordNet::Similarity::res; $object = WordNet::Similarity::res->new($wn, '~/resnik.conf'); The reference of the initialized object is stored in the scalar variable '$object'. '$wn' contains a WordNet::QueryData object that should have been created earlier in the program. The second parameter to the 'new' method is the path of the configuration file for the resnik measure. If the 'new' method is unable to create the object, '$object' would be undefined. This, as well as any other error/warning may be tested. die "Unable to create resnik object.\n" unless defined $object; ($err, $errString) = $object->getError(); die $errString."\n" if($err); To create a Leacock-Chodorow measure object, using default values, i.e. no configuration file, we would have the following: use WordNet::Similarity::lch; $measure = WordNet::Similarity::lch->new($wn); To find the semantic relatedness of the first sense of the noun 'car' and the second sense of the noun 'bus' using the resnik measure, we would write the following piece of code: $relatedness = $object->getRelatedness('car#n#1', 'bus#n#2'); To get traces for the above computation: print $object->getTraceString(); However, traces must be enabled using configuration files. By default traces are turned off. =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Jason Michelizzi, Univeristy of Minnesota Duluth mich0212 at d.umn.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu =head1 BUGS None. To submit a bug report, go to http://groups.yahoo.com/group/wn-similarity or send e-mail to tpederse I d.umn.edu. =head1 SEE ALSO perl(1), WordNet::Similarity::jcn(3), WordNet::Similarity::res(3), WordNet::Similarity::lin(3), WordNet::Similarity::lch(3), WordNet::Similarity::hso(3), WordNet::Similarity::lesk(3), WordNet::Similarity::wup(3), WordNet::Similarity::path(3), WordNet::Similarity::random(3), WordNet::Similarity::ICFinder(3), WordNet::Similarity::PathFinder(3) WordNet::QueryData(3) http://www.cs.utah.edu/~sidd http://wordnet.princeton.edu http://www.ai.mit.edu/~jrennie/WordNet http://groups.yahoo.com/group/wn-similarity =head1 COPYRIGHT Copyright (c) 2005, Ted Pedersen, Siddharth Patwardhan, Jason Michelizzi and Satanjeev Banerjee This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/get_wn_info.pm0000644000175000017500000006401611015336622020260 0ustar sidsid# WordNet::get_wn_info.pm version 2.04 # (Last updated $Id: get_wn_info.pm,v 1.1 2008/03/27 05:13:01 sidz1979 Exp $) # # Package used by WordNet::Similarity::lesk module that # computes semantic relatedness of word senses in WordNet # using gloss overlaps. # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota, Duluth # tpederse at d.umn.edu # # Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh # banerjee+ at cs.cmu.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::get_wn_info; =head1 NAME WordNet::get_wn_info - Provides access to glosses related to a concept in WordNet =head1 SYNOPSIS use WordNet::get_wn_info; my $gwn = WordNet::get_wn_info->new($wn, 0); my $hypHash = $gwn->hype($synsHash, 0); my $holHash = $gwn->holo($synsHash, 0); =head1 DESCRIPTION This module provides a layer between the user and the WordNet::QueryData module, by providing easy access to glosses and synsets related to particular concpets in WordNet. =head2 Methods =over =cut use WordNet::stem; use strict; use Exporter; use vars qw($VERSION @ISA @EXPORT @EXPORT_OK %EXPORT_TAGS); @ISA = qw(Exporter); %EXPORT_TAGS = (); @EXPORT_OK = (); @EXPORT = (); $VERSION = '2.04'; =item new Creates a new WordNet::get_wn_info object. Parameters: $wn, $stemFlag Returns: $gwn =cut # function to set up the wordnet object and the various boundary indices sub new { my $className; my $self = {}; my $wn; my $stemmingReqd; my $stemmer; # get the class name $className = shift; # get wordnet object $wn = shift; $self->{'wn'} = $wn; # check WordNet::QueryData version $wn->VERSION(1.39); # check if stemming called for $stemmingReqd = shift; $self->{'stem'} = $stemmingReqd; if($stemmingReqd) { $stemmer = WordNet::stem->new($wn); $self->{'stemmer'} = $stemmer; } # set up various boundaries. $self->{'glosBoundaryIndex'} = 0; $self->{'exampleBoundaryIndex'} = 0; $self->{'synonymBoundaryIndex'} = 0; bless($self, $className); return $self; } =item hype Returns the hypernyms of a synset. Parameters: $synsHash, $ipType Returns: $hypeHash =cut # NOTE: Thanks to Wybo Wiersma for contributing optimizations # in the following code. # function to take a set of synsets and to return their # hypernyms. both input and output will be arrays of fully qualified # WordNet senses (in WORD#POS#SENSE format). sub hype { my $self = shift; my $wn = $self->{'wn'}; my ($synsetsh, $outprep) = @_; # check if this is a request for the input-output types of this # function return(1, 1) if(defined($outprep)); my %newsynsetsh; foreach my $syns (keys %{$synsetsh}) { # check if in word-pos-sense format # TODO: Return error code instead of "exit" if($syns !~ /\#\w\#\d+/) { print STDERR "$syns is not in WORD\#POS\#SENSE format!\n"; exit; } # get the hypernyms my @hypernyms = $wn->querySense($syns, "hypes"); # put the hypernyms in a hash. this way we will avoid multiple # copies of the same hypernym my $temp; foreach $temp (@hypernyms) { $newsynsetsh{$temp} = 1; } } # return the hypernyms in an hash ref return(\%newsynsetsh); } =item hypo Returns the hyponyms of a synset. Parameters: $synsHash, $ipType Returns: $hypoHash =cut # function to take a set of synsets and to return their # hyponyms. both input and output will be arrays of fully qualified # WordNet senses (in WORD#POS#SENSE format). sub hypo { my $self = shift; my $wn = $self->{'wn'}; my ($synsetsh, $outprep) = @_; # check if this is a request for the input-output types of this # function return(1, 1) if(defined($outprep)); my %hyponymHash; foreach my $syns (keys %{$synsetsh}) { # check if in word-pos-sense format # TODO: Replace error message and exit with return error code. if($syns !~ /\#\w\#\d+/) { print STDERR "$syns is not in WORD\#POS\#SENSE format!\n"; exit; } # get the hyponyms my @hyponyms = $wn->querySense($syns, "hypos"); # put the hyponyms in a hash. this way we will avoid multiple # copies of the same hyponym my $temp; foreach $temp (@hyponyms) { $hyponymHash{$temp} = 1; } } # return the hyponyms in an hash ref return(\%hyponymHash); } =item holo Returns the holonyms of a synset. Parameters: $synsHash, $ipType Returns: $holoHash =cut # function to take a set of synsets and to return their # holonyms. both input and output will be arrays of fully qualified # WordNet senses (in WORD#POS#SENSE format). sub holo { my $self = shift; my $wn = $self->{'wn'}; my ($synsetsh, $outprep) = @_; my %holonymHash = (); # check if this is a request for the input-output types of this # function return(1, 1) if(defined($outprep)); foreach my $syns (keys %{$synsetsh}) { # check if in word-pos-sense format # TODO: Replace error message and exit with return error code. if($syns !~ /\#\w\#\d+/) { print STDERR "$syns is not in WORD\#POS\#SENSE format!\n"; exit; } # get the holonyms my @holonyms = $wn->querySense($syns, "holo"); # put the holonyms in a hash. this way we will avoid multiple # copies of the same holonym my $temp; foreach $temp (@holonyms) { $holonymHash{$temp} = 1; } } # return the holonyms in an hash ref return(\%holonymHash); } =item mero Returns the meronyms of a synset. Parameters: $synsHash, $ipType Returns: $meroHash =cut # function to take a set of synsets and to return their # meronyms. both input and output will be arrays of fully qualified # WordNet senses (in WORD#POS#SENSE format). sub mero { my $self = shift; my $wn = $self->{'wn'}; my ($synsetsh, $outprep) = @_; my %meronymHash = (); # check if this is a request for the input-output types of this # function return (1, 1) if(defined($outprep)); foreach my $syns (keys %{$synsetsh}) { # check if in word-pos-sense format # TODO: Replace error message and exit with return error code. if($syns !~ /\#\w\#\d+/) { print STDERR "$syns is not in WORD\#POS\#SENSE format!\n"; exit; } # get the meronyms my @meronyms = $wn->querySense($syns, "mero"); # put the meronyms in a hash. this way we will avoid multiple # copies of the same meronym my $temp; foreach $temp (@meronyms) { $meronymHash{$temp} = 1; } } # return the meronyms in an hash ref return(\%meronymHash); } =item attr Returns the attributes of a synset. Parameters: $synsHash, $ipType Returns: $attrHash =cut # function to take a set of synsets and to return their # attributes. both input and output will be arrays of fully qualified # WordNet senses (in WORD#POS#SENSE format). sub attr { my $self = shift; my $wn = $self->{'wn'}; my ($synsetsh, $outprep) = @_; my %attrHash = (); # check if this is a request for the input-output types of this # function return (1, 1) if(defined($outprep)); foreach my $syns (keys %{$synsetsh}) { # check if in word-pos-sense format if($syns !~ /\#\w\#\d+/) { print STDERR "$syns is not in WORD\#POS\#SENSE format!\n"; exit; } # get the attrs my @attrs = $wn->querySense($syns, "attr"); # put the attrs in a hash. this way we will avoid multiple # copies of the same attr my $temp; foreach $temp (@attrs) { $attrHash{$temp} = 1; } } # return the attrs in an hash ref return(\%attrHash); } =item also Returns the also-see of a synset. Parameters: $synsHash, $ipType Returns: $alsoHash =cut # function to take a set of synsets and to return their also-see # synsets. both input and output will be arrays of fully qualified # WordNet senses (in WORD#POS#SENSE format). sub also { my $self = shift; my $wn = $self->{'wn'}; my ($synsetsh, $outprep) = @_; my %alsoSeeHash = (); # check if this is a request for the input-output types of this # function return (1, 1) if(defined($outprep)); foreach my $syns (keys %{$synsetsh}) { # check if in word-pos-sense format if($syns !~ /\#\w\#\d+/) { print STDERR "$syns is not in WORD\#POS\#SENSE format!\n"; exit; } # get the also see synsets my @alsoSees = $wn->queryWord($syns, "also"); # put the synsets in a hash. this way we will avoid multiple # copies of the same synset my $temp; foreach $temp (@alsoSees) { $alsoSeeHash{$temp} = 1; } } # return the synsets in an hash ref return(\%alsoSeeHash); } =item deri Returns the derived forms of a synset. Parameters: $synsHash, $ipType Returns: $deriHash =cut # function to take a set of words and to return their derived forms. # both input and output will be arrays of fully qualified # WordNet senses (in WORD#POS#SENSE format). sub deri { my $self = shift; my $wn = $self->{wn}; my ($wordsh, $outprep) = @_; my %deriHash = (); return (1, 1) if(defined($outprep)); foreach my $word (keys %{$wordsh}) { # TODO: Replace error message and exit with return error code. if($word !~ m/\#\w+\#\d+/) { print STDERR "$word is not in WORD#POS#SENSE format!\n"; exit 1; } my @deris = $wn->queryWord($word, "deri"); foreach my $temp (@deris) { $deriHash{$temp} = 1; } } return(\%deriHash); } =item domn Returns the domains of a synset. Parameters: $synsHash, $ipType Returns: $domnHash =cut # function to take a set of synsets and to return their domain # synsets. both input and output will be arrays of fully qualified # WordNet senses (in WORD#POS#SENSE format). sub domn { my $self = shift; my $wn = $self->{wn}; my ($wordsh, $outprep) = @_; my %domnHash = (); return(1, 1) if(defined($outprep)); foreach my $word (keys %{$wordsh}) { # TODO: Replace error message and exit with return error code. if($word !~ m/\#\w+\#\d+/) { print STDERR "$word is not in WORD#POS#SENSE format!\n"; exit 1; } my @domns = $wn->queryWord($word, "domn"); foreach my $temp (@domns) { $domnHash{$temp} = 1; } } return (\%domnHash); } =item domt Returns the domain terms of a synset. Parameters: $synsHash, $ipType Returns: $domtHash =cut # function to take a set of synsets and to return their domain term # synsets. both input and output will be arrays of fully qualified # WordNet senses (in WORD#POS#SENSE format). sub domt { my $self = shift; my $wn = $self->{wn}; my ($wordsh, $outprep) = @_; my %domtHash = (); return (1, 1) if(defined($outprep)); foreach my $word (keys %{$wordsh}) { # TODO: Replace error message and exit with return error code. if($word != m/\#\w+\#\d+/) { print STDERR "$word is not in WORD#POS#SENSE format!\n"; exit 1; } my @domts = $wn->queryWord ($word, "domt"); foreach my $temp (@domts) { $domtHash{$temp} = 1; } } return (\%domtHash); } =item sim Returns the similar-to synsets. Parameters: $synsHash, $ipType Returns: $simHash =cut # function to take a set of synsets and to return their similar-to # synsets. both input and output will be arrays of fully qualified # WordNet senses (in WORD#POS#SENSE format). sub sim { my $self = shift; my $wn = $self->{'wn'}; my ($synsetsh, $outprep) = @_; my %simHash = (); # check if this is a request for the input-output types of this # function return (1, 1) if(defined($outprep)); foreach my $syns (keys %{$synsetsh}) { # check if in word-pos-sense format # TODO: Replace error message and exit with return error code. if($syns !~ /\#\w\#\d+/) { print STDERR "$syns is not in WORD\#POS\#SENSE format!\n"; exit; } # get the sim synsets my @sims = $wn->querySense($syns, "sim"); # put the synsets in a hash. this way we will avoid multiple # copies of the same synset my $temp; foreach $temp (@sims) { $simHash{$temp} = 1; } } # return the synsets in an hash ref return(\%simHash); } =item enta Returns the entailment of a synset. Parameters: $synsHash, $ipType Returns: $entaHash =cut # function to take a set of synsets and to return their entailment # synsets. both input and output will be arrays of fully qualified # WordNet senses (in WORD#POS#SENSE format). sub enta { my $self = shift; my $wn = $self->{'wn'}; my ($synsetsh, $outprep) = @_; my %entailsHash = (); # check if this is a request for the input-output types of this # function return (1, 1) if(defined($outprep)); foreach my $syns (keys %{$synsetsh}) { # check if in word-pos-sense format # TODO: Replace error message and exit with return error code. if($syns !~ /\#\w\#\d+/) { print STDERR "$syns is not in WORD\#POS\#SENSE format!\n"; exit; } # get the entails synsets my @entails = $wn->querySense($syns, "enta"); # put the entails synsets in a hash. this way we will avoid # multiple copies of the same entails synset my $temp; foreach $temp (@entails) { $entailsHash{$temp} = 1; } } # return the causs in an hash ref return(\%entailsHash); } =item caus Returns the cause of a synset. Parameters: $synsHash, $ipType Returns: $causHash =cut # function to take a set of synsets and to return their cause # synsets. both input and output will be arrays of fully qualified # WordNet senses (in WORD#POS#SENSE format). sub caus { my $self = shift; my $wn = $self->{'wn'}; my ($synsetsh, $outprep) = @_; my %causeHash = (); # check if this is a request for the input-output types of this # function return(1, 1) if(defined($outprep)); foreach my $syns (keys %{$synsetsh}) { # check if in word-pos-sense format # TODO: Replace error message and exit with return error code. if($syns !~ /\#\w\#\d+/) { print STDERR "$syns is not in WORD\#POS\#SENSE format!\n"; exit; } # get the cause synsets my @cause = $wn->querySense($syns, "caus"); # put the cause synsets in a hash. this way we will avoid # multiple copies of the same cause synset my $temp; foreach $temp (@cause) { $causeHash{$temp} = 1; } } # return the causs in an hash ref return(\%causeHash); } =item part Returns the participles of a synset. Parameters: $synsHash, $ipType Returns: $partHash =cut # function to take a set of synsets and to return their participle # synsets. both input and output will be arrays of fully qualified # WordNet senses (in WORD#POS#SENSE format). sub part { my $self = shift; my $wn = $self->{'wn'}; my ($synsetsh, $outprep) = @_; my %partHash = (); # check if this is a request for the input-output types of this # function return(1, 1) if(defined($outprep)); foreach my $syns (keys %{$synsetsh}) { # check if in word-pos-sense format # TODO: Replace error message and exit with return error code. if($syns !~ /\#\w\#\d+/) { print STDERR "$syns is not in WORD\#POS\#SENSE format!\n"; exit; } # get the part synsets my @part = $wn->queryWord($syns, "part"); # put the part synsets in a hash. this way we will avoid # multiple copies of the same part synset my $temp; foreach $temp (@part) { $partHash{$temp} = 1; } } # return the causs in an hash ref return(\%partHash); } =item pert Returns the pertainyms of a synset. Parameters: $synsHash, $ipType Returns: $pertHash =cut # function to take a set of synsets and to return their pertainym # synsets. both input and output will be arrays of fully qualified # WordNet senses (in WORD#POS#SENSE format). sub pert { my $self = shift; my $wn = $self->{'wn'}; my ($synsetsh, $outprep) = @_; my %pertHash = (); # check if this is a request for the input-output types of this # function return (1, 1) if(defined($outprep)); foreach my $syns (keys %{$synsetsh}) { # check if in word-pos-sense format # TODO: Replace error message and exit with return error code. if($syns !~ /\#\w\#\d+/) { print STDERR "$syns is not in WORD\#POS\#SENSE format!\n"; exit; } # get the pert synsets my @pert = $wn->queryWord($syns, "pert"); # put the pert synsets in a hash. this way we will avoid # multiple copies of the same pert synset my $temp; foreach $temp (@pert) { $pertHash{$temp} = 1; } } # return the causs in an hash ref return(\%pertHash); } =item glos Returns the gloss of a synset. Parameters: $synsHash, $ipType Returns: $glossString =cut # function to take a set of synsets and to return the concatenation of # their glosses sub glos { my $self = shift; my $wn = $self->{'wn'}; my $stemmer = $self->{'stemmer'}; my ($synsetsh, $outprep) = @_; my $returnString = ""; # check if this is a request for the input-output types of this # function return (1, 2) if(defined($outprep)); my @synshkeys = keys %{$synsetsh}; my $i = 0; foreach my $syns (@synshkeys) { # check if in word-pos-sense format # TODO: Replace error message and exit with return error code. if($syns !~ /\#\w\#\d+/) { print STDERR "$syns is not in WORD\#POS\#SENSE format!\n"; exit; } # get the glos my $glosString; ($glosString) = $wn->querySense($syns, "glos"); # regularize the glos $glosString =~ s/\".*//; # get rid of most punctuation $glosString =~ tr/.;:,?!(){}\x22\x60\x24\x25\x40<>/ /; # get rid of apostrophes not surrounded by word chars $glosString =~ s/(?stemString($glosString, 1) if($self->{stem}); $glosString =~ s/^\s*/ /; $glosString =~ s/\s*$/ /; # append to return string $returnString .= $glosString; # put in boundary if more glosses coming! if($i < $#synshkeys) { my $boundary = sprintf("GGG%05dGGG", $self->{'glosBoundaryIndex'}); $returnString .= $boundary; ($self->{'glosBoundaryIndex'})++; } $i++; } # and we are done! return($returnString); } =item example Returns the example of a synset. Parameters: $synsHash, $ipType Returns: $example =cut # function to take a set of synsets and to return the concatenation of # their example strings sub example { my $self = shift; my $wn = $self->{'wn'}; my $stemmer = $self->{'stemmer'}; my ($synsetsh, $outprep) = @_; my @exampleStrings = (); # check if this is a request for the input-output types of this # function return (1, 2) if(defined($outprep)); # first get all the example strings into an array foreach my $syns (keys %{$synsetsh}) { # check if in word-pos-sense format # TODO: Replace error message and exit with return error code. if($syns !~ /\#\w\#\d+/) { print STDERR "$syns is not in WORD\#POS\#SENSE format!\n"; exit; } # get the glos my $exampleString; ($exampleString) = $wn->querySense($syns, "glos"); # check if this has any examples if($exampleString !~ /\"/) {next;} while($exampleString =~ /\"([^\"]*)\"/g) { push @exampleStrings, $1; } } # now put the example strings together to form the return # string. separate examples with the example boundary my $returnString = ""; my $i; for ($i = 0; $i <= $#exampleStrings; $i++) { # preprocess ### # get rid of most punctuation $exampleStrings[$i] =~ tr/.;:,?!(){}\x22\x60\x24\x25\x40<>/ /; # get rid of apostrophes not surrounded by word chars $exampleStrings[$i] =~ s/(?stemString($exampleStrings[$i], 1) if($self->{'stem'}); $exampleStrings[$i] =~ s/^\s*/ /; $exampleStrings[$i] =~ s/\s*$/ /; # append to $returnString $returnString .= $exampleStrings[$i]; # put in boundary if more examples coming! if($i < $#exampleStrings) { my $boundary = sprintf("EEE%05dEEE", $self->{'exampleBoundaryIndex'}); $returnString .= $boundary; ($self->{'exampleBoundaryIndex'})++; } } # and we are done! return($returnString); } =item syns Returns the words in the synset. Parameters: $synsHash, $ipType Returns: $wordString =cut # function to take a set of synsets and to return the concatenation of # all the words in them. repeated words are returned only once. sub syns { my $self = shift; my $wn = $self->{'wn'}; my ($synsetsh, $outprep) = @_; my $returnString = ""; # check if this is a request for the input-output types of this # function return (1, 2) if(defined($outprep)); my %synonymHash = (); foreach my $syns (keys %{$synsetsh}) { # check if in word-pos-sense format # TODO: Replace error message and exit with return error code. if($syns !~ /\#\w\#\d+/) { print STDERR "$syns is not in WORD\#POS\#SENSE format!\n"; exit; } # get the words my @synsetWords = $wn->querySense($syns, "syns"); # for each word, remove the POS and SENSE, and put only the # word in a hash my $temp; foreach $temp (@synsetWords) { $temp =~ s/\#.*//; $synonymHash{$temp} = 1; } } # now get hold of all the words in sorted order my @synonymArray = sort keys %synonymHash; # concatenate them, using the synonym boundary for(my $i = 0; $i <= $#synonymArray; $i++) { $synonymArray[$i] =~ s/ /_/g; $returnString .= " $synonymArray[$i] "; # put in boundary if more examples coming! if($i < $#synonymArray) { my $boundary = sprintf("SSS%05dSSS", $self->{synonymBoundaryIndex}); $returnString .= $boundary; ($self->{synonymBoundaryIndex})++; } } # and we are done! return($returnString); } =item glosexample Returns the gloss and example of a synset. Parameters: $synsHash, $ipType Returns: $glosExampleString =back =cut # function to take a set of synsets and to return the concatenation of # their glosses (including the examples) sub glosexample { my $self = shift; my $wn = $self->{wn}; my $stemmer = $self->{stemmer}; my ($synsetsh, $outprep) = @_; my $returnString = ""; # check if this is a request for the input-output types of this # function return (1, 2) if(defined($outprep)); my @synshkeys = keys %{$synsetsh}; for(my $i = 0; $i < scalar(@synshkeys); $i++) { # check if in word-pos-sense format # TODO: Replace error message and exit with return error code. if($synshkeys[$i] !~ /\#\w\#\d+/) { print STDERR "$synshkeys[$i] is not in WORD\#POS\#SENSE format!\n"; exit; } # get the glos my $glosString; ($glosString) = $wn->querySense($synshkeys[$i], "glos"); # regularize the glos ###$glosString =~ s/\'//g; ###$glosString =~ s/[^\w]/ /g; # get rid of most punctuation $glosString =~ tr/.;:,?!(){}\x22\x60\x24\x25\x40<>/ /; # get rid of apostrophes not surrounded by word chars $glosString =~ s/(?stemString($glosString, 1) if($self->{stem}); $glosString =~ s/^\s*/ /; $glosString =~ s/\s*$/ /; # append to return string $returnString .= $glosString; # put in boundary if more glosses coming! if($i < $#synshkeys) { my $boundary = sprintf("XXX%05dXXX", $self->{glosBoundaryIndex}); $returnString .= $boundary; ($self->{glosBoundaryIndex})++; } } # and we are done! return($returnString); } 1; __END__ =head1 SEE ALSO perl(1), WordNet::Similarity(3), WordNet::QueryData(3) http://www.cs.utah.edu/~sidd http://wordnet.princeton.edu http://www.ai.mit.edu/~jrennie/WordNet http://groups.yahoo.com/group/wn-similarity =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu =head1 BUGS None. To report bugs, go to http://groups.yahoo.com/group/wn-similarity/ or e-mail "S". =head1 COPYRIGHT AND LICENSE Copyright (c) 2005, Ted Pedersen and Satanjeev Banerjee This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/lib/WordNet/vector-relation.dat0000644000175000017500000000013011015336622021216 0ustar sidsidVectorRelationFile also attr example glosexample glos holo hype hypo mero pert sim syns WordNet-Similarity-2.05/lib/WordNet/lesk-relation.dat0000644000175000017500000000157511015336622020670 0ustar sidsidRelationFile also-also also-attr also-glos also-holo also-hype also-hypo also-mero also-pert also-sim attr-also attr-attr attr-glos attr-holo attr-hype attr-hypo attr-mero attr-pert attr-sim example-example example-glos example-syns glos-also glos-attr glos-example glos-glos glos-holo glos-hype glos-hypo glos-mero glos-pert glos-sim glos-syns holo-also holo-attr holo-glos holo-holo holo-hype holo-hypo holo-mero holo-pert holo-sim hype-also hype-attr hype-glos hype-holo hype-hype hype-hypo hype-mero hype-pert hype-sim hypo-also hypo-attr hypo-glos hypo-holo hypo-hype hypo-hypo hypo-mero hypo-pert hypo-sim mero-also mero-attr mero-glos mero-holo mero-hype mero-hypo mero-mero mero-pert mero-sim pert-also pert-attr pert-glos pert-holo pert-hype pert-hypo pert-mero pert-pert pert-sim sim-also sim-attr sim-glos sim-holo sim-hype sim-hypo sim-mero sim-pert sim-sim syns-example syns-glos WordNet-Similarity-2.05/lib/WordNet/stem.pm0000644000175000017500000001456411015336622016735 0ustar sidsid# WordNet::stem.pm version 2.04 # (Last updated $Id: stem.pm,v 1.1 2008/03/27 05:13:01 sidz1979 Exp $) # # Package used by WordNet::Similarity::lesk module that # computes semantic relatedness of word senses in WordNet # using gloss overlaps. # # Copyright (c) 2005, # # Ted Pedersen, University of Minnesota Duluth # tpederse at d.umn.edu # # Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh # banerjee+ at cs.cmu.edu # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to # # The Free Software Foundation, Inc., # 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # # ------------------------------------------------------------------ package WordNet::stem; =head1 NAME WordNet::stem - Module that find the stem of a word or the stems of a string of words, using WordNet. =head1 SYNOPSIS use WordNet::stem; my $wn = WordNet::QueryData->new(); my $stemmer = WordNet::stem->new($wn) my @stems = $stemmer->stemWord($word); my $string = $stemmer->stemString($inString, $cache); =head1 DESCRIPTION This module uses the internal stemming algorithm of WordNet to stem words and strings of words. This module is used by the lesk measure of the WordNet::Similarity package. =head2 Methods =over =cut use strict; use Exporter; use vars qw($VERSION @ISA @EXPORT @EXPORT_OK %EXPORT_TAGS); @ISA = qw(Exporter); %EXPORT_TAGS = (); @EXPORT_OK = (); @EXPORT = (); $VERSION = '2.04'; =item new Creates a new stemmer object and initilizes it with a WordNet::QueryData object. Parameters: $wn Returns: $stemmer =cut # function to create the stemmer object sub new { my $className = shift; my $wn = shift; my $self = {}; $self->{wn} = $wn; $self->{wordStemHash} = (); $self->{stringStemHash} = (); bless($self, $className); return $self; } =item stemString Takes a string of words as input and returns a string of stemmed words. Parameters: $inString Returns: $retString =cut # Function to take a string, and process it in such a way that all the # words in it get stemmed. Note that if a single word has two or more # possible stems, we return the original surface form since there is # no way to select from the competing stems. The stem of the string # can be cached if requested. Useful if the calling function knows # which strings it will have to stem over and over again. Strings that # will be only stemmed ones need not be cached - thereby saving space. sub stemString { my $self = shift; my $inputString = shift; my $cache = shift; # whether or not this string has been requested for cacheing, # check in the cache return $self->{'stringStemHash'}->{$inputString} if (defined $self->{'stringStemHash'}->{$inputString}); # Not in cache. Stem. # for each word in the input get the stem and put in the output string my $outputString = ""; while ($inputString =~ /(\w+)/g) { my $word = $1; my @stems = $self->stemWord($word); # if multiple or no stems, use surface form. $outputString .= ($#stems != 0) ? "$word " : "$stems[0] "; } # if cache required, do so $self->{'stringStemHash'}->{$inputString} = $outputString if (defined($cache)); # return the string return($outputString); } =item stemWord Takes a word as input and returns its stems. A word may have more than one stem. All are returned. Parameters: $word Returns: @stems =back =cut # stem the word passed to this function and return an array of words # that contain all the possible stems of this word. All possible stems # of the word may include the surface form too if its a valid WordNet # lemma. sub stemWord { my $self = shift; my $word = shift; my $wn = $self->{wn}; my @stems = (); # if not in the cache, create and put in cache if (!defined $self->{wordStemHash}->{$word}) { # So not in the hash. gotta check for all possible parts of speech. my %stems = (); my $possiblePartsOfSpeech = "nvar"; my $pos; while ("nvar" =~ /(.)/g) { foreach ($wn->validForms("$word\#$1")) { # put underscore for space $_ =~ s/ /_/g; # remove part of speech if any $_ =~ s/\#\w$//; # put in stems hash (the hash allows us to not worry about # multiple copies of the same stem!) $stems{$_} = 1; } } # put in the cache $self->{wordStemHash}->{$word} = join(" ", (keys %stems)); } # return the stems return (split / /, $self->{wordStemHash}->{$word}); } 1; __END__ =head1 SEE ALSO perl(1), WordNet::Similarity(3), WordNet::QueryData(3) http://www.cs.utah.edu/~sidd http://wordnet.princeton.edu http://www.ai.mit.edu/~jrennie/WordNet http://groups.yahoo.com/group/wn-similarity =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu =head1 BUGS None. To report bugs, go to http://groups.yahoo.com/group/wn-similarity/ or e-mail "S". =head1 COPYRIGHT AND LICENSE Copyright (c) 2005, Ted Pedersen and Satanjeev Banerjee This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut WordNet-Similarity-2.05/build/0000755000175000017500000000000011025552366014372 5ustar sidsidWordNet-Similarity-2.05/build/Infocontent.PL0000644000175000017500000000163411015336622017113 0ustar sidsid# This script generates default infocontent files for WordNet::Similarity # (Last updated $Id: Infocontent.PL,v 1.2 2008/03/07 22:06:49 sidz1979 Exp $) # # ---------------------------------------------------------------------------- use File::Spec; # Set up @ARGV print STDERR "Generating default information content files.\n"; my $fname = shift; die "Undefined information content file specified.\n" if(!defined($fname)); @ARGV = ("--outfile=$fname"); if(open(WNPATH, "build/wnpath.txt")) { my $wndictpath = ; $wndictpath =~ s/[\r\f\n]+//g; $wndictpath =~ s/^\s+//; $wndictpath =~ s/\s+$//; $wndictpath = File::Spec->catfile($wndictpath, 'dict'); push(@ARGV, "--wnpath=$wndictpath"); close(WNPATH); } # Call the semCorFreq command require "utils/semCorFreq.pl"; # Check if the file was generated if(-z $fname) { print STDERR "Error: Failed to create default infocontent file.\n"; exit(1); } WordNet-Similarity-2.05/build/Depthfiles.PL0000644000175000017500000000177411015336622016721 0ustar sidsid# This script generates default depth files for WordNet::Similarity # (Last updated $Id: Depthfiles.PL,v 1.2 2008/03/07 22:06:49 sidz1979 Exp $) # # ---------------------------------------------------------------------------- use File::Spec; # Set up @ARGV print STDERR "Generating default depth files.\n"; my $synsetsfile = shift; die "Undefined synset depths file.\n" if(!defined($synsetsfile)); my $taxonomyfile = $synsetsfile; $taxonomyfile =~ s/[a-zA-Z_\.]+$/treedepths.dat/; @ARGV = ("--outfile=$taxonomyfile", "--depthfile=$synsetsfile"); if(open(WNPATH, "build/wnpath.txt")) { my $wndictpath = ; $wndictpath =~ s/[\r\f\n]+//g; $wndictpath =~ s/^\s+//; $wndictpath =~ s/\s+$//; $wndictpath = File::Spec->catfile($wndictpath, 'dict'); push(@ARGV, "--wnpath=$wndictpath"); close(WNPATH); } # Call the wnDepths command require "utils/wnDepths.pl"; # Check if it worked if(-z $synsetsfile or -z $taxonomyfile) { print STDERR "Error: failed to create WordNet depths files.\n"; exit(1); } WordNet-Similarity-2.05/SIGNATURE0000644000175000017500000010013711025550324014551 0ustar sidsidThis file contains message digests of all files listed in MANIFEST, signed via the Module::Signature module, version 0.55. 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8d39dee595ab01c57a97f5b65ecc5e95c21eab77 web/index.html -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.6 (GNU/Linux) iD8DBQFIVtDUperucdg8sVQRAtuJAJ4kV0kJZg3y1dSjhfwuoJZ5hWqYnQCeNgUm gc0vjB8usO/dNNuoZL8f2H4= =2dY/ -----END PGP SIGNATURE----- WordNet-Similarity-2.05/MANIFEST0000644000175000017500000002564611020104076014423 0ustar sidsidbuild/Depthfiles.PL build/Infocontent.PL CHANGES doc/changelog.pod doc/config.pod doc/developers.pod doc/install.pod doc/intro.pod doc/modules.pod doc/todo.pod doc/utils.pod FDL.txt GPL.txt INSTALL lib/WordNet/get_wn_info.pm lib/WordNet/stem.pm lib/WordNet/vectorFile.pm lib/WordNet/lesk-relation.dat lib/WordNet/Similarity.pm lib/WordNet/Tools.pm lib/WordNet/Similarity/DepthFinder.pm lib/WordNet/Similarity/FrequencyCounter.pm lib/WordNet/Similarity/GlossFinder.pm lib/WordNet/Similarity/hso.pm lib/WordNet/Similarity/ICFinder.pm lib/WordNet/Similarity/jcn.pm lib/WordNet/Similarity/lch.pm lib/WordNet/Similarity/lesk.pm lib/WordNet/Similarity/lin.pm lib/WordNet/Similarity/path.pm lib/WordNet/Similarity/PathFinder.pm lib/WordNet/Similarity/random.pm lib/WordNet/Similarity/res.pm lib/WordNet/Similarity/vector.pm lib/WordNet/Similarity/vector_pairs.pm lib/WordNet/Similarity/wup.pm lib/WordNet/vector-pairs-relation.dat lib/WordNet/vector-relation.dat lib/WordNet/wordvectors.dat Makefile.PL MANIFEST README samples/config-files/config-all.conf samples/config-files/config-hso.conf samples/config-files/config-jcn.conf samples/config-files/config-lch.conf samples/config-files/config-lesk.conf samples/config-files/config-lin.conf samples/config-files/config-path.conf samples/config-files/config-random.conf samples/config-files/config-res.conf samples/config-files/config-vector.conf samples/config-files/config-vector_pairs.conf samples/config-files/config-wup.conf samples/lesk-relation.dat samples/millercharles.txt samples/README samples/resnikdiab.txt samples/sample.pl samples/stoplist.txt samples/vector-pairs-relation.dat samples/vector-relation.dat samples/wn30compounds.txt SIGNATURE t/access.t t/config/all.t t/config/invalid.t t/config/lesk.t t/config/random.t t/config/rootnode.t t/hso.t t/ICFinder.t t/jcn.t t/keys/0.06/edgepairs.key t/keys/0.06/edgetrace.key t/keys/0.06/hsopairs.key t/keys/0.06/hsotrace.key t/keys/0.06/jcnpairs.key t/keys/0.06/jcntrace.key t/keys/0.06/lchpairs.key t/keys/0.06/lchtrace.key t/keys/0.06/leskpairs.key t/keys/0.06/lesktrace.key t/keys/0.06/linpairs.key t/keys/0.06/lintrace.key t/keys/0.06/respairs.key t/keys/0.06/restrace.key t/keys/0.06/wuppairs.key t/keys/0.06/wuppairs_nr.key t/keys/0.06/wuptrace.key t/keys/0.07/hsopairs.key t/keys/0.07/hsopairs_nr.key t/keys/0.07/hsotrace.key t/keys/0.07/jcnpairs.key t/keys/0.07/jcnpairs_nr.key t/keys/0.07/jcntrace.key t/keys/0.07/lchpairs.key t/keys/0.07/lchpairs_nr.key t/keys/0.07/lchtrace.key t/keys/0.07/leskpairs.key t/keys/0.07/leskpairs_nr.key t/keys/0.07/lesktrace.key t/keys/0.07/linpairs.key t/keys/0.07/linpairs_nr.key 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t/keys/0.15/lchpairs_nr.key t/keys/0.15/lchtrace.key t/keys/0.15/leskpairs.key t/keys/0.15/leskpairs_nr.key t/keys/0.15/lesktrace.key t/keys/0.15/linpairs.key t/keys/0.15/linpairs_nr.key t/keys/0.15/lintrace.key t/keys/0.15/pathpairs.key t/keys/0.15/pathpairs_nr.key t/keys/0.15/pathtrace.key t/keys/0.15/respairs.key t/keys/0.15/respairs_nr.key t/keys/0.15/restrace.key t/keys/0.15/wuppairs.key t/keys/0.15/wuppairs_nr.key t/keys/0.15/wuptrace.key t/keys/0.16/hsopairs.key t/keys/0.16/hsopairs_nr.key t/keys/0.16/hsotrace.key t/keys/0.16/jcnpairs.key t/keys/0.16/jcnpairs_nr.key t/keys/0.16/jcntrace.key t/keys/0.16/lchpairs.key t/keys/0.16/lchpairs_nr.key t/keys/0.16/lchtrace.key t/keys/0.16/leskpairs.key t/keys/0.16/leskpairs_nr.key t/keys/0.16/lesktrace.key t/keys/0.16/linpairs.key t/keys/0.16/linpairs_nr.key t/keys/0.16/lintrace.key t/keys/0.16/pathpairs.key t/keys/0.16/pathpairs_nr.key t/keys/0.16/pathtrace.key t/keys/0.16/respairs.key t/keys/0.16/respairs_nr.key t/keys/0.16/restrace.key t/keys/0.16/wndepths.txt t/keys/0.16/wuppairs.key t/keys/0.16/wuppairs_nr.key t/keys/0.16/wuptrace.key t/keys/1.04/hsopairs.key t/keys/1.04/hsopairs_nr.key t/keys/1.04/hsotrace.key t/keys/1.04/jcnpairs.key t/keys/1.04/jcnpairs_nr.key t/keys/1.04/jcntrace.key t/keys/1.04/lchpairs.key t/keys/1.04/lchpairs_nr.key t/keys/1.04/lchtrace.key t/keys/1.04/leskpairs.key t/keys/1.04/leskpairs_nr.key t/keys/1.04/lesktrace.key t/keys/1.04/linpairs.key t/keys/1.04/linpairs_nr.key t/keys/1.04/lintrace.key t/keys/1.04/pathpairs.key t/keys/1.04/pathpairs_nr.key t/keys/1.04/pathtrace.key t/keys/1.04/respairs.key t/keys/1.04/respairs_nr.key t/keys/1.04/restrace.key t/keys/1.04/wndepths.txt t/keys/1.04/wuppairs.key t/keys/1.04/wuppairs_nr.key t/keys/1.04/wuptrace.key t/keys/diff/hsopairs.diff t/keys/diff/jcnpairs.diff t/keys/diff/lchpairs.diff t/keys/diff/leskpairs.diff t/keys/diff/linpairs.diff t/keys/diff/pathpairs.diff t/keys/diff/respairs.diff t/keys/diff/wuppairs.diff t/keys/diff/wuppairs_nr.diff t/keys/hsopairs.key t/keys/hsopairs_nr.key t/keys/hsotrace.key t/keys/jcnpairs.key t/keys/jcnpairs_nr.key t/keys/jcntrace.key t/keys/lchpairs.key t/keys/lchpairs_nr.key t/keys/lchtrace.key t/keys/leskpairs.key t/keys/leskpairs_nr.key t/keys/lesktrace.key t/keys/linpairs.key t/keys/linpairs_nr.key t/keys/lintrace.key t/keys/pathpairs.key t/keys/pathpairs_nr.key t/keys/pathtrace.key t/keys/respairs.key t/keys/respairs_nr.key t/keys/restrace.key t/keys/wndepths.txt t/keys/wnver.key t/keys/wuppairs.key t/keys/wuppairs_nr.key t/keys/wuptrace.key t/lch.t t/lesk.t t/lin.t t/offsetwps.t t/pairs-noroot.t t/pairs.t t/pairs.txt t/path.t t/PathFinder.t t/pod.t t/pod-doc.t t/pod-coverage.t t/random.t t/README t/res.t t/tests/edge.txt t/tests/hso.txt t/tests/jcn.txt t/tests/lch.txt t/tests/lesk.txt t/tests/lin.txt t/tests/random.txt t/tests/res.txt t/tests/test-modules.pl t/tests/test-utils.pl t/tests/vector.txt t/tests/wup.txt t/trace.t t/utils/compounds.t t/utils/rawtextFreq.t t/utils/sim-file.t t/utils/sim.t t/utils/wnDepths.t t/vector.t t/vector_pairs.t t/wn30loop.t t/wup.t utils/BNCFreq.pl utils/brownFreq.pl utils/compounds.pl utils/rankFormat.pl utils/rawtextFreq.pl utils/semCorFreq.pl utils/semCorRawFreq.pl utils/similarity.pl utils/similarity_server.pl utils/treebankFreq.pl utils/wnDepths.pl utils/wordVectors.pl web/cgi-bin/similarity.cgi web/cgi-bin/wps.cgi web/docs/explanations.html web/docs/index.html web/docs/instructions.html web/docs/logo_black.gif web/docs/measures.html web/docs/rootnode.html web/docs/sim-style.css web/index.html web/README META.yml WordNet-Similarity-2.05/FDL.txt0000644000175000017500000004767311015336623014455 0ustar sidsid GNU Free Documentation License Version 1.2, November 2002 Copyright (C) 2000,2001,2002 Free Software Foundation, Inc. 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. 0. PREAMBLE The purpose of this License is to make a manual, textbook, or other functional and useful document "free" in the sense of freedom: to assure everyone the effective freedom to copy and redistribute it, with or without modifying it, either commercially or noncommercially. Secondarily, this License preserves for the author and publisher a way to get credit for their work, while not being considered responsible for modifications made by others. This License is a kind of "copyleft", which means that derivative works of the document must themselves be free in the same sense. It complements the GNU General Public License, which is a copyleft license designed for free software. We have designed this License in order to use it for manuals for free software, because free software needs free documentation: a free program should come with manuals providing the same freedoms that the software does. 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A copy of the license is included in the section entitled "GNU Free Documentation License". If you have Invariant Sections, Front-Cover Texts and Back-Cover Texts, replace the "with...Texts." line with this: with the Invariant Sections being LIST THEIR TITLES, with the Front-Cover Texts being LIST, and with the Back-Cover Texts being LIST. If you have Invariant Sections without Cover Texts, or some other combination of the three, merge those two alternatives to suit the situation. If your document contains nontrivial examples of program code, we recommend releasing these examples in parallel under your choice of free software license, such as the GNU General Public License, to permit their use in free software. WordNet-Similarity-2.05/META.yml0000644000175000017500000000143211017747705014550 0ustar sidsid--- #YAML:1.0 name: WordNet-Similarity version: 2.05 abstract: WordNet-based measures of semantic relatedness. license: gpl generated_by: ExtUtils::MakeMaker version 6.36 distribution_type: module requires: CGI: 0 Digest::SHA1: 0 File::Find: 0 File::Spec: 0 File::Temp: 0 Getopt::Long: 0 IO: 0 Text::Similarity: 0.02 WordNet::QueryData: 1.40 meta-spec: url: http://module-build.sourceforge.net/META-spec-v1.2.html version: 1.2 author: - Ted Pedersen, Siddharth Patwardhan, Satanjeev Banerjee, Jason Michelizzi WordNet-Similarity-2.05/web/0000755000175000017500000000000011025552366014050 5ustar sidsidWordNet-Similarity-2.05/web/index.html0000644000175000017500000000075211015336625016046 0ustar sidsid Empty

You proabably were looking for the web interface for WordNet::Similarity.

WordNet-Similarity-2.05/web/cgi-bin/0000755000175000017500000000000011025552366015360 5ustar sidsidWordNet-Similarity-2.05/web/cgi-bin/wps.cgi0000755000175000017500000000446511017747562016676 0ustar sidsid#!/usr/bin/perl -wT use strict; use CGI; use Socket; my $remote_host = '127.0.0.1'; my $remote_port = 31134; my $doc_base = '../../similarity'; my $cgi = CGI->new; print $cgi->header; my $wps = $cgi->param ('wps') || 'undefined word'; print <<"EOB"; Gloss for $wps EOB unless ($wps =~ /[^#]+#[nvars]#\d+/) { print "

Error: bad input word: $wps

\n"; goto SHOW_END; } # connect to Similarity server socket (Server, PF_INET, SOCK_STREAM, getprotobyname ('tcp')); my $internet_addr = inet_aton ($remote_host) or do { print "

Could not convert $remote_host to an IP address: $!

\n"; goto SHOW_END; }; my $paddr = sockaddr_in ($remote_port, $internet_addr); unless (connect (Server, $paddr)) { print "

Cannot connect to server $remote_host:$remote_port ($!)

\n"; goto SHOW_END; } select ((select (Server), $|=1)[0]); print Server "g $wps\015\012"; print Server "\015\012"; while (my $line = ) { last if $line eq "\015\012"; my ($type, $str) = $line =~ m/^(\S+) (.+)/; if ($type eq 'g' or $type eq '1') { my ($wps, $gloss) = $str =~ m/([^#]+#[nvar]#\d+) (.*)/; print "
$wps
$gloss
\n"; } elsif ($type eq '!') { print "

$str

\n"; } else { print "

Error: odd message from server: ($type) $str

\n"; } } SHOW_END: close Server; print <<'EOH'; EOH __END__ =head1 NAME wps.cgi =head1 DESCRIPTION This script takes one parameter 'wps', which is a valid word#pos#sense string and produces a web page that displays the gloss of the synset to which that word sense belongs. =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Jason Michelizzi =head1 BUGS None known. =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen and Jason Michelizzi This program is free software; you may redistribute and/or modify it under the terms of the GNU General Public License, version 2 or, at your option, any later version. =cut WordNet-Similarity-2.05/web/cgi-bin/similarity.cgi0000755000175000017500000005152011017747562020245 0ustar sidsid#!/usr/bin/perl -wT use strict; # where do we connect to the Similarity server? Here: my $remote_host = '127.0.0.1'; my $remote_port = '31134'; my $doc_base = '../../similarity'; use CGI; use Socket; BEGIN { # Our University's webserver uses an ancient version of CGI::Carp # so we can't do fatalsToBrowser. # The carpout() function lets us modify the format of messages sent to # a filehandle (in this case STDERR) to include timestamps use CGI::Carp 'carpout'; carpout(*STDOUT); } # subroutine prototypes sub showForm ($$$$); sub round ($); my $cgi = CGI->new; # These are the colors of the text when we alternate text colors (when # showing errors, for example). my $text_color1 = 'black'; my $text_color2 = '#d03000'; # Mapping from hash-code to version my %versionMap = ('eOS9lXC6GvMWznF1wkZofDdtbBU' => '3.0', 'LL1BZMsWkr0YOuiewfbiL656+Q4' => '2.1'); # print the HTTP header print $cgi->header; # if the showform parameter is no, then don't show the form--this is how # we avoid showing the form in popups my $showform = $cgi->param ('showform') || 'yes'; # show the start of the page (all the usual HTML that goes at the top # of a page, etc.) showPageStart (); # check if we want to show the version information (version of WordNet, etc.) my $showversion = $cgi->param ('version'); if ($showversion) { socket (Server, PF_INET, SOCK_STREAM, getprotobyname ('tcp')); my $internet_addr = inet_aton ($remote_host) or die "Could not convert $remote_host to an Internet addr: $!\n"; my $paddr = sockaddr_in ($remote_port, $internet_addr); unless (connect (Server, $paddr)) { print "

Cannot connect to server $remote_host:$remote_port

\n"; goto SHOW_END; } select ((select (Server), $|=1)[0]); print Server "v\015\012\015\012"; print "

Version information

\n"; while (my $line = ) { last if $line eq "\015\012"; if ($line =~ /^v (\S+) (\S+)/) { if($1 eq "WordNet") { my $verstring = $versionMap{$2}; print "

$1 version $verstring (hash-code: $2)

\n" if(defined($verstring)); print "

$1 hash-code: $2

\n" if(!defined($verstring)); } else { print "

$1 version $2

\n"; } } elsif ($line =~ m/^! (.*)/) { print "

$1

\n"; } else { print "

Strange message from server: $line\n"; } } local $ENV{PATH} = "/usr/local/bin:/usr/bin:/bin:/sbin"; my $t_osinfo = `uname -a` || "Couldn't get system information: $!"; # $t_osinfo is tainted. Use it in a pattern match and $1 will # be untainted. $t_osinfo =~ /(.*)/; print "

HTTP server: $ENV{HTTP_HOST} ($1)

\n"; print "

Similarity server: $remote_host

\n"; goto SHOW_END; } # check if we're generating this page as the result of a query; if so, then # we need to show the results. my $word1 = $cgi->param ('word1'); my $word2 = $cgi->param ('word2'); if ($word1 and !$word2) { print "

Word 2 was not specified.

"; } elsif (!$word1 and $word2) { print "

Word 1 was not specified.

"; } elsif ($word1 and $word2) { print "
\n"; socket (Server, PF_INET, SOCK_STREAM, getprotobyname ('tcp')); my $internet_addr = inet_aton ($remote_host) or die "Could not convert $remote_host to an Internet addr: $!\n"; my $paddr = sockaddr_in ($remote_port, $internet_addr); unless (connect (Server, $paddr)) { print "

Cannot connect to server $remote_host:$remote_port

\n"; goto SHOW_END; } select ((select (Server), $|=1)[0]); # value of the parameters can be 'all', 'gloss', or 'synset' my $w1option = $cgi->param ('senses1'); my $w2option = $cgi->param ('senses2'); my $query_type = 2; if ($w1option eq 'gloss' or $w1option eq 'synset') { $query_type = 1 unless $word1 =~ m/[^#]+\#[nvars]\#\d+/; } if ($w2option eq 'gloss' or $w2option eq 'synset') { $query_type = 1 unless $word2 =~ m/[^#]+\#[nvars]\#\d+/; } if ($query_type == 1) { my @senses1; my @senses2; if ($w1option eq 'gloss') { print Server "g $word1\015\012"; } elsif ($w1option eq 'synset') { print Server "s $word1\015\012"; } elsif ($w1option eq 'all') { @senses1 = ([$word1, ""]); } else { print "
Internal error: invalid option `$w1option'
\n"; } if ($w2option eq 'gloss') { print Server "g $word2\015\012"; } elsif ($w2option eq 'synset') { print Server "s $word2\015\012"; } elsif ($w2option eq 'all') { @senses2 = ([$word2, ""]); } else { print "
Internal error: invalid option `$w2option'
\n"; } print Server "\015\012"; while (my $response = ) { last if $response eq "\015\012"; my $prefix = substr $response, 0, 1; my $end = substr $response, 2; if ($prefix eq 'g') { my ($wps, $gloss) = m/([^#]+\#[nvar]\#\d+) (.*)/; print "

$wps: $gloss

"; } elsif ($prefix eq '1') { my ($wps, $gloss) = $end =~ m/([^#]+\#[nvar]\#\d+) (.*)/; push @senses1, [$wps, $gloss]; } elsif ($prefix eq '2') { my ($wps, $gloss) = $end =~ m/([^#]+\#[nvar]\#\d+) (.*)/; push @senses2, [$wps, $gloss]; } else { print "Strange message from server `$response'"; } } my $measure = $cgi->param ('measure') || 'path'; showForm (1, \@senses1, \@senses2, $measure); showPageEnd (); exit; } else { my $measure = $cgi->param ('measure'); my $trace = $cgi->param ('trace') ? 'yes' : 'no'; my $gloss = $cgi->param ('gloss') ? 'yes' : 'no'; my $root = $cgi->param ('rootnode') ? 'yes' : 'no'; my $syns = $cgi->param ('synset') ? 'yes' : 'no'; my $all_senses = $cgi->param ('sense') ? 1 : 0; $word1 =~ tr/A-Z /a-z_/; $word2 =~ tr/A-Z /a-z_/; # terminate all messages with CRLF (best to avoid \r\n because the # meaning of \r and \n varies from platform to platform if ($measure eq 'all') { foreach my $m (qw/hso lch lesk lin jcn path res vector vector_pairs wup/) { print Server +("r $word1 $word2 $m $trace $gloss $syns $root", "\015\012"); } print Server "\015\012"; } else { print Server ("r $word1 $word2 $measure $trace $gloss $syns $root", "\015\012\015\012"); } my @glosses; my %scores; my @errors; my @synsets; my @version_info; my $lines = 0; my $last_measure = ''; while (my $response = ) { last if $response eq "\015\012"; $lines++; my $beginning = substr $response, 0, 1; my $end = substr $response, 2; if ($beginning eq '!') { $end =~ s/\s+$//; push @errors, $end; } elsif ($beginning eq 'r') { my ($measure, $wps1, $wps2, $score) = split /\s+/, $end; $score = round ($score); $last_measure = $measure; push @{$scores{$measure}}, [$score, $wps1, $wps2]; } elsif ($beginning eq 't') { $end =~ s||
|g; ### FIXME -- we lost the traces push @{$scores{$last_measure}->[-1]}, "$end\n"; } elsif ($beginning eq 'g') { my ($wps, @gloss_words) = split /\s+/, $end; push @glosses, [$wps, substr ($end, length ($wps))]; } elsif ($beginning eq 's') { my (@synset_words) = split /\s+/, $end; push @synsets, [@synset_words]; } elsif ($beginning eq 'v') { my ($package, $version) = split /\s+/, $end; push @version_info, [$package, $version]; } else { push @errors, "Error: received strange message from server `$response'"; } } my $query_string = $ENV{QUERY_STRING} || ""; # replace literal ampersands with their XML entity equivalents $query_string =~ s/&/&/g; if (scalar @version_info) { foreach my $item (@version_info) { print "

$item->[0] version $item->[1]

\n"; } goto SHOW_END; } # show errors, if any if (scalar @errors) { unless ($cgi->param ('errors') eq 'show') { my $query = $query_string . '&errors=show'; my $url = "similarity.cgi?${query}"; # Having onclick return false should keep the browser from # loading the page specified by href, but IE loads it # anyways. That's why we set href to # instead of the # URL (setting it to the URL would let non-JavaScript # browsers see the page in the main window, but such # browsers are rare) print +("

", "View errors", '

', "\n"); } else { print '

Warnings/Errors:

'; print '

'; my $parity = 0; foreach (0..$#errors) { my $color = $parity ? $text_color1 : $text_color2; print "

$errors[$_]
"; $parity = !$parity; } print "

\n"; goto SHOW_END; } } # show glosses, if any if ($gloss eq 'yes') { my $parity = 0; if (scalar @glosses) { print '

Glosses:

'; print '

'; print "

"; foreach my $ref (@glosses) { print "
$ref->[0]
$ref->[1]
"; } print "
\n"; } else { print "

Sorry, no glosses were found.

\n"; } goto SHOW_END; } else { my $query = $query_string . '&gloss=yes'; my $url = "similarity.cgi?${query}"; print +('

', "", "View glosses (definitions)", '

', "\n"); } if ($syns eq 'yes') { # show complete synsets, if any were requested if (scalar @synsets) { print '

Synsets:

'; print '

'; my $parity = 1; foreach (0..$#synsets) { my $color = $parity ? $text_color1 : $text_color2; print "

{"; print join (', ', @{$synsets[$_]}); print "}
"; $parity = !$parity; } print "

\n"; } else { print "

Sorry, no synsets were found.

\n"; } goto SHOW_END; } else { my $query = $query_string . '&synset=yes'; my $url = "similarity.cgi?${query}"; print +('

', "View synsets", '

', "\n"); } if ($all_senses) { print '

Results:

' if scalar keys %scores; print ''; print ''; print '' if $trace eq 'yes'; print "\n"; foreach my $m (keys %scores) { my @scrs = sort {$b->[0] <=> $a->[0]} @{$scores{$m}}; foreach (@scrs) { my $wps1 = $_->[1]; $wps1 =~ s/\#/%23/g; my $wps2 = $_->[2]; $wps2 =~ s/\#/%23/g; print ""; print ""; print ""; print ""; if ($trace eq 'yes') { print ""; } print "\n"; } } print "
MeasureWord 1Word 2ScoreTrace
$m$_->[1]$_->[2]$_->[0]$_->[3]
\n"; } else { my $query = $query_string; # remove from the query string options that we don't want $query =~ s/(?:&)sense=yes//; $query =~ s/(?:&)?trace=yes//; # now add the option we do want $query .= '&sense=yes'; # prepare two query strings--one without traces and one with my $url_nt = "similarity.cgi?${query}"; # 'nt' means 'no trace' my $url_trace = $url_nt . '&trace=yes'; goto SHOW_END unless scalar keys %scores; print '

Results:

'; foreach my $m (keys %scores) { my $good = $scores{$m}->[0]; foreach my $i (1..$#{$scores{$m}}) { if ($scores{$m}->[$i]->[0] > $good->[0]) { $good = $scores{$m}->[$i]; } } my $wps1 = $good->[1]; $wps1 =~ s/\#/%23/g; my $wps2 = $good->[2]; $wps2 =~ s/\#/%23/g; print +("\n

", "The relatedness of ", "$good->[1] ", "and $good->[2] ", "using $m is $good->[0].", "

\n"); if ($trace eq 'yes') { print "

$good->[3]

"; } } print +("

", "View relatedness of all senses (without traces)

\n"); print +("

", "View relatedness of all senses (with traces)

\n"); } unless ($trace eq 'yes') { my $urltrace = "similarity.cgi?${query_string}&trace=yes"; print +("

", "View traces

\n"); } SHOW_END: print "
"; close Server; } } $word1 = defined $word1 ? $word1 : ""; $word2 = defined $word2 ? $word2 : ""; my $measure = 'path';#defined $measure ? $measure : 'path'; showForm (2, $word1, $word2, $measure) unless $showform eq 'no'; showPageEnd (); exit; # ========= subroutines ========= sub round ($) { my $num = shift; my $str = sprintf ("%.4f", $num); $str =~ s/\.?0+$//; return $str; } sub showPageStart { print <<"EOINTRO"; Similarity

WordNet::Similarity

Read an overview of WordNet::Similarity.

EOINTRO } sub showForm ($$$$) { my ($type, $arg1, $arg2, $arg3) = @_; # the 'action' attribute for the HTML form below--should be the script # name my $action = 'similarity.cgi'; print <<"EOFORM1";

You may enter any two words in one of three formats:

  1. word
  2. word#part_of_speech (where part_of_speech is one of n, v, a, or r)
  3. word#part_of_speech#sense (where sense is a positive integer)

If words are entered in format 1 or 2, then the relatedness of all valid forms of the words will be computed (e.g., if 'dogs' is entered, then 'dog' will be used to compute relatedness). More instructions.

EOFORM1 # check if we are trying to get the user to type in a pair of words or # if the user needs to select senses from a option menu. if ($type == 2) { # the user needs to type in two words print <<"EOT";

EOT } else { # the user needs to select word senses from a menu print "\n"; print "
\n"; print "\n"; print "
\n"; } print '', "\n"; print '\n"; print <<"EOFORM"; About the measures

Show version info

EOFORM } sub showPageEnd { print <<'ENDOFPAGE';
Created by Ted Pedersen and Jason Michelizzi
E-mail: tpederse (at) d (dot) umn (dot) edu
ENDOFPAGE } __END__ =head1 NAME similarity.cgi - a CGI script implementing a portion of a web interface for WordNet::Similarity =head1 DESCRIPTION This script works in conjunction with similarity_server.pl and wps.cgi to provide a web interface for WordNet::Similarity. The documentation for similarity_server.pl describes how messages are passed between this script and that one. =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Jason Michelizzi =head1 BUGS None known. =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen and Jason Michelizzi This program is free software; you may redistribute and/or modify it under the terms of the GNU General Public License version 2 or, at your option, any later version. =cut WordNet-Similarity-2.05/web/docs/0000755000175000017500000000000011025552366015000 5ustar sidsidWordNet-Similarity-2.05/web/docs/index.html0000644000175000017500000000100011017445527016765 0ustar sidsid Empty

You proabably were looking for the web interface for WordNet::Similarity.

WordNet-Similarity-2.05/web/docs/sim-style.css0000644000175000017500000000212611015336625017436 0ustar sidsid/* Stylesheet for the WordNet::Similarity web interface */ /* Copyright (c) 2005, * * Ted Pedersen, University of Minnesota Duluth * tpederse at d.umn.edu * * Jason Michelizzi, University of Minnesota Duluth * mich0212 at d.umn.edu * * This is free software; you may redistribute and/or modify it under * the terms of the GNU General Public License, version 2 or, at your * option, any later version. */ body { color: black; background-color: #e0dfd9; } h1 { text-align: center; } hr { width: 90%; } a:link { color: blue; text-decoration: none; } a:active { color: purple; text-decoration: none; } a:hover { color: green; text-decoration: underline; } div.footer { font-size: smaller; } font.footer > i { text-align: center; } label.leftlabel { width: 10%; float: left; padding-left: 5px; } .results { font-family: serif; } .trace { font-family: monospace; font-size: smaller; } .gloss { font-family: monospace; font-size: smaller; } .synset { font-family: monospace; font-size: smaller; } WordNet-Similarity-2.05/web/docs/logo_black.gif0000644000175000017500000000123211015336625017556 0ustar sidsidGIF89aF/ãÌÌÌæææµµµyyy333«««fff™™™OOO‹‹‹ÿÿÿÿÿÿÿÿÿÿÿÿ!ù,F/þðÉI«½r¤Tz! &Ždy£®l[˜pŒtÚÞkÇÈ<ÌÐ(œP§ê+¡R™:ò‡¥TZ@ œ¢ëcÊb1bwŒûVä4ó ªß·—Á`„Âï¬Î¼çÆûU|„‚< „x G‰wŒ=4 6j–•–KN*•p˜Ÿ^V1” 9¢R¥&}ˆJ{ {  “,!Fš‘§7Å˥̷}NBh8ËÀÌ,àPÎFÚÛgBÜÞáàâ+äæ=b-»³ö? ùŒð<½0³EÉ®r!®±çïp0’`Xrp^Bt*‚5øPY ‰þ(ò*¢Õ õ€"Ñ '¤ð°%%<–1\N48’A¤(Pú§r=FÀÔVMŸL¡ß8z⡃øЕÐÐP‡*èÀ "kÀÌÊ®vȺbkW¨î¤2‹ÔFÙ³‹&¨­Ò“[;p‰2£[‚l ¼Ê„­˜ à€ÂMS†È‹Ä+.y' @¦Y¦% y(%‘ ÍάüÉÊŠŒ†ÑgÌéE R[ Ä‚×*bÏöaºtMŽŽgzsòxò½¬È4NýjÁêØfßþ㺠DJ‚^ì]ïàIªÇö`_ Wä›”Ÿ_Ð{1ã;ê{o[  I$ßBôZ{ä­WFeü×%âÍG`}~V{ ÞÚƒEÞxzM!‚$y¨!xÇT%hp”  @ •4µÙ@ ¦2A;WordNet-Similarity-2.05/web/docs/rootnode.html0000644000175000017500000000204211015336625017512 0ustar sidsid What's a root node?

What's a root node?

The nouns and verbs in WordNet are arranged into is-a hierarchies. There are nine noun hierarchies and several hundred verb hierarchies. By setting the root node option, the measures that use the hierarchy information will join all of the noun hierarchies together with a single node and all of the verb hierarchies together with another single node.

Using the unique root nodes changes the behavior of the measures slightly. The path-finding measures (path, lch, and wup), will now be able to find paths between synsets that would otherwise not have had paths between them.

The root node is on by default.

WordNet-Similarity-2.05/web/docs/measures.html0000644000175000017500000001774511017747563017536 0ustar sidsid Measures

Measures

Path length

A simple node-counting scheme (path). The relatedness score is inversely proportional to the number of nodes along the shortest path between the synsets. The shortest possible path occurs when the two synsets are the same, in which case the length is 1. Thus, the maximum relatedness value is 1.

Leacock & Chodorow

The relatedness measure proposed by Leacock and Chodorow (lch) is -log (length / (2 * D)), where length is the length of the shortest path between the two synsets (using node-counting) and D is the maximum depth of the taxonomy.

The fact that the lch measure takes into account the depth of the taxonomy in which the synsets are found means that the behavior of the measure is profoundly affected by the presence or absence of a unique root node. If there is a unique root node, then there are only two taxonomies: one for nouns and one for verbs. All nouns, then, will be in the same taxonomy and all verbs will be in the same taxonomy. D for the noun taxonomy will be somewhere around 18, depending upon the version of WordNet, and for verbs, it will be 14. If the root node is not being used, however, then there are nine different noun taxonomies and over 560 different verb taxonomies, each with a different value for D.

If the root node is not being used, then it is possible for synsets to belong to more than one taxonomy. For example, the synset containing turtledove#n#2 belongs to two taxonomies: one rooted at group#n#1 and one rooted at entity#n#1. In such a case, the relatedness is computed by finding the LCS that results in the shortest path between the synsets. The value of D, then, is the maximum depth of the taxonomy in which the LCS is found. If the LCS belongs to more than one taxonomy, then the taxonomy with the greatest maximum depth is selected (i.e., the largest value for D).

Wu & Palmer

The Wu & Palmer measure (wup) calculates relatedness by considering the depths of the two synsets in the WordNet taxonomies, along with the depth of the LCS. The formula is score = 2*depth(lcs) / (depth(s1) + depth(s2)). This means that 0 < score <= 1. The score can never be zero because the depth of the LCS is never zero (the depth of the root of a taxonomy is one). The score is one if the two input synsets are the same.

Resnik

The related value is equal to the information content (IC) of the Least Common Subsumer (LCS) (most informative subsumer). This means that the value will always be greater-than or equal-to zero. The upper bound on the value is generally quite large and varies depending upon the size of the corpus used to determine information content values. To be precise, the upper bound should be ln (N) where N is the number of words in the corpus.

Jiang & Conrath

The relatedness value returned by the jcn measure is equal to 1 / jcn_distance, where jcn_distance is equal to IC(synset1) + IC(synset2) - 2 * IC(lcs).

There are two special cases that need to be handled carefully when computing relatedness; both of these involve the case when jcn_distance is zero.

In the first case, we have ic(synset1) = ic(synset2) = ic(lcs) = 0. In an ideal world, this would only happen when all three concepts, viz. synset1, synset2, and lcs, are the root node. However, when a synset has a frequency count of zero, we use the value 0 for the information content. In this first case, we return 0 due to lack of data.

In the second case, we have ic(synset1) + ic(synset2) = 2 * ic(ics). This is almost always found when synset1 = synset2 = lcs (i.e., the two input synsets are the same). Intuitively this is the case of maximum relatedness, which would be infinity, but it is impossible to return infinity. Insteady we find the smallest possible distance greater than zero and return the multiplicative inverse of that distance.

Lin

The relatedness value returned by the lin measure is a number equal to 2 * IC(lcs) / (IC(synset1) + IC(synset2)). Where IC(x) is the information content of x. One can observe, then, that the relatedness value will be greater-than or equal-to zero and less-than or equal-to one.

If the information content of any of either synset1 or synset2 is zero, then zero is returned as the relatedness score, due to lack of data. Ideally, the information content of a synset would be zero only if that synset were the root node, but when the frequency of a synset is zero, we use the value of zero as the information content because of a lack of better alternatives.

Adapted Lesk (Extended Gloss Overlaps)

The Extended Gloss Overlaps measure (lesk) works by finding overlaps in the glosses of the two synsets. The relatedness score is the sum of the squares of the overlap lengths. For example, a single word overlap results in a score of 1. Two single word overlaps results in a score of 2. A two word overlap (i.e., two consecutive words) results in a score of 4. A three word overlap results in a score of 9.

Gloss Vector

The Gloss Vector measure (vector) works by forming second-order co-occurrence vectors from the glosses or WordNet definitions of concepts. The relatedness of two concepts is determined as the cosine of the angle between their gloss vectors. In order to get around the data sparsity issues presented by extremely short glosses, this measure augments the glosses of concepts with glosses of adjacent concepts as defined by WordNet relations.

Gloss Vector (pairwise)

The Gloss Vector (pairwise) measure (vector_pairs) is very similar to the "regular" Gloss Vector measure, except in the way it augments the glosses of concepts with adjacent glosses. The regular Gloss Vector measure first combines the adjacent glosses to form one large "super-gloss" and creates a single vector corresponding to each of the two concepts from the two "super-glosses". The pairwise Gloss Vector measure, on the other hand, forms separate vectors corresponding to each of the adjacent glosses (does not form a single super gloss). For example separate vectors will be created for the hyponyms, the holonyms, the meronyms, etc. of the two concepts. The measure then takes the sum of the individual cosines of the corresponding gloss vectors, i.e. the cosine of the angle between the hyponym vectors is added to the cosine of the angle between the hlonym vectors, and so on. From empirical studies, we have found that the regular Gloss Vector measure performs better than the pairwise Gloss Vector measure.

Hirst & St-Onge

This measure (hso) works by finding lexical chains linking the two word senses. There are three classes of relations that are considered: extra-strong, strong, and medium-strong. The maximum relatedness score is 16.

Random

The relatedness values are simply randomly generated numbers. This is intended only to be used as a baseline.

WordNet-Similarity-2.05/web/docs/explanations.html0000644000175000017500000000205511015336625020372 0ustar sidsid Explanations of terms used

Terms used

Gloss
A definition of a word in WordNet.
Root node
A virtual node connecting all the hierarchies together. The nouns and verbs in WordNet are arranged in hierarchies. There are nine noun hierarchies and several hundred verb hierarchies. By turning this option on, one unique root node joins all the noun hierarchies together, and another node joins all the verb hierarchies together.
Synset
Short for synonym set. A non-empty set of synonyms. E.g., {dog#n#1, domestic_dog#n#1, canis_familiaris#n#1}
WordNet-Similarity-2.05/web/docs/instructions.html0000644000175000017500000000417311015336625020434 0ustar sidsid Similarity web interface instructions

Similarity web interface instructions

Enter the words whose relatedness you wish to compute into the fields labeled Word 1 and Word 2. You can enter the input in three formats:

  1. word
  2. word#part_of_speech
  3. word#part_of_speech#sense

The part_of_speech must be one of n (noun), v (verb), a (adjective), or r (adverb).

The sense must be an integer. The noun tree#n has three senses in WordNet, so tree#n#1, tree#n#2, and tree#n#3 designate the three different noun senses of tree.

For each word that is entered, you have the choice of selecting one of three options: using all senses of the word, choosing a sense using glosses, and choosing a sense using the full synset to which each word sense belongs. Let's assume you entered tree#n in one of the input fields and shrub#n#1 in the other. If you select "use all senses", then the relatedness of each of the three senses of tree#n and shrub#n#1 will be computed. The sense of tree#n that gives the highest relatedness score will be shown.

If, on the other hand, you select "select a sense by gloss", after you click on the Compute button, you will be asked to select a sense of tree#n from a selection box, and the gloss of each sense will be shown. If you select "select a sense by synset", after you click on the Compute button, you will be asked to select a sense from a selection box, and the synset that each sense of tree#n belongs to will be shown in the box.

WordNet-Similarity-2.05/web/README0000644000175000017500000001204011017747562014732 0ustar sidsidWordNet::Similarity Web Interface ================================= The web interface distribution contains two CGI scripts, which are in the cgi-bin directory: cgi-bin/similarity.cgi cgi-bin/wps.cgi The interface employs a client-server model. The two CGI scripts, similarity.cgi and wps.cgi, are the clients that request information from the similarity server (similarity_server.pl) The similarity server is distributed as one of the utilities (in the /utils directory) in this package. The similarity server is a network interface to the WordNet::Similarity measures. It is similarity_server.pl that actually interacts with WordNet and computes relatedness scores. It loads all the similarity modules, and sits on a machine listening for network requests for relatedness information. A client can then connect to this similarity server and request information about semantic relatedness of words and concepts. This information, if available, will be returned by the similarity server to the client over the netwrok connection. In the web interface, similarity.cgi and wps.cgi are the clients that connect to the similarity server to request similarity/relatedness information. The information retrieved by these two clients from the server is then presented via web-pages to the end-user. Thus, wps.cgi and similarity.cgi form part of the web-server creating web pages to serve up to the end-user, using information obtained from the backend similarity server (similarity_server.pl). Here is a block schematic: [Similarity Server] <-----> [Web Server] <-----> [End User] (similarity.cgi) (browser) (wps.cgi) The similarity_server.pl can be run on the same machine on which your webserver is running or it can be on a different machine. Running similarity_server.pl on a different machine than the webserver can be useful if you have only limited control over the webserver machine (for example, you might not be able to install WordNet on the webserver machine). Quick Installation Instructions ------------------------------- This guide assumes that you are using the Apache webserver as installed on a Fedora/RedHat based Linux distribution. If you are using a different server or Linux distribution, then the setup process will probably vary a little. Step 1 ------ Put the similarity.cgi and wps.cgi scripts whereever CGI scripts go on your webserver (e.g., /usr/local/apache2/cgi-bin). In order to keep your system somewhat organized, you may want to put them in a subdirectory in your cgi directory (e.g., /usr/local/apache2/cgi-bin/similarity). These instructions generally assume the latter. There are various html, style sheets, and images in the 'doc' directory of the distribution. These should be put wherever html documents go on your webserver (e.g., /usr/local/apache2/htdocs). In order to keep your system somewhat organized, you may want to put them in a subdirectory in your html directory (e.g., /usr/local/apache2/htdocs/similarity). These instructions generally assume the latter. Note that if you are using Ubuntu Linux, your cgi-bin directory is typically found at /usr/lib/cgi-bin and documents are found in /var/www Step 2 ------ The following two files may need to be edited: similarity.cgi wps.cgi - similarity.cgi * change $remote_host to be the hostname or IP addr of the machine on which similarty_server.pl is located. If similarity_server.pl is running on the same machine as your web server, then 'localhost' or '127.0.0.1' will work. Note: the setting for $remote_host and $remote_port in similarity.cgi are not related to Apache's LISTEN setting. In fact, $remote_port needs to be different than the port on which Apache is listening. * set $doc_base to be the relative path to the HTML files that are in the 'doc' directory in the distribution. For example, if you have the .cgi files in /usr/local/apache2/cgi-bin/similarity and the HTML files in /usr/local/apache2/htdocs/similarity, then you would set $doc_base to '../../similarity'. Note: this variable is not (closely) related to Apache's DOCUMENT_ROOT setting. - wps.cgi * $remote_host should be the same as $remote_host for similarity.cgi. $doc_base is the location of a style sheet (sim-style.css), which is often the same as $doc_base in similarity.cgi. Step 3 ------ Start similarity_server.pl, by running: $ similarity_server.pl A number of options are available for running the server. Please see the documentation of similarity_server.pl for more information on this. This utility should be installed along with all the other utilities as part of the installation of the WordNet::Similarity package. COPYRIGHT --------- Copyright (c) 2005-2008, Ted Pedersen and Jason Michelizzi This distribution is free software; you may redistribute and/or modify it under the terms of the GNU General Public License, version 2 or, at your option, any later version. SEE ALSO -------- http://wn-similarity.sourceforge.net http://groups.yahoo.com/group/wn-similarity/ WordNet-Similarity-2.05/Makefile.PL0000644000175000017500000001237011015336623015243 0ustar sidsid# This -*- perl -*- script writes the Makefile for WordNet::Similarity # (Last updated $Id: Makefile.PL,v 1.44 2008/04/11 12:53:12 sidz1979 Exp $) # # Generates the Makefile for the WordNet::Similarity CPAN modules. # # ---------------------------------------------------------------------------- use 5.005; use ExtUtils::MakeMaker; use strict; # See lib/ExtUtils/MakeMaker.pm for details of how to influence # the contents of the Makefile that is written. my $short_tests = 't/access.t t/res.t t/lin.t t/jcn.t t/path.t t/lch.t t/wup.t t/lesk.t t/vector_pairs.t t/vector.t t/hso.t t/random.t t/wn30loop.t t/config/rootnode.t'; my $long_tests = 't/*.t t/config/*.t t/utils/*.t'; my $opt_WNHOME; my @programs_to_install = qw(BNCFreq.pl brownFreq.pl compounds.pl rawtextFreq.pl semCorRawFreq.pl semCorFreq.pl similarity.pl treebankFreq.pl wordVectors.pl wnDepths.pl similarity_server.pl); # Get the WNHOME option from the command line my @tmpArgs = @ARGV; @ARGV = (); foreach my $arg (@tmpArgs) { if($arg =~ /^WNHOME=(.*)/) { $opt_WNHOME=$1; } else { push(@ARGV, $arg); } } # Write out the make file WriteMakefile('NAME' => 'WordNet::Similarity', 'PREREQ_PM' => {'WordNet::QueryData' => '1.40', 'Text::Similarity' => '0.02', 'Digest::SHA1' => '0', 'File::Spec' => '0', 'File::Find' => '0', 'File::Temp' => '0', 'Getopt::Long' => '0', 'CGI' => '0', 'IO' => '0'}, 'PL_FILES' => {'build/Infocontent.PL' => '$(INST_LIB)/WordNet/ic-semcor.dat', 'build/Depthfiles.PL' => '$(INST_LIB)/WordNet/synsetdepths.dat'}, 'CONFIGURE' => \&configSub, 'VERSION_FROM' => 'lib/WordNet/Similarity.pm', 'EXE_FILES' => [ map "utils/$_", @programs_to_install ], 'dist' => {'COMPRESS' => 'gzip -9f', 'SUFFIX' => 'gz'}, 'realclean' => {'FILES' => 'build/wnpath.txt'}, ($] >= 5.005 ? ('ABSTRACT_FROM' => 'lib/WordNet/Similarity.pm', 'AUTHOR' => 'Ted Pedersen, Siddharth Patwardhan, Satanjeev Banerjee, Jason Michelizzi') : () ), ($ExtUtils::MakeMaker::VERSION ge '6.30_00' ? ('LICENSE' => 'gpl') : () ), 'NO_META' => 'true' ); print STDERR <<"EOIN"; *** IMPORTANT NOTE: A number of default data files (information content files, depth files, etc.) will be generated during this installation. Some of these files require WordNet to be installed and accessible through the WordNet::QueryData Perl module. This is typically done by defining the WNHOME environment variable (specifying the WordNet installation directory). Alternatively, the WNHOME option can specified to Makefile.PL. In addition, this means that the data files generated during the installation depend on the version of WordNet currently installed on your system. If at a later date, a newer version of WordNet is installed, these data files should be regenerated by running this installation again. To continue the installation process run: make make test make install You may also run a series of more rigorous tests by running 'make test_all' instead of 'make test'. EOIN # Config subroutine called by MakeMaker # Only saves the WNPATH (to be used during make), if provided # on the command line. sub configSub { # Check if path to WordNet has been provided... If so... save it. if(defined $opt_WNHOME) { open(WNPATH, ">build/wnpath.txt") || die "Unable to open file for writing (internal error).\n"; print WNPATH "$opt_WNHOME\n"; close(WNPATH); } return {}; } # What follows is some magic that makes some low-level modifications to # the text that is about to be written to the Makefile. We have to put # this in the MY package so that the call to SUPER works correctly. The # documentation for ExtUtils::MakeMaker has information on what we're # actually doing here under the section 'Overriding MakeMaker Methods'. # Essentially, we override MakeMaker's test() method and insert some # extra text to add a new target, test_all. package MY; # override test() method to add a new target, test_all sub test { my $self = shift; my $text = $self->SUPER::test (@_); # here we make a copy of the Makefile portion made by SUPER::test(), # and then use the copy to make our own rule: test_all my $newtext = $text; $newtext =~ s/test(?!_harness)/test_all/g; $newtext =~ s/TEST/TEST_ALL/g; # don't forget the /m modifier: $newtext =~ s/(?<=TEST_ALL_FILES = ).*$/${long_tests}/m; $text .= $newtext; return $text; } # override the special_targets method to add test_all to the list of # phony targets sub special_targets { my $self = shift; my $str = $self->SUPER::special_targets (@_); # add test_all to the list of phony targets $str =~ s/(\.PHONY:.* test\b)/$1 test_all/; return $str; } WordNet-Similarity-2.05/doc/0000755000175000017500000000000011025552366014040 5ustar sidsidWordNet-Similarity-2.05/doc/utils.pod0000644000175000017500000005502311015336622015703 0ustar sidsid# Run perldoc on this file to get nice, formatted documentation =head1 NAME UTILS - [documentation] WordNet::Similarity supporting utilities =head1 SYNOPSIS The '/utils' subdirectory of the package contains supporting Perl programs. 'similarity.pl' is a command-line interface to the relatedness modules. A number of Perl programs that generate information content files from various corpora are provided. 'wnDepths.pl' is a program that will generate files with the depths of WordNet synsets and the maximum depths of WordNet taxonomies. As part of the standard install, these are also installed into the system directories, and can be accessed from any working directory if the common system directories (/usr/bin, /usr/local/bin, etc.) are in your path. =head1 DESCRIPTION The '/utils' directory of the package contains a few support Perl programs, that use the WordNet::Similarity modules or generate data files for it. As part of the standard installation these are installed into the system directories (such as /usr/bin or /usr/local/bin) from where they can be easily accessed. =head2 similarity.pl The similarity.pl program provides a command-line interface to the relatedness modules. =head3 Usage Usage: similarity.pl [{--type TYPE [--config CONFIGFILE] [--allsenses] [--offsets] [--trace] [--wnpath PATH] [--simpath SIMPATH] {--interact --file FILENAME | WORD1 WORD2} |--help |--version }] Displays the semantic similarity between the base forms of WORD1 and WORD2 using various similarity measures described in Budanitsky Hirst (2001). The parts of speech of WORD1 and/or WORD2 can be restricted by appending the part of speech (n, v, a, r) to the word. (For eg. car#n will consider only the noun forms of the word 'car' and walk#nv will consider the verb and noun forms of 'walk'). Individual senses of can also be given as input, in the form of word#pos#sense strings (For eg., car#n#1 represents the first sense of the noun 'car'). =head3 Options =over =item --type Switch to select the type of similarity measure to be used while calculating the semantic relatedness. The following strings are defined. L Simple node-counts (inverted). L The Wu Palmer measure. L The Leacock Chodorow measure. L The Jiang Conrath measure. L The Resnik measure. L The Lin measure. L The Hirst St-Onge measure. L Extended gloss overlap measure. L Gloss Vector measure. LWordNet::Similarity::vector_pairs> Gloss Vector measure (pairwise). L A random-number "measure". =item --config Module-specific configuration file CONFIGFILE. This file contains the configuration that is used by the WordNet::Similarity modules during initialization. The format of this file is specific to each modules and is specified in the module man pages and in the documentation of the WordNet::Similarity package. See L for more details. =item --allsenses Displays the relatedness between every sense pair of the two input words WORD1 and WORD2. =item --offsets Displays all synsets (in the output, including traces) as synset offsets and part of speech, instead of the word#partOfSpeech#senseNumber format used by QueryData. With this option any WordNet synset is displayed as word#partOfSpeech#synsetOffset in the output. =item --trace Switches on 'Trace' mode. Displays as output on STDOUT, the various stages of the processing. This option overrides the trace option in the module configuration file (if specified). =item --interact Starts the interactive mode. Useful for demoes, debugging, and generally messing around with the measures. =item --file Allows the user to specify an input file FILENAME containing pairs of word whose semantic similarity needs to be measured. The file is assumed to be a plain text file with pairs of words separated by newlines, and the words of each pair separated by a space. =item --wnpath Option to specify the path of the WordNet data files as PATH. (Defaults to /usr/local/WordNet-2.0/dict on Unix systems and C:\WordNet\2.0\dict on Windows systems) =item --simpath If the relatedness module to be used, is locally installed, then SIMPATH can be used to indicate the location of the local install of the measure. =item --help Displays this help screen. =item --version Displays version information. =back NOTE: The environment variables WNHOME and WNSEARCHDIR, if present, are used to determine the location of the WordNet data files. Use '--wnpath' to override this. ANOTHER NOTE: For the information-content based measures, similarity.pl without the '--config' option invokes the relatedness modules using the default information content file generated during installation of the modules. If, however, the version of WordNet being used has changed since that time, or for some reason the modules are unable to locate the default information content files, then alternate information content files can be specified only via the configuration file. Utilities to generate information content files have been provided in the package. For the WordNet::Similarity::vector_pairs measure, it is mandatory to provide the location of a word vector data file and this can be only done by using the '--config' option. In short, the '--config' option is REQUIRED for the WordNet::Similarity::vector_pairs measure. YET ANOTHER NOTE: During any given session, only one of three modes of input can be specified to the program -- command-line input (WORD1 WORD2), file input (--file option) or the interactive input (--interact option). If more than one mode of input is invoked at a given time, only one of those modes will work, according to the following levels of priority: interactive mode (--interact option) has highest priority. file input (--file option) has medium priority. command-line input (WORD1 WORD2) has lowest priority. Compound words may also be given as input to similarity.pl. They may be specified using underscores for spaces (as in WordNet) or may be enclosed within double quotes. For example: similarity.pl --type WordNet::Similarity::jcn school private_school similarity.pl --type WordNet::Similarity::lch "interest rate" bank Here 'private school' and 'interest rate' are the compound words intended in the two examples, respectively. ANOTHER NOTE: Using the '--file' option however, does not allow us to use both methods of entering compound words in the input file. The compound words in the input file may be entered only using underscores for spaces (the double quotes option is not available for input via the input file). The part of speech of the input word(s) may be restricted to one or more parts of speech by appending '#' followed by a combination of one or more of 'n', 'v', 'a' or 'r' (for nouns, verbs, adjectives and adverbs) to the one or both words. A particular sense of a particular word may also be specified as input in the word#pos#sense format. Here 'pos' is exactly one of 'n', 'v', 'a' or 'r'. For example: similarity.pl --type WordNet::Similarity::jcn school#n child#n similarity.pl --type WordNet::Similarity::lesk "interest rate#n" bank#nv similarity.pl --type WordNet::Similarity::hso telephone talk#v similarity.pl --type WordNet::Similarity::vector_pairs word#n#2 newspaper#v similarity.pl --type WordNet::Similarity::random chat#n#1 talk#v#2 - Interpreting the output In the simplest case interpreting the output is rather straightforward. This is the case when just the semantic relatedness of two words has been requested. The output, in this case, consists of the two words and the relatedness value. However, when the '--allsenses' option or the '--trace' option is specified, the program needs to display in the output, WordNet synsets. In order to do this, we decided to adopt the convention introduced by Jason Rennie in the WordNet::QueryData module to represent the WordNet synsets.According to this convention a synset is represented by =over =item (1) a representative word from that synset =item (2) its part of speech and =item (3) a number specifying the sense number of the word (in this synset) =back For example, consider the synset (teacher, instructor) from the noun data file of WordNet. Here the words 'teacher' as well as 'instructor' are each in their first sense. Using the above convention this synset may be represented by 'teacher#n#1' or by 'instructor#n#1'. Besides this, if '--offsets' command-line option is used, a small variation of the above convention is used that displays the offset of the synset (in the WordNet data file) instead of the sense number. The above synset could then be represented by 'teacher#n#8562747' or 'instructor#n#8562747', since 8562747 if the offset of this synset in the noun data file of WordNet 1.7. The first convention was adopted as the default, since synset offsets vary between different versions of WordNet, while sense numbers of words would more or less remain constant. =head3 Typical usage examples =over =item (1) Suppose you wanted to find the measure of relatedness between 'car' and 'bicycle', using the Jiang-Conrath measure. similarity.pl --type WordNet::Similarity::jcn car bicycle =item (2) Suppose you need to find the relatedness of the noun forms of 'comb' and 'hair' using the Leacock-Chodorow measure and also your WordNet database files happen to be located at /WordNet-2.0/dict, then you would have similarity.pl --type WordNet::Similarity::lch --wnpath /WordNet-2.0/dict comb#n hair#n If the --wnpath option is not given, the program looks for the path to the data files in the WNHOME and the WNSEARCHDIR environment variables. If these have also not been specified, then by default the program assumes that the WordNet data files reside in the directory /usr/local/WordNet-2.0/dict on a Unix machine and in C:\WordNet\2.0\dict on a Windows machine. =item (3) An example using a data file as input to the program (using the Jiang-Conrath measure for this example) similarity.pl --type WordNet::Similarity::jcn --file testfile =item (4) Displaying relatedness between all senses of the two words along with traces. similarity.pl --type WordNet::Similarity::lch --allsenses --trace paper pencil =item (5) Displaying the relatedness between the verb form of 'talk' and all parts of speech of 'speaker', with traces using the extended gloss overlap measure. similarity.pl --type WordNet::Similarity::lesk --trace speaker talk#v =item (6) Using a configuration file "/home/sid/lesk.conf" to specify the configuration options to the WordNet::Similarity::lesk module. similarity.pl --type WordNet::Similarity::lesk --config /home/sid/lesk.conf duck fowl =item (7) To display version information. similarity.pl --version =item (8) To display detailed help. similarity.pl --help =back =head2 Information content programs Three of the measures provided within the package require information content values of concepts (WordNet synsets) for computing the semantic relatedness of concepts. We provide these measures with frequency counts of WordNet synsets computed from large corpora of text, in files called information content files. A number of programs have been provided in the '/utils' subdirectory to generate information content files from various different corpora of text available. L -- from the BNC corpus. L -- from the Brown corpus. L -- from SemCor X.X (using the sense tags). L -- from SemCor X.X (ignoring the sense tags). L -- from the Treebank corpus. L -- from raw text. All the six have a similar interface, however there are slight differences in the way the programs are called on the command-line due to the differences in the organization and format of the various corpora. But the following sub-sections give the typical usage and examples of all these programs. Please use the '--help' switch of each of the programs for the exact usage and help. =head3 Usage [{--outfile OUTFILE [--stopfile STOPFILE] [--wnpath WNPATH] [--resnik] [--smooth SCHEME] PATH | --help | --version }] Here is one of the Perl programs provided, that generates an information content file from a large corpus of text. This program computes the information content of concepts, by counting the frequency of their occurrence in a corpus. PATH specifies the files of the corpus or the root of the directory tree containing the text of the corpus. Each utility has a different way in which the input files may be specified to it. Please use --help to get the -specific idiosyncrasies. =head3 Options: =over =item --outfile Specifies the output file OUTFILE. =item --stopfile STOPFILE is a list of stop listed words that will not be considered in the frequency count. =item --wnpath Option to specify WNPATH as the location of WordNet data files. If this option is not specified, the program tries to determine the path to the WordNet data files using the WNHOME environment variable. =item --resnik To enable the counting of frequencies using the method described by Resnik [3]. This was the method of counting originally used. We implemented a different scheme of counting described in our publication (Patwardhan, Banerjee and Pedersen [9]). =item --smooth Specifies the smoothing to be used on the probabilities computed. SCHEME specifies the type of smoothing to perform. It is a string, which can be only be 'ADD1' as of now. Other smoothing schemes will be added in future releases. =item --help Displays this help screen. =item --version Displays version information. =back A sample COMPFILE containing the list of compounds in WordNet 2.0 is present is the '/samples' subdirectory. A utility called compounds.pl has been provided in the '/utils' subdirectory. This utility generates a list of compounds present in your version of WordNet and can be used to generate a file containing the list of compounds in WordNet as follows: compounds.pl > compounds.dat In this case compounds.pl detects the location of the WordNet data files using the WNHOME environment variable. If the WNHOME environment variable has not been set up it tries default locations (C:\Program Files\WordNet\2.0 on Windows and /usr/local/WordNet-2.0 on a Unix system). Another way to specify the location of the WordNet data files is by using the '--wnpath' option in compounds.pl, like so compounds.pl --wnpath /usr/local/WordNet-2.0 > compounds.dat - The utility-specific idiosyncrasies =over =item (a) BNCFreq.pl -- This utility creates the information content file from the word frequencies counted from the British National Corpus. The data files in the BNC are XML tagged files present in a 2-level directory structure. In a typical BNC install, the data files of the BNC reside in /BNC-world/Texts subpath of the BNC installation. This is the path that needs to be specified to BNCFreq.pl to count the frequency of words: BNCFreq.pl [OPTIONS] /home/sid/BNC-world/Texts =item (b) brownFreq.pl -- The version of the Brown corpus that we used contained the data files in the BROWN1 and BROWN2 subdirectories of the Brown corpus installation. Both directories contain the same data formatted a little differently. These files are provided to the brownFreq.pl utility as a list of files (commonly specified by wildcards, as follows): brownFreq.pl [OPTIONS] /home/sid/Brown/BROWN1/*.TXT =item (c) semCorRawFreq.pl -- These information content files are computed using SemCor, but ignore the sense tags. SemCor X.X was downloaded from Dr. Rada Mihalcea's website http://www.cs.unt.edu/~rada/software.html and is a sense tagged subset of the Brown Corpus and the Red Badge of Courage. The tagged data files are present in the /brown1/tagfiles subdirectory of the extracted package. These files are provided to the semCorRawFreq.pl utility as a list of files (using wildcards): semCorRawFreq.pl [OPTIONS] /home/sid/semcorXX/brown1/tagfiles/* where XX is the the version of semCor appropriate for the version of WordNet being used. =item (d) semCorFreq.pl -- These information content files are computed from SemCor X.X (using the sense tags). The word frequencies for these have already been computed and are distributed as a part of the standard distribution of WordNet. Thus only the location of the WordNet data files need be specified for this utility (using the WNHOME environment variable or the '--wnpath' option). semCorFreq.pl [OPTIONS] =item (e) treebankFreq.pl -- This utility computes the information content files from the Treebank corpus (only the Wall Street Journal articles). The Wall Street Journal articles are usually present in the /raw/wsj subdirectory of the Treebank installation. Only this needs to be specified when using this utility: treebankFreq.pl [OPTIONS] /home/sid/treebank/raw/wsj =item (f) rawtextFreq.pl -- To compute the information content files from raw text, only the raw text file(s) need to be specified as the input. A good source of raw text files is the Gutenberg Project: http://www.gutenberg.org =back =head3 Some typical examples =over =item (1) In order to generate the information content file from the BNC, we type the command: BNCFreq.pl --outfile infoBNC.dat /home/sid/BNC-world/Texts Here '/home/sid/BNC-world/Texts' is the path containing the BNC. Ouptut information content file is infoBNC.dat. =item (2) Frequency counts generated from the Brown corpus, using a stop-list. brownFreq.pl --outfile infoBrown.dat --stopfile stop.txt /home/sid/Brown/* Uses the file 'stop.txt' containing stop words -- words that are ignored while counting the frequencies. =item (3) Frequency counts generated from a raw text file, using Resnik counting. rawtextFreq.pl --outfile infoRawText.dat --resnik /home/sid/Texts/WorldWar.txt WorldWar.txt is the raw text file. infoRawText.dat is the output information content file. =item (4) Using a the Treebank corpus (WSJ articles) to generate an information content file with the option for Add-1 smoothing. treebankFreq.pl --outfile tbInfo.dat --smooth ADD1 /home/sid/treebank/raw/wsj 'tbInfo.dat' is the output file. '/home/sid/treebank/raw/wsj' is the path to the Wall Street Journal articles of the Treebank corpus. The '--smooth ADD1' requests the program to use Add-1 smoothing of the frequency counts. It adds 1 to all frequency counts to prevent any 0 frequency values. =back =head2 wordVectors.pl The WordNet::Similarity::vector_pairs module requires a file containing co-occurrence vectors for all the words in the WordNet glosses. The utility wordVectors.pl has been provided to generate such a database file. This utility generates co-occurrence vectors from the WordNet glosses themselves. Utilities to generate these from other corpora will be provided in future releases of this software. Usage: wordVectors.pl [{ [--stopfile STOPLIST] [--wnpath WNPATH] [--noexamples] [--cutoff VALUE] [--rhigh RHIGH] [--rlow RLOW] [--chigh CHIGH] [--clow CLOW] DBFILE | --help | --version }] This program writes out word vectors computed from WordNet glosses in a database file specified by filename DBFILE. Options: =over =item --stopfile Option specifying a list of stopwords to not be considered while counting. =item --wnpath WNPATH specifies the path of the WordNet data files. Ordinarily, this path is determined from the $WNHOME environment variable. But this option overides this behavior. =item --noexamples Removes examples from the glosses before processing. =item --cutoff Option used to restrict the dimensions of the word vectors with an tf/idf cutoff. VALUE is the cutoff above which is an acceptable tf/idf value of a word. =item --rhigh RHIGH is the upper frequency cutoff of the words selected to have a word-vector entry in the database. =item --rlow RLOW is the lower frequency cutoff of the words selected to have a word-vector entry in the database. =item --chigh CHIGH is the upper frequency cutoff of words that form the dimensions of the word-vectors. =item --clow CLOW is the lower frequency cutoff of words that form the dimensions of the word-vectors. =item --help Displays help screen. =item --version Displays version information. =back A useful utility called 'readDB.pl' has been included in the package, so as to be able to view the word vectors formed and stored in the database. =head2 wnDepths.pl The lch and wup measures both need files that contain certain types of information about the WordNet taxonomies. The wup measure needs a file that lists the depth of every synset in its taxonomy(-ies), and the lch measure needs a file that lists the maximum depth of each taxonomy. The wnDepths.pl program can generate both of these files. Usage: wnDepths.pl [[--wnpath=PATH] [--outfile=FILE] [--depthfile=FILE] [--wps] [--verbose]] | --help | --version] Options: =over =item --wnpath=PATH PATH is the path to WordNet. The default is /usr/local/WordNet-2.0/dict on Unix and C:\WordNet\2.0\dict on Windows =item --outfile=FILE File to which the maximum depths of the taxonomies should be output. =item --depthfile=FILE File to which the depth of every synset should be output =item --wps output is in 'word#part_of_speech#sense format instead of offset format =item --verbose be verbose =item --help show this help message =item --version show version information =back =head1 AUTHORS Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah sidd at cs.utah.edu Satanjeev Banerjee, Carnegie-Mellon University banerjee+ at cs.cmu.edu Jason Michelizzi =head1 COPYRIGHT Copyright (c) 2005 - 2008, Ted Pedersen, Siddharth Patwardhan, Satanjeev Banerjee, and Jason Michelizzi Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. Note: a copy of the GNU Free Documentation License is available on the web at L and is included in this distribution as FDL.txt. =cut WordNet-Similarity-2.05/doc/config.pod0000644000175000017500000001716111015336622016011 0ustar sidsid# run perldoc on this file to get nicely formatted documentation =head1 NAME CONFIG - [documentation] Description of all configuration options for measures =head1 DESCRIPTION The following is a list of options supported by the measures of semantic relatedness. This is intended to serve as a "master list" of options so that descriptions can be copied from here and pasted into the documentation for specific modules. =over =item trace This option is supported by all measures. The value of this parameter specifies the level of tracing that should be employed for generating the traces. This value is an integer equal to 0, 1, or 2. If the value is omitted, then the default value, 0, is used. A value of 0 switches tracing off. A value of 1 or 2 switches tracing on. The difference between a value of 1 or 2 depends upon the measure being used. For vector_pairs and lesk, a value of 1 displays as traces only the gloss overlaps found. A value of 2 displays as traces all the text being compared. For the res, lin, jcn, wup, lch, path, and hso measures, a trace of level 1 means the synsets are represented as word#pos#sense strings, while for level 2, the synsets are represented as word#pos#offset strings. =item cache This option is supported by all measures. The value of this parameter specifies whether or not caching of the relatedness values should be performed. This value is an integer equal to 0 or 1. If the value is omitted, then the default value, 1, is used. A value of 0 switches caching 'off', and a value of 1 switches caching 'on'. =item maxCacheSize This option is supported by all measures. The value of this parameter indicates the size of the cache, used for storing the computed relatedness value. The specified value must be a non-negative integer. If the value is omitted, then the default value, 5,000, is used. Setting maxCacheSize to zero has the same effect as setting cache to zero, but setting cache to zero is likely to be more efficient. Caching and tracing at the same time can result in excessive memory usage because the trace strings are also cached. If you intend to perform a large number of relatedness queries, then you might want to turn tracing off. =item rootNode This option is supported by the res, lin, jcn, wup, path, and lch measures. The value of this parameter indicates whether or not a unique root node should be used. In WordNet, there is no unique root node for the noun and verb taxonomies. If this parameter is set to 1 (or if the value is omitted), then certain measures (wup, path, lch, res, lin, and jcn) will "fake" a unique root node. If the value is set to 0, then no unique root node will be used. If the value is omitted, then the default value, 1, is used. =item infocontent This option is supported by the res, lin, and jcn measures. The value for this parameter should be a string that specifies the path of an information content file containing the frequency of occurrence of every WordNet concept in a large corpus. A number of utility programs are included in this distribution that can be used to generate an infocontent file (see utils.pod). If no path is specified, then the default infocontent file is used, which was generated from SemCor using the sense-tags. =item taxonomyDepthsFile This option is supported only by the lch measure. The value for this parameter should be a string that specifies the location of a taxonomy depths file (as generated by wnDepths.pl). If no path is specified, then the default file is used, which was generated when the Similarity package was installed. =item synsetDepthsFile This option is supported only by the wup measure. The value for this parameter should be a string that specifies the location of a synset depths file (as generated by wnDepths.pl. If no path is specified, then the default file is used, which was generated when the Similarity package was installed. =item relation This option is supported only by the lesk and vector_pairs measures. The value of this parameter is the path to a file that contains a list of WordNet relations. The path may be either an absolute path or a relative path. The vector_pairs module combines the glosses of synsets related to the target synsets by these relations and forms the gloss-vector from this combined gloss. The lesk module combines glosses of synsets related to the target synsets by these relations and then searches for overlaps in these "super-glosses." WARNING: the format of the relation file is different for the vector_pairs and lesk measures. The documentation for lesk and vector_pairs describe the respective formats for the relation files. See I(3pm) and I(3pm). =item stop This option is supported only by the lesk and vector_pairs measures. The value of this parameter the path of a file containing a list of stop words that should be ignored in the glosses. The path may be either an absolute path or a relative path. =item stem This option is supported only by the lesk and vector_pairs measures. The value of this parameter indicates whether or not stemming should be performed. The value must be an integer equal to 0 or 1. If the value is omitted, then the default value, 0, is used. A value of 1 switches 'on' stemming, and a value of 0 switches stemming 'off'. When stemming is enabled, all the words of the glosses are stemmed before their vectors are created for the vector measure or their overlaps are compared for the lesk measure. =item normalize This option is supported only by the lesk measure. The value of this parameter indicates whether or not normalization of scores is performed. The value must be an integer equal to 0 or 1. If the value is omitted, then the default value, 0, is assumed. A value of 1 switches 'on' normalizing of the score, and a value of 0 switches normalizing 'off'. When normalizing is enabled, the score obtained by counting the gloss overlaps is normalized by the size of the glosses. The details are described in Banerjee and Pedersen (2002). =item vectordb This option is supported only by the vector_pairs measure. The value of this parameter is the path to a Vectors file containing word vectors, i.e. co-occurrence vectors for all the words in the WordNet glosses. The value of this parameter may not be omitted, and the vector_pairs measure will not run without a DB file being specified in a configuration file. =item maxrand This option is supported only by the random measure. The value of this option is the maximum random number that will be generated. The value of this option must be a positive floating-point number. The default value is 1.0. All random numbers generated will be in the range [0, maxrand). =back =head1 SEE ALSO L Mailing list: L Project Home page: L =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Jason Michelizzi =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen, Siddharth Patwardhan, Satanjeev Banerjee, and Jason Michelizzi Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. Note: a copy of the GNU Free Documentation License is available on the web at L and is included in this distribution as FDL.txt. WordNet-Similarity-2.05/doc/todo.pod0000644000175000017500000001077311015336622015513 0ustar sidsid=head1 NAME TODO - Todo list for WordNet::Similarity =head1 DESCRIPTION As these items are completed, move them down into Recently Completed Items, make sure to date and initial. When we have a version release, all of the recently completed items should be moved into changelog.pod. =head1 TODO LIST FOR FUTURE VERSIONS =over =item * Update hso to support new derivational relations introduced in WordNet 2.0 =item * Update hso to support the use of a hypothetical root node. Currently (as of version 0.06 and 0.07) its paths (for hypernyms) are limited to a particular taxonomy. This might be problematic when it comes to nouns, which are split into 9(?) separate taxonomies within WordNet. And of course verbs are split into hundreds of taxonomies. Right now when hso is on a hypernym path it isn't able to cross "up and over". Seems like it should be able to do so. Note that as of WordNet 3.0 the nouns have been joined into a single hierarchy, so perhaps hso will behave differently there. =item * Re-write hso to make it faster and more generic. check to see if hso uses hypo root node, and consider adding ability to turn on/off. Note that as of WordNet 3.0 the hypo root node is only relevant for verbs, since nouns have been joined into a single large hierarchy. =item * Re-write *Freq.pl programs to reduce redundancy and make faster. At present there are bugs in all of these programs. Create test cases that are manually verified and included in testing directory. =item * Support --trace option on info content programs to allow for wps format to be displayed in addition to (or instead of?) offset. =item * Run profiles of rawtextFreq.pl and BNCFreq.pl to determine where time is being spent. Brown, SemCor and Treebank all seem to run reasonably quickly (20 minutes, 5 minutes, and 40 minutes, respectively). Run 1 million words worth of BNC in order to compare with Brown and Treebank. =item * rawtextFreq.pl runs really slowly. It may have to do with the fact that raw text has no markup in the text to identify sentence boundaries or otherwise guide the programs. This might particularly slow down compound identification. =item * Makefile.PL and semCorFreq.pl seem to be somewhat alike. Can Makefile.PL simply call /utils/SemCorFreq.pl, or can this duplication be avoided in some other way? =item * Update documentation to clarify that stoplists must also be all lowercase. Consider adjusting stoplists to use regular expressions. =item * Speed up lesk, and make it more generic. string matching is the big offender with respect to speed, and wordnet specific stuff is the problem with respect to generality. =item * Update lesk/vector to support new derivational relations introduced in WordNet 2.0 =item * Use info content programs to create new cntlist files for WordNet? =back =head1 WISH LIST FOR FUTURE RELEASES, DO WHEN POSSIBLE =over =item * Edge/path and jcn are both distance measures. To convert them to similarity measures, we currently use 1/distance. This shifts the scale of the measures and changes the relative distance between pairs. Alternatives are to use -dist or maxdist-dist. Computation of maxdist for path is much like computation for lch (with and without hypo root node). for jcn it poses a new issue, in that we would need to find the pair of concepts that had the greatest individual information content, and are subsumed by a root node (either hypo or "real"). =item * Check if warnings are issued when there are version clashes between info content files and wordnet version. =back =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Jason Michelizzi Last modified: $Id: todo.pod,v 1.83 2008/04/12 17:41:52 tpederse Exp $ =head1 SEE ALSO L =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen, Siddharth Patwardhan, Satanjeev Banerjee, and Jason Michelizzi. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. Note: a copy of the GNU Free Documentation License is available on the web at L and is included in this distribution as FDL.txt. =cut WordNet-Similarity-2.05/doc/install.pod0000644000175000017500000002070711020102627016202 0ustar sidsid=head1 NAME INSTALL - [documentation] How to install WordNet::Similarity =head1 SYNOPSIS perl Makefile.PL make make test make install =head1 DESCRIPTION =head2 Prerequisites You need to have WordNet (version 3.0) installed, L (version 1.40 or later), and L (version 0.04 or later) installed. [NOTE: However, if you're using WordNet 3.0, you must use WordNet::QueryData v1.46 or above. WordNet::QueryData v1.40 through v1.45 can only be used with WordNet 2.1.] WordNet is available at L, L is available from L, and L is available from L. You should set the WNHOME environment variable to the location where you have WordNet installed; see the L documentation for more information. =head2 Installing The usual way to install the package is to run the following commands: perl Makefile.PL make make test make install If you can't set the WNHOME environment variable, you can use the WNHOME option when running S. For example, perl Makefile.PL WNHOME=/usr/local/WordNet-3.0 You will often need root access/superuser priviledges to run S. The module can also be installed locally. To do a local install, you need to specify a PREFIX option when you run 'perl Makefile.PL'. For example, perl Makefile.PL PREFIX=/home/sid or perl Makefile.PL LIB=/home/sid/lib PREFIX=/home/sid will install Similarity into /home/sid. The first method above will install the modules in /home/sid/lib/perl5/site_perl/5.8.3 (assuming you are using version 5.8.3 of Perl; otherwise, the directory will be slightly different). The second method will install the modules in /home/sid/lib. In either case the executable scripts will be installed in /home/sid/bin and the man pages will be installed in home/sid/share. Warning: do not put a dash or hyphen in front of PREFIX, LIB or WNHOME. In your perl programs that you may write using the modules, you may need to add a line like so use lib '/home/sid/lib/perl5/site_perl/5.8.3'; if you used the first method or use lib '/home/sid/lib'; if you used the second method. By doing this, the installed modules are found by your program. To run the similarity.pl program, you would need to do perl -I/home/sid/lib/perl5/site_perl/5.8.3 similarity.pl or perl -I/home/sid/lib Of course, you could also add the 'use lib' line to the top of the program yourself, but you might not want to do that. You will need to replace 5.8.3 with whatever version of Perl you are using. The preceeding instructions should be sufficient for standard and slightly non-standard installations. However, if you need to modify other makefile options you should look at the ExtUtils::MakeMaker documentation. Modifying other makefile options is not recommended unless you really, absolutely, and completely know what you're doing! NOTE: The information-content based measures (res, lin, jcn) are invoked using the default information content file generated during installation of the modules. If, however, the version of WordNet being used on your system has changed since that time, or for some reason the modules are unable to locate the default information content files, then alternate information content files can be specified only by using a configuration file corresponding to each of the modules. Format and creation of configuration files has been discussed in the documentation. Utilities to generate information content files have been provided in the package. NOTE: If one (or more) of the tests run by 'make test' fails, you will see a summary of the tests that failed, followed by a message of the form "make: *** [test_dynamic] Error Y" where Y is a number between 1 and 255 (inclusive). If the number is less than 255, then it indicates how many test failed (if more than 254 tests failed, then 254 will still be shown). If one or more tests died, then 255 will be shown. For more details, see: http://search.cpan.org/dist/Test-Simple/lib/Test/Builder.pm#EXIT_CODES =head2 System Requirements =over =item 1. Perl version 5.6 or later. This package has been written in Perl, which is freely available from www.perl.org. This package assumes that perl is installed in the directory /usr/bin. If this is where perl is on your computer, then the support programs can be run directly at the command line as 'similarity.pl ...' or 'semCorFreq.pl...', etc. However, if Perl is not installed at this location, you would need to explicitly invoke them as 'perl similarity.pl ... ' or 'perl freqCount.pl...', etc. =item 2. WordNet: All the measures are based on WordNet. WordNet must be installed on your system. WordNet is freely downloadable from L. WordNet version 3.0 was used during the development and testing of the package. Unfortunately, due to major changes in WordNet (and L) from version 2.0 to 2.1, this version of L supports only WordNet 2.1 (or later). The L Perl module is used to access WordNet. This module requires that an environment variable 'WNHOME', containing the path to the WordNet files, be set up. For further details, please see the L documentation. =item 3. L: This is the Perl interface to WordNet written by Jason Rennie. QueryData should be accessible on the @INC path of Perl. (Can be freely downloaded from L). QueryData 1.46 was used during the development. Also we observed that that due to some major changes in QueryData from its previous versions, this software does not work with the earlier versions of QueryData. If you have an earlier version of QueryData (1.38 or earlier) you may need to upgrade QueryData. =item 4. L: This module is used by the L measure for finding runs of word overlaps in glosses. The L module can be downloaded from L. Version 0.04 or better is required in order to be compatible with Perl version 5.6 and better. =back =head2 Optional Tests Running 'make install' after make will run a short series of tests. These tests should not take more than a few minutes to run. There is another series of more rigorous tests that may also be run; however, these tests can take quite some time to run (over an hour on some machines). To run these tests, run 'make test_all'. =head2 Example The following procedure will work on most Linux systems. cd /tmp wget http://wordnet.princeton.edu/3.0/WordNet-3.0.tar.gz wget http://search.cpan.org/CPAN/authors/id/J/JR/JRENNIE/WordNet-QueryData-1.46.tar.gz wget http://search.cpan.org/CPAN/authors/id/J/JA/JASONM/Text-Similarity-0.02.tar.gz wget http://search.cpan.org/CPAN/authors/id/T/TP/TPEDERSE/WordNet-Similarity-2.01.tar.gz Then unpack each one: tar -zxvf WordNet-3.0.tar.gz tar -zxvf WordNet-QueryData-1.46.tar.gz tar -zxvf Text-Similarity-0.02.tar.gz tar -zxvf WordNet-Similarity-2.01.tar.gz Install WordNet: cd /tmp/WordNet-3.0 ./configure make su make install exit Installing QueryData and Similarity: cd /tmp/WordNet-QueryData-1.46 perl Makefile.PL make make test su make install exit cd /tmp/Text-Similarity-0.02 perl Makefile.PL make make test su make install exit cd /tmp/WordNet-Similarity-2.01 perl Makefile.PL make make test su make install exit =head1 SEE ALSO L Mailing list: L Project Home page: L =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Jason Michelizzi =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen, Siddharth Patwardhan, Satanjeev Banerjee, and Jason Michelizzi Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. Note: a copy of the GNU Free Documentation License is available on the web at L and is included in this distribution as FDL.txt. =cut WordNet-Similarity-2.05/doc/intro.pod0000644000175000017500000002461411015336622015700 0ustar sidsid# run perldoc on this file to get nicely formatted documentation =head1 NAME README - [documentation] Introduction to WordNet::Similarity =head1 SYNOPSIS There are a number of documentation files covering different aspects of WordNet::Similarity available: =over =item * L Introduction to WordNet::Similarity (this document) =item * L How to install =item * L How to use the utility programs in /utls =item * L How the measure modules are designed =item * L How to write your own measure of semantic relatedness =item * L How to set configuration options for the measures =back You can use pod2html, pod2latex, pod2man, or pod2text to get this documentation in a different format. See the man pages for pod2html, etc. These translators should come with Perl, but you can also download them from L. =head1 DESCRIPTION This package consists of Perl modules along with supporting Perl programs that implement the semantic relatedness measures described by Leacock & Chodorow (1998), Jiang & Conrath (1997), Resnik (1995), Lin (1998), Hirst & St-Onge (1998), Wu & Palmer (1994), the extended gloss overlap measure by Banerjee and Pedersen (2002), and two measures based on context vectors by Patwardhan (2003). The details of the vector measure are described in the Master's thesis work of Patwardhan (2003) at the University of Minnesota Duluth, and the vector_pairs measure is derived from that. The Perl modules are designed as objects with methods that take as input two word senses. The semantic relatedness of these word senses is returned by these methods. A quantitative measure of the degree to which two word senses are related has wide ranging applications in numerous areas, such as word sense disambiguation, information retrieval, etc. For example, in order to determine which sense of a given word is being used in a particular context, the sense having the highest relatedness with its context word senses is most likely to be the sense being used. Similarly, in information retrieval, retrieving documents containing highly related concepts are more likely to have higher precision and recall values. A command line interface to these modules is also present in the package. The simple, user-friendly interface returns the relatedness measure of two given words. A number of switches and options have been provided to modify the output and enhance it with trace information and other useful output. Details of the usage are provided in other sections of this README. Supporting utilities for generating information content files from various corpora are also available in the package. The information content files are required by three of the measures for computing the relatedness of concepts. The following sections describe the organization of this software package and how to use it. A few typical examples are given to help clearly understand the usage of the modules and the supporting utilities. =head1 SEMANTIC RELATEDNESS We observe that humans find it extremely easy to say if two words are related and if one word is more related to a given word than another. For example, if we come across two words -- 'car' and 'bicycle', we know they are related as both are means of transport. Also, we easily observe that 'bicycle' is more related to 'car' than 'fork' is. But is there some way to assign a quantitative value to this relatedness? Some ideas have been put forth by researchers to quantify the concept of relatedness of words, with encouraging results. A number of different measures of relatedness have been implemented in this software package. These include a simple edge counting approach and a random method for measuring relatedness. The measures rely heavily on the vast store of knowledge available in the online electronic dictionary -- WordNet. So, we use a Perl interface for WordNet called WordNet::QueryData to make it easier for us to access WordNet. The modules in this package REQUIRE that the WordNet::QueryData module be installed on the system before these modules are installed. =head1 CONTENTS The package contains the semantic relatedness modules, some support Perl utilities and some sample configuration files, data files and programs. =head2 Modules All the modules that will be installed in the Perl system directory are present in the '/lib' directory tree of the package. These include the semantic relatedness modules -- jcn.pm, res.pm, lin.pm, lch.pm, hso.pm, lesk.pm, vector.pm, vector_pairs.pm, wup.pm, path.pm and random.pm -- present in the /lib/WordNet/Similarity subdirectory and the supporting modules get_wn_info.pm and string_compare.pm. There also exists a WordNet/Similarity.pm module that currently serves as a base class for all the relatedness modules and contains Perl documentation. All these modules, once installed in the Perl system directory, can be directly used by Perl programs. =head2 Supporting Utilities The '/utils' subdirectory of the package contains supporting Perl programs. 'similarity.pl' is a command-line interface to the relatedness modules. A number of Perl programs, that generate information content files from various corpora, are provided. As part of the standard install, these are also installed into the system directories, and can be accessed from any working directory if the common system directories (/usr/bin, /usr/local/bin, etc) are in your path. =head2 Samples If you downloaded this package as a tar-gzipped file from the web, you will find a '/samples' subdirectory in the package. There is a separate README in that directory. The directory contains sample configuration files for the modules, sample programs showing usage of the modules and sample data files (e.g., relation files). =head1 REFERENCES =over =item 1 Wu Z. and Palmer M. 1994. Verb Semantics and Lexical Selection. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics. Las Cruces, New Mexico. =item 2 Resnik P. 1995. Using information content to evaluate semantic similarity. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, pages 448-453. Montreal. =item 3 Jiang J. and Conrath D. 1997. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of International Conference on Research in Computational Linguistics. Taiwan. =item 4 Fellbaum C., editor. WordNet: An electronic lexical database. MIT Press, 1998. =item 5 Leacock C. and Chodorow M. 1998. Combining local context and WordNet similarity for word sense identification. In Fellbaum 1998, pp. 265-283. =item 6 Lin D. 1998. An information-theoretic definition of similarity. In Proceedings of the 15th International Conference on Machine Learning. Madison, WI. =item 7 Hirst G. and St-Onge D. 1998. Lexical Chains as representations of context for the detection and correction of malapropisms. In Fellbaum 1998, pp. 305-332. =item 8 SchEtze H. 1998. Automatic Word Sense Discrimination. Computational Linguistics, 24(1):97-123. =item 9 Resnik P. 1999. Semantic Similarity in a Taxonomy: An Information- Based Measure and its Applications to Problems of Ambiguity in Natural Language. Journal of Artificial Intelligence Research, 11, 95-130. =item 10 Budanitsky A. and Hirst G. 2001. Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures. In Workshop on WordNet and Other Lexical Resources, Second meeting of the North American Chapter of the Association for Computational Linguistics. Pittsburgh, PA. =item 11 Banerjee S. and Pedersen T. 2002. An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet. In Proceeding of the Fourth International Conference on Computational Linguistics and Intelligent Text Processing (CICLING-02). Mexico City. =item 12 Patwardhan S., Banerjee S. and Pedersen T. 2002. Using Semantic Relatedness for Word Sense Disambiguation. In Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics. Mexico City. =item 13 Banerjee S. and Pedersen T. 2003. Extended Gloss Overlaps as a Measure of Semantic Relatedness. In the Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Acapulco, Mexico. =item 14 Patwardhan S. and Pedersen T. 2006. Using WordNet-based Context Vectors to Estimate the Semantic Relatedness of Concepts. In the Proceedings of the EACL 2006 Workshop Making Sense of Sense - Bringing Computational Linguistics and Psycholinguistics Together. Trento, Italy. =item 15 Banerjee S. Adapting the Lesk algorithm for word sense disambiguation to WordNet. Master Thesis, University of Minnesota, Duluth, 2002. =item 16 Patwardhan S. Incorporating dictionary and corpus information into a vector measure of semantic relatedness. Master Thesis, University of Minnesota, Duluth, 2003. =back =head1 SEE ALSO L Mailing list: L Project Home page: L =head1 ACKNOWLEDGEMENTS We would like to thank the following for their support and contribution towards the development of this package. We thank Jason Rennie for his QueryData package, the WordNet guys at Princeton for WordNet, Resnik, Hirst, St-Onge, Jiang, Conrath, Lin, Wu, Palmer, Leacock, and Chodorow for their algorithms and work on the relatedness measures. We also thank Bano (Satanjeev Banerjee) for his work on the extended gloss overlap module. We are grateful to Wybo Wiersma for contributing his optimizations to the GlossFinder code. We also appreciate the many helpful suggestions and bug patches from Ben Haskell. =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Jason Michelizzi =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen, Siddharth Patwardhan, Satanjeev Banerjee, and Jason Michelizzi Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. Note: a copy of the GNU Free Documentation License is available on the web at L and is included in this distribution as FDL.txt. =cut WordNet-Similarity-2.05/doc/changelog.pod0000644000175000017500000012316711025550157016501 0ustar sidsid=head1 NAME CHANGES - Revision history for WordNet::Similarity =head1 DESCRIPTION =head2 Version 2.05 (Released 06/16/2008) =over =item * 05/30/08 =over =item (1) Created new module WordNet::Similarity::FrequencyCounter containing common support code for information content programs. (Sid) =item (2) Updated all the frequency counting programs in /utils (*Freq.pl) to use the common code in WordNet::Similarity::FrequencyCounter. (Sid) =item (3) Changed the default path to Perl from /usr/local/bin to /usr/bin in all scripts and tests in the package. (Sid) =item (4) Fixed incorrect handling of BNC header information. (Sid) =item (5) Modified the compoundify() method in WordNet::Tools to include compounds containing special characters (period, hyphen, forward-slash, single-quote). (Sid) =item (6) Updated compoundify() to handle larger compounds. (Sid) =back =item * 04/23/08 =over =item (1) Fixed the "excessive ROOTs" bug in *Freq.pl. (Sid) =item (2) Fixed the extra verb concept counts in *Freq.pl. (Sid) =back =back =head2 Version 2.04 (Released 04/19/2008) =over =item * 04/17/08 =over =item (1) Reorganized similarity_server initialization. (Sid) =item (2) The similarity server now prints more intuitive messages. (Sid) =item (3) Attached timestamps to log messages. (Sid) =item (4) Added additional checks to input strings from clients. (Sid) =back =item * 04/12/08 =over =item (1) Added more detailed description of information content to rawtextFreq.pl, and made minor copy editing and formatting changes to other /utils files (TDP) =item (2) Made minor copy editing and formatting changes to files in /doc (TDP) =back =item * 04/10/08 =over =item (1) Moved get_wn_info, stem and vectorFile modules under WordNet, i.e., they are now WordNet::get_wn_info, WordNet::stem and WordNet::vectorFile. (Sid) =item (2) Updated all the modules and programs using the above modules. (Sid) =item (3) Added copyright notices in all module and program headers. (Sid) =item (4) Added method getCompoundsList() to WordNet::Tools. (Sid) =item (5) Made a more distrtibutable version of simialrity_server. The similarity_server is now "daemonized", and is installed in /usr/bin along with the other utils. (Sid) =back =item * 03/23/08 =over =item (1) Added SIGNATURE to distrribution to enable package verification. (Sid) =item (2) Updated MANIFEST to reflect new SIGNATURE. (Sid) =item (3) Set the LICENSE to gpl in META.yml and Makefile.PL. (Sid) =back =item * 03/17/08 =over =item (1) Added NO_META option to Makefile.PL to prevent automatic generation of META.yml during 'make dist'. (Sid) =item (2) Removed unused variable "loaded" from Makefile.PL. (Sid) =back =back =head2 Version 2.03 (Released 03/11/2008) =over =item * 03/07/08 =over =item (1) Removed all references to WordNet::QueryData from Makefile.PL. This is based on the following advice present in the ExtUtils::MakeMaker documentation: "Module installation tools have ways of resolving unmet dependencies but to do that they need a Makefile". By checking for the presence of WordNet::QueryData during 'perl Makefile.PL', we are preventing any opportunity for automated dependency resolution. (Sid) =item (2) The WordNet path (if specified by the WNHOME option during 'perl Makefile.PL') is not checked for validity beforehand, and is now directly provided as-is to build/Infocontent.PL and build/Depthfiles.PL. In case of a WNHOME error, now 'make' should fail instead of 'perl Makefile.PL' (which is more appropriate). (Sid) =item (3) Corrected a typo in DepthFinder.pm synopsis that refered to getTaxonomyRoot rather than getTaxonomies. Removed some cut and paste documentation from the templated used for GlossFinder.pm and PathFinder.pm (Ted) =item (4) Made synopsis examples WordNet version independent by not hard coding offsets, etc. Did this in Depthfinder.pm, PathFinder.pm, ICFinder, and GlossFinder.pm (Ted) =item (5) Made minor changes in path names and file names in the /samples directory and the /config-files subdirectory. (Ted) =back =back =head2 Version 2.02 (Released 03/04/2008) =over =item * 03/04/08 =over =item (1) Applied patch from Ben Haskell to fix a bug report (submitted by Quang Do Xuan) about failing self-similarity of tilde#n#1 using wup and lch measures. (Sid) =item (2) Added tests for above bug to t/wup.t and t/lch.t. (Sid) =item (3) Added WordNet::Similarity package version info to similarity.pl --version. (Sid) =back =item * 01/31/08 =over =item (1) Changed some default options in the similarity_server.conf configuration. (Sid) =item (2) Reformatted some of the similarity_server code. (Sid) =back =item * 01/10/08 =over =item (1) Reduced version requirements of some of the PREREQ_PM modules. (Sid) =item (2) Changed WordNet::QueryData requirements to v1.40 in the documentation. (Sid) =back =back =head2 Version 2.01 (Released 10/14/2007) =over =item * 10/13/07 =over =item (1) Fixed error in loading WordNet::Tools for similarity_server.pl. (Sid) =item (2) Removed the use of default (hardcoded) stoplist and word-vectors file for similarity_server.pl. (Sid) =item (3) Print WordNet hash-code instead of WordNet version, for similarity.cgi WordNet version information. (Sid) =back =item * 10/09/07 =over =item (1) Updated the Pathfinder code to handle loops in the WordNet is-a hierarchy (like the one in WN3.0). (Sid) =item (2) Updated MANIFEST, changelog and documentation to reflect the new changes. (Sid) =back =item * 10/08/07 =over =item (1) The modules now are not dependent on the version() method of WordNet::QueryData (which is no longer reliable). Instead they now use a 'hash-code' representing a specific WordNet distribution. (Sid) =item (2) Added module WordNet::Tools which provides the hashCode and compoundify methods used by most of the other modules and utilities. (Sid) =item (3) Completely modified the build procedure to generate data files during the 'make' step instead of the 'perl Makefile.PL' step. (Sid) =item (4) Removed the WordNet version numbers appended to synsetdepths.dat and treedepths.dat. (Sid) =item (5) Added two "build" utilities -- build/Infocontent.PL and build/Depthfiles.PL -- which are run during the 'make' step to generate data files. (Sid) =item (6) The default WordNet version is now v3.0. Changed all documentation, code and examples to reflect this. (Sid) =item (7) The package now requires WordNet::QueryData version 1.46 or above. (Sid) =item (8) Revised all tests and test-keys for the new code and new version of WordNet and QueryData. (Sid) =item (9) Removed the multiple pieces of code implementing "compoundify" and moved it all into a single method in WordNet::Tools. (Sid) =back =item * 10/04/07 =over =item (1) Included a default word vectors file in the distribution and eliminated the creation of a default word vectors file at install time. (Sid) =back =item * 02/25/07 =over =item (1) Fixed documentation where module WordNet::Similarity::path was referred to as WordNet::Similarity::edge (old name). (Sid) =back =item * 01/30/07 =over =item (1) Fixed wnDepths.pl man-page to display the wnpath option consistently in the usage and the description. (Sid) =item (2) Fixed the "deep recursion" error (only with WN3.0) in the findWPSDepths() subroutine in the wnDepths.pl script. (Sid) =back =back =head2 Version 1.04 (Released 12/13/2006) =over =item * 12/13/06 =over =item (1) Fixed major bug reported in vector_pairs, where every alternate function is skipped because of a loop variable being incremented twice. (Sid) =back =item * 04/21/06 =over =item (1) The web-interface was still not working for the vector measure, because only one side of the client-server interface had been updated. Updated the similarity server with code to support both, vector and vector_pairs measures. (Sid) =item (2) Updated the description of the Gloss Vector measure in measures.html (web interface). (Sid) =back =back =head2 Version 1.03 (Released 04/14/2006) =over =item * 04/14/06 =over =item (1) Applied Ben Haskell's patch to ICFinder.pm (to make the behaviour of the probability() and IC() functions consistent with their comments). =back =item * 04/05/06 =over =item (1) Updated the names for the Extended Gloss Overlaps measure and the Gloss Vector measure in the documentation. (Sid) =back =item * 02/19/06 =over =item (1) Updated PODs for all modules. (Sid) =item (2) Added tests for POD errors and for POD coverage. (Sid) =back =item * 03/31/06 =over =item (1) Changed "hash-style" constants (Perl v5.8) to single line constants (Perl v5.6) for compatibility with Perl v5.6.0. (Sid) =back =back =head2 Version 1.02 (Released 02/07/2006) =over =item * 02/06/06 =over =item (1) Added utility rankFormat.pl for ranking the output of similarity.pl and making the output suitable for input to rank.pl (to compute Spearman's correlation coefficient) of the Text::NSP package. (Sid) =back =item * 01/15/06 =over =item (1) Fixed issue in lesk.pm where undefined values for $wc1 and $wc2 caused errors with the normalize option. (Sid) =item (2) Fixed minor UI issues in wnDepths.pl. (Sid) =back =back =head2 Version 1.01 (Released 12/21/2005) =over =item * 12/09/05 =over =item (1) Modified get_wn_info.pm with Wybo Wiersma's changes. (Sid) =item (2) Modified lesk.pm, vector.pm and vector_pairs.pm to be compatible with above changes. (Sid) =back =item * 12/07/05 =over =item (1) Updated all utilities to use WordNet 2.1 (WordNet::QueryData 1.39 or above). (Sid) =item (2) Updated all modules and test cases for WordNet 2.1. (Sid) =back =item * 12/05/05 =over =item (1) Changed order of authors in package documentation. (Sid) =back =back =head2 Version 0.16 (Released 12/12/2005) =over =item * 12/01/05 =over =item (1) Added Wybo Wiersma's super-gloss caching code to GlossFinder.pm. (Sid) =item (2) Updated documentation to reflect above changes. (Sid) =back =back =head2 Version 0.15 (Re-released 12/11/2005) =over =item * 12/11/05 =over =item (1) tar file unpacked as WordNet-Similarity for June 12, v 0.15, now unpacks as WordNet-Similarity-0.15, which is consistent with all previous versions. (Ted) =item (2) Similarity.pm version was shown as 0.14, is now 0.15. Our general convention for modules is that their version number only change when the module itself changes, so the module version number can tell you when was the last time a module changed. However, for Similarity.pm this is needlessly confusing, so it will always carry the same version number as the release. (Ted) =back =back =head2 Version 0.15 (Released 6/12/2005) =over =item * 06/10/05 =over =item (1) Fixed a minor bug in MANIFEST. (Sid) =item (2) Updated modules.pod and developers.pod to reflect new software architecture. (Jason) =back =back =head2 Version 0.14 (Released 6/9/2005) =over =item * 06/08/05 =over =item (1) Re-introduced the previous (non-pairwise-comparison) vector. (Sid) =item (2) Updated documentation and test cases to support the new vector measure. (Sid) =item (3) Added default relation file for new vector measure. (Sid) =item (4) Expunged erroneous references to LCSFinder, esp. in test scripts. (JM) =back =back =head2 Version 0.13 (Released 5/9/2005) =over =item * 04/21/05 =over =item (1) removed LCSFinder module; moved LCS methods to DepthFinder, ICFinder, and PathFinder (JM) =item (2) renamed vector measure vector_pairs (JM) =back =item * 03/24/05 =over =item (1) Modified the documentation to reflect the relation file format for vector and for lesk. (Sid) =back =item * 03/02/05 =over =item (1) Set up selective test cases for "make test", depending upon the default data files installed by user. (Sid) =back =item * 02/24/05 =over =item (1) Reinstated default relation files for vector and lesk. In case the default relation files (vector-relation.dat and lesk-relation.dat) are missing, both modules would default to the glosexample-glosexample relation. (Sid) =item (2) Modified Makefile.PL to query the user before installing default data files. (Sid) =item (3) Removed infocontent file generation code from Makefile.PL. Now Makefile.PL simply calls utilities from the /utils directory (wnDepths.pl, semCorFreq.pl and wordVectors.pl) to generate the all default data files. (Sid) =item (4) Installation process now generates a default word vectors file. The vectordb configuration variable for vector is now optional. (Sid) =item (5) Earlier, the WNHOME option was given to Makefile.PL as --WNHOME , whereas the PREFIX option was written as PREFIX=. This inconsistent (and potentially confusing) notation has now been fixed. Now, the WNHOME option is provided to Makefile.PL as WNHOME=. (Sid) =item (6) Added some basic tests for vector in t/vector.t. =back =item * 12/11/04 =over =item (1) Created WordNet::Similarity::GlossFinder.pm, a super-class of WordNet::Similarity::vector and WordNet::Similarity::lesk. (Sid) =item (2) Removed default relation file for lesk. Vector and lesk both default to glosexample-glosexample. (Sid) =back =back =head2 Version 0.12 (Released 10/29/04) =over =item * 10/29/04 =over =item (1) Added vector to the CGI interface. (JM) =item (2) Incorporated a configuration file into similarity_server.pl. (JM) =back =item * 10/28/04 =over =item (1) Removed readDB.pl. (JM) =back =item * 10/27/04 =over =item (1) Modified string overlap finding in lesk to use the Text::OverlapFinder module. Removed string_compare.pm. This fixed an old bug where the relatedness of word1 and word2 wasn't always equal to the relatedness of word2 and word1. (JM) =item (2) Updated Makefile.PL, INSTALL, and doc/install.pod to reflect new dependency on Text::OverlapFinder. (JM) =item (3) Removed lib/dbInterface.pm and lib/string_compare.pm from MANIFEST. (JM) =back =item * 10/19/04 =over =item (1) Word vectors no longer stored in a BerkeleyDB database, a plain text file is now used. Modified wordVectors.pl, WordNet::Similarity::vector to use the plain text word vectors file. New module vectorFile.pm now used to access this plain text database. Module dbInterace.pm is obsolete. (Sid) =item (2) Modified Makefile.PL to no longer check for BerkeleyDB dependency. All modules are installed. (Sid) =back =back =head2 Version 0.11 (Released 09/23/04) =over =item * 09/23/04 =over =item (1) Fixed bug in wup that allowed some relatedness scores to be greater than 1. This bug is discussed in the archives of the mailing list. (JM) =back =back =head2 Version 0.10 (Released 09/03/04) =over =item * 09/01/04 =over =item (1) Modified vector to look like the other measures. It now is derived from WordNet::Similarity.pm. (Sid) =item (2) Updated the MANIFEST. (Sid) =item (3) Fixed some minor typos in Makefile.PL. (Sid) =item (4) Added single test case (for vector) to t/access.t. (Sid) =item (5) Fixed config option name conflict in WordNet::Similarity.pm. (JM) =item (6) Fixed WNHOME and WNSEARCHDIR related bugs. (JM) =item (7) Updated documentation for the web interface. (JM) =back =back =head2 Version 0.09 (Released 05/19/04) =over =item * 05/19/04 =over =item (1) Fixed over-counting problem in *Freq.pl programs. Under certain conditions, word senses would sometimes get counted twice. (JM) =item (2) Updated *Freq.pl programs to use WordNet 2.0. (JM) =item (3) Input files to rawtextFreq.pl are now specified with the --infile option. (JM) =item (4) Improved speed of compound identification in rawtextFreq.pl by adding ',', ';', and ':' to the list of characters that we consider to be the end of a sentence (compound identification time is proportional to the square of the length of the sentence). (JM) =back =back =head2 Version 0.08 (Released 04/28/04) =over =item * 04/28/2004 =over =item (1) Created a CGI-based web interface for the relatedness modules. (JM) =back =item * 04/19/2004 =over =item (1) Fixed problem with path to Perl interpreter in Makefile.PL. This was causing problems during installation if there was no /usr/local/bin/perl. (JM) =item (2) wnDepths.pl had forgotten that on Windows some filenames are different; for example, data.noun is noun.dat. (JM) =back =back =head2 Version 0.07 (Released 03/24/04) =over =item * 03/23/2004 =over =item (1) In /t, save diff files between 0.06 and 0.07. Make sure to run diff tests for path/0.07 and edge/0.06. =back =item * 03/16/2004 =over =item (1) make sure that every .pm and .pl file has the same GNU copyleft language. Use PathFinder.pm as a template. =item (2) make sure that documentation is clear that vector and lesk require different format relation files (ie they are not interchangeable). =item (3) convert README into a series of pod documents in doc directory. In the intro.pod, provide a table of contents like structure (much like perldoc perl does). Make sure that each pod documents follows the cpan style (name, synopsis, etc.) This should be true of any pod documentation in the package. =item (4) Modify INSTALL to describe local install correctly. In particular, the description of how to do a 'use lib' or -I may need adjustment. =back =item * 03/12/2004 =over =item (1) Make developers.pod into a self contained document that provides a step by step tutorial on how to write a measure of relatedness. The file NewStats.txt in NSP provides an example of the style of presentation that is expected. =item (2) developers.pod should be a tutorial that explains how to create a new measure. It should take the reader through a complete example, such as creating a measure that returns the sum of the information content of the concpets found in the shortest path between two concepts. This should include an example of how to use all of the available configuration options, and also adding a new one. =back =item * 03/11/2004 =over =item (1) document measure modules (lch.pm, wup.pm, etc.) with information about effect of hypo root node. (Take discussion from email explaining why it has an effect, and why it doesn't have an effect) and make it a part of the .pm perldoc. This will eventually be used in thesis writing, so it should be complete and detailed. Of particular important is the behavior of lch.pm, but all of the modules should have their expected behaviour with and without the hypo root node clearly documented. Also, you should note what the behavior was in 0.06 for both nouns and verbs, and if this has changed. =back =item * 03/09/2004 =over =item (1) lch.pm does not yet support not having a hypo root. Remember that the lack of hypo root will change (potentially) the max path length found for each taxonomy. =back =item * 03/08/2004 =over =item (1) depth finding code should be contained with DepthFinder.pm. We should not do any depth finding on the fly, rather that should all be precomputed (like we do info content). That includes the depth of individual concepts, and the max depths of taxonomies. =item (2) When wup.pm encounters two or more paths to the root, the trace output "condenses" those paths into a single path. It would be better to show all paths in the trace (as res does, for example). Also, make sure that the depth reported in such cases is always the minimum (shortest path to root). =back =item * 03/05/2004 =over =item (1) Modify wnDepths such that it shows both the depths of individual concepts, as well as the max distance from a root node. In the case of multiple inheritance, wndepths should show the depth of the concept in each case, and also the relevant root node. wnDepths should sort these depths from shortest to longest. The output of wndepths should be formatted like infocontent.dat, anticipating an eventual merger. =back =item * 03/02/2004 =over =item (1) in docs, update/replace current discussion of modules. Include example usage as well. Make sure that path length is clearly defined for lch, edge, and wup. =back =item * 02/25/2004 =over =item (1) In PathFinder.pm, Infocontent.pm, Similarity.pm, and LCSFinder.pm each function should be documented in perldoc form such that their input, output and basic functionality is described. This should then appear in the DESCRIPTION portion of the perldoc. The SYNOPSIS should contain examples or templates of each function being used. =back =item * 02/23/2004 =over =item (1) redo random pairs testing such that we have 60 noun-noun pairs, 25 verb-verb pairs, and 15 mixed pairs. =back =item * 02/20/2004 =over =item (1) Revisit the distance versus similarity issue in jcn.pm. It maybe be that simply inverting the distance is too extreme a solution. One possibility is to make it a linear transformation via maxdist - dist instead. (JM - we'll stick with inverting the distance, but added a discussion of this issue to the documentation) =back =item * 02/18/2004 =over =item (1) document all multiple inheritance issues that are being handled for measures. =back =item * 02/16/2004 =over =item (1) validateSynset should check wps format fairly closely, and issue descriptive errors if the wps is ill formed. Words can apparently be about anything (except #) but pos should be lower case nvra, and senses should be digits. Error messages should point out which field is the problem, or if there are too few or too many fields. =item (2) place all hypo root handling node code in PathFinder.pm. The measures should not have any hypo root handling code in them. =item (3) PathFinder.pm should include a function getAllPaths.pm that returns all paths between two concepts, their length, and their "tops" (the candidate LCSs). This should be used as the main source of input for the getLCS* functions, and for getShortestPath. =item (4) remove all "input verifcation" code from the measures. That should be inherited from Similarity.pm. =item (5) There is replicated code in the measure modules that checks validity of input. This should be removed to a common module that can be called by all of the measures. Any other replicated code should be removed as well. The goal of 0.07 is to largely eliminate replicated code via the use of inheritance, and to make the writing of new measures simpler. =back =item * 02/13/2004 =over =item (1) add pod/perldoc to lib/ICFinder.pm. Should also be done for all other files as they are modified for other reasons. In particular, introductory material that appears in source code comments, author information, GPL, etc. should be moved into pod and removed from source code comments. See similarity.pl for an example. =item (2) path should use getShortestPath from PathFinder.pm. =back =item * 02/09/2004 =over =item (1) getLCSDepth, getLCSInfo, getLCSPath should appear in LCSFinder.pm, which should inherit from both ICFinder and Pathfiner. =item (2) The measures (lch, path, jcn, lin, res, wup) should default to having the hypo root node turned on (for both nouns and verbs). This will eventually be true of hso, but is not currently. hypo root nodes could also be used for lesk and vector, although they are not currently. =back =item * 02/04/2004 =over =item (1) Wps and offsets will be supported internally. The user can request either mode via an option to getRelatedness. offset is our default. profiling has shown wps to be somewhat faster, in that it makes fewer calls to getSense, although it does make some. For input, we only support wps. For trace output we support wps and offset. For output we support wps and offset. =back =item * 01/29/2004 =over =item (1) modify option in config files such that an option without a value reverts to the default in all cases (except vectordb). =back =item * 01/24/2004 =over =item (1) Provide support for undefined values in the path finding and info content measures (path, wup, lch, res, lin, jcn). If two concepts are not in the same taxonomy then an error should be issued and a large negative integer should be returned. This can occur in two cases, between the same part of speech (noun-noun, verb-verb), or between nouns and verbs. Distinct error messsages should be indicated in both cases. =back =item * 01/20/2004 =over =item (1) Clean up configuration file examples (in samples). Make them consistent by having a master list (all-options.conf) that is what we make changes to. Then specific example files can be created via copy and paste. Make sure all possible options for a measure are included, and that the explanations describe all possible values as well as default handling. (TDP updated all-options.conf on 12/10/03, use this as source of cut and paste). =back =item * 01/19/2004 =over =item (1) Create test scripts that can be run to verify the correctness of output - they should include "correct" answers that can be compared to (automatically) and rerun as the system changes. We should use the CPAN module Test::More, and create .t files in a /t directory that test specific situations/problems, etc. The .t files themselves should be documented with an explanation of what is being tested. We should have lots of smaller, specific .t tests (rather than a few big test files). Whenever a bug is found and fixed, a .t file should be created that tests the fix, and this should be mentioned in the source code comments where the fix is made (this fix is tested by t/xyz.t). Make sure that the testing system can be easily extended/modified, and that it can support the use of multiple input files and configuration files. We should have multiple *.t files to run our tests, and each module and utility should have at least its own *.t file (maybe more than one in some cases). We should also have *.t files that are dedicated to particular situations that affect a number of measures (like what happens when info content is zero for one concept, what happens if one of the concepts being compared is the lcs of the other, what if the two concepts are the same (self similarity), and so forth. =item (2) Test cases for configuration file handling should include: repeated options in configuration file, as in trace::0 trace::1 bad values in configuration file, as in trace::nothankyou bad options in configuration file, as in tracer::0 =item (3) Test cases for similarity.pl should include: ill formed file input for similarity.pl, as in cat#dog#1 cat#n#2 cat#n#n cat#n#2 cat =item (4) Test cases for measures should include: show that wps and offset methods of path finding are equivalent check trace output for each of the measures. use wps format, as that is subject to fewer changes than offsets. a "big" file of word pairs (maybe 100 pairs) that run all the measures and compare values to what is obtained in 0.6. If there are differences, let's see what they are. =item (5) Test cases for information content programs should include: an information content file based on one of our resident text files that is large enough to be interesting (readme, gpl, etc.) as computed in 0.6/0.7 (should be the same). This can be used as a reference point when we make changes in future. Information content computed with a very small number of concepts, to expose the counting problem that ted mentions below. =item (6) Test cases for wnDepth... Generate output for 0.07 to use as a point of reference. A few specific manual checks would be good too (leather_carp, entity, etc.) =item (7) run tests to determine where the system now provides different results from version 0.06 - make sure to document these cases (that are different). =back =item * 01/12/2004 =over =item (1) document configuration options extensively in a separate pod called doc/config.pod. Organize such that you have options that are used with all measures, and then those that are used with certain classes of measures. Then, use this as a master copy to update .pm files with. =back =item * 01/09/2004 =over =item (1) modify option handling such that multiple occurrences of an option in a config file cause an error. For example trace:: trace::1 should cause an error. =back =item * 12/17/2003 =over =item (1) SemCor1.7Freq.pl and SemTagFreq.pl need to be renamed. They are now called semCorRawFreq.pl and SemCorFreq.pl. semCorRawFreq.pl counts without sense tags and SemCorFreq.pl counts the sense tags. (TDP) =back =item * 12/09/2003 =over =item (1) In similarity.pl cache error strings that indicate that two input synsets are from different parts of speech so that we only print out a warning once for each unique word1#pos1 word2#pos2 combination (JM) =item (2) =over =item (a) Enhance similarity.pl file handling (for input files). Comments should be allowed - this will help in creation of test data (we can explain in the comment what "case" is being tested by a particular set of pairs. Use standard perl commenting style line starting with a # is a comment. Note that I don't think we can use the convention of # anywhere in a line as being the start of a comment (due to w#p#s) but I think any line that starts with a # can be safely treated as a comment. (JM -- we are using // to indicated the start of a comment) =item (b) Enhance similarity.pl file handling (for input files). At present if a single word (not a pair) appears on a line, no error is issued. It silently ignores this case. This should result in an error to the effect that the input format is invalid, only one word. Also, I'm not sure what happens if you have more than two words on a line. An error of some sort would also be necessary in that case. Also, I am not sure if similarity.pl checks to see that the words pairs are "well formed", that is to say do they adhere to the word, word#pos, or word#pos#number format. It would be good to have a simple check that verifies we have alphanumeric words, pos of n, v, a, or r, and numeric numbers. (JM) =back =back =item * 12/08/2003 =over =item (1) Clean up configuration file examples (in samples). Make them consistent by having a master list (all-options.conf) that is what we make changes to. Then specific example files can be created via copy and paste. Make sure all possible options for a measure are included, and that the explanations describe all possible values as well as default handling. (JM)(TDP updated all-options.conf on 12/10/03, use this as source of cut and paste). =item (2) Determine if it is feasible (not too difficult or time consuming) to modify --version option so it can display both the version of similarity.pl and the version of the module used when --type is specified. (JM -- version will show module version as well if a module is specified) =back =item * 12/05/2003 =over =item (1) all configuration options are now printed to traceString after module initialization. (JM) =item (2) explain the distinction between compounds and collocations raised in sample README. (Drop the distinction, and clarify what we mean by Wordnet compounds. TDP Dec 3). (JM) =back =item * 12/04/2003 =over =item (1) document caching for random (normally random uses an unlimited cache size) (JM -- random now uses the same default as all other measures) =item (2) determine a reasonable default cache size. Should not be unlimited. Current default is 1000, maybe it can be increased to 5000 or 10000. Let lesk with trace be the standard as to what is reasonable. (JM -- default is now 5,000). =item (3) Improve error handling when processing config files. Make sure the values specified are valid and that filenames refer to extant files. All options should allow the value to be omitted, in which case the default is used. (JM) =back =item * 12/01/2003 =over =item (1) Adjust Makefile.PL to account for new contents of samples directory. Added entries to MANIFEST as well. (JM) =item (2) update samples/sample.pl to run with the new files (and organization) provided in the samples directory. This was also a problem in 0.06, where it did not run for hso properly due to a mismatch in the name specified in sample.pl and the configuration file. =item (3) Rename infocontent.dat in Makefile.PL to use our standard name for semcor information content files. Name should reflect options used in computing information content values (if any). JM =item (4) relation.dat is in lib/WordNet. Should be referred to as lesk-relation.dat. Should also have vector-relation.dat I would think. (if not, what does vector do?). JM (vector doesn't try finding a default relation file--it fails silently). =item (5) /sample/vector-relation.dat is wrong. Calls itself LeskRelationFile. JM =item (6) In intro.pod, provide instruction on how to convert to html or whatever if user wishes (just point them to documentation that describes this elsewhere even). JM =back =item * 11/28/2003 =over =item (1) remove wordnet 1.7.1 compounds from samples directory. (TDP) =item (2) change comment in Similarity.pm to explain the pluses and minuses of using/not using a unique root node. (JM) =back =item * 11/26/2003 =over =item (1) added info content files in samples/Infocontent =item (2) changed version numbers to 0.07 in all modules and utils =item (3) fixed bug in wup: if user supplies car#n#1 and auto#n#1, the LCS found by wup is motor_vehicle#n#1, not car#n#1 =item (4) added POD to all programs in /samples =back =item * 11/24/2003 =over =item (1) added documentation (in the form of POD) to /doc =back =item * 11/21/2003 =over =item (1) added /doc directory to contain documentation =back =item * 11/18/2003 =over =item (1) ensured that each measure initializes a part-of-speech list in _initialize =item (2) all measures (except vector) now use fetchFromCache and storeToCache =item (3) updated README: =over =item (a) Replaces most references to WordNet 1.7.1 with 2.0 =item (b) Add some documentation on how to write a new measure =back =item (4) added an INSTALL file =item (5) cleaned up /samples. relation.dat is now named lesk-relation.dat and added vector-relation.dat. A sample config file is also provided for each measure (in /samples/config-files) =back =item * 11/15/2003 =over =item (1) updated jcn, hso, random, and lesk to use the funcitions that have been moved to Similarity.pm (such as the cache management functions). =item (2) cleaned up the /samples directory. Removed outdated files. Put sample config files in samples/config-files. Added README in /samples. =back =item * 11/12/2003 =over =item (1) Added fetchFromCache() and storeToCache() to Similarity.pm to make caching easier and cleaner. =item (2) Updated wup, edge, lch, res, and lin to use fetchFromCache() and storeToCache(). =back =item * 10/25/2003 =over =item (1) Reduced the amount of duplication code in the measure modules by moving some common code to WordNet::Similarity. WordNet::Similarity is now a base class for all the measures. Also added a module called infocontent.pm from which all information content measures are descended (i.e., res, lin, jcn). =item (2) Removed @ symbol from all email addresses in all files (I think). This might help keep spammers from harvesting our email addresses. =back =back =head2 Version 0.06 =over =item * 10/18/2003 =over =item (1) Removed dependence of the vector measure on PDL. Implemented "in-house" sparse vector manipulation functions. =item (2) Modified the README with updated documentation of similarity.pl (--interact option) and wordVectors.pl. =back =item * 10/15/2003 =over =item (1) Changed Makefile.PL so that it checks for version 1.30 of QueryData =back =item * 10/13/2003 =over =item (1) Added "maxCacheSize" option to all measures. =item (2) Added "maxCacheSize" option info to the man/pod documentation. =item (3) Used the new dataPath() method of QueryData 1.31 in all the utilities to obtain the path of the WordNet data files. =item (4) Modified Makefile.PL to check for PDL and BerkeleyDB dependency during installation. vector.pm is not installed on failed dependencies. =back =item * 10/11/2003 =over =item (1) Replaced instances of deprecated WordNet::QueryData::query with WordNet::QueryData::queryWord in hso.pm =item (2) made hso.pm check QueryData version. queryWord was broken in QueryData 1.29 and earlier =item (3) added support for new relations in WordNet 2.0 to get_wn_info.pm =item (4) updated test scripts to work with WN 2.0 (and WN 1.7.1) =back =item * 10/06/2003 =over =item (1) Added rootNode option to wup.pm =back =item * 09/27/2003 =over =item (1) Fixed syntax error in wordVectors.pl. =item (2) Added readDB.pl to utils. =item (3) Changed contact information in docs. =item (4) Re-organized the samples subdirectory. =item (5) Fixed typo in random.pm. =item (6) Updated the MANIFEST. =back =item * 09/21/2003 =over =item (1) Updated POD for WordNet::Similarity::wup =item (2) Added option to wup to specify a cache size in a configuration file. =item (3) similarity.pl now 'use's QueryData 1.30 or later. Previous versions of QueryData will not work. t/access.t also 'use's QueryData 1.30. get_wn_info.pm and lesk.pm both check for QueryData 1.30 and will die if it not found. =item (4) Reorganized the bibliography in README and slightly re-worded part of the introduction. =back =item * 09/18/2003 =over =item (1) Added new Wu Palmer measure of similarity (lib/WordNet/Similarity/wup.pm) =item (2) Updated README to mention wup =item (3) Added t/wup.t =item (4) Updated POD for WordNet::Similarity to mention wup =item (5) Updated the help message of similarity.pl to mention wup =item (6) Added t/wup.t and lib/WordNet/Similarity/wup.pm to MANIFEST =back =item * 09/05/2003 =over =item (1) Added '--interact' option to similarity.pl. =item (2) Changed the structure of the Vector Relation File. =item (3) Fixed a minor bug in similarity.pl. (s///g) =item (4) Updated the perldocs for the measures. =item (5) Incorporated some new features into the 'wordVectors.pl' utility. These features were used for thesis experiments. =item (6) Added documentation about the Lesk and Vector relation files (they have different formats now). =back =back =head2 Version 0.05 =over 4 =item * 06/03/2003 =over =item (1) Added new measure of semantic relatedness, based on co-occurrence vectors of WordNet glosses. =item (2) Set up the package so that similarity.pl and the other perl utilities get installed in "/usr/local/bin". =item (3) Complete rewrite of similarity.pl with cleaner code and added functionality: =over =item (a) Multiple parts of speech can be specified as car#nv (noun and verb forms of car) or cool#nar (noun, adjective and adverb forms of cool). =item (b) Word senses can now be specified as car#n#2, jump#v#2, etc. =item (c) Added functionality to similarity.pl to use a local install of WordNet::Similarity modules (in non-standard directories). =item (d) Output of similarity.pl now specifies the senses that represent the relatedness of two words. =back =item (4) Enforced limit on the cache size of modules. =item (5) Updated README to reflect the changes and to specify options for local installs of similarity.pl and the other utilities. =item (6) Fixed the perl docs (remove leading spaces). =item (7) Added mailing list address to documentation -- (http://groups.yahoo.com/group/wn-similarity). =item (8) Improved jcn and lin tracing ("bird-crane" problem obvious now). =item (9) Added new utility wordVectors.pl required for WordNet::Similarity::vector module. =back =back =head2 Version 0.04 =over 4 =item * 05/02/2003 =over =item (1) *Fixed* newline in traces. =item (2) *Fixed* blank line bug in brownFreq.pl. =item (3) *Fixed* "--offset" option bug in similarity.pl. =item (4) *Fixed* lin measure non-normalized scores... added zero infocontent handling in jcn and lin. =item (5) New utility rawtextFreq.pl, to generate information content files from plain text. =item (6) similarity.pl supports option to specify part-of-speech of input words while measuring relatedness. =item (7) Added option to specify (conifuration / information content) file in similarity.pl. =item (8) Added Resnik counting option to the information content generation utilities. =item (9) More documentation on information content utilities. =item (10) Added Add-1 smoothing option to the information content generation utilities. =back =back =head2 Version 0.03 =over =item * 03/10/2003 =over =item (1) Removed trace bug in hso.pm. =item (2) Added test cases for all modules. =back =back =head2 Version 0.01 =over =item * 02/10/2003 =over =item (1) Created CPAN modules from distance ver 0.11. =item (2) Modules are completely object oriented. =item (3) Added Adapted Lesk semantic relatedness measure -- lesk.pm. =item (4) Added simple edge counting semantic relatedness measure -- edge.pm. =item (5) Added a random relatedness measure -- random.pm. =item (6) jcn, res and lin measures now support verb hierarchies. =item (7) Information content files can now be specified as parameters to the modules. =item (8) Tools provided to build information content files from various publicly available corpora. =item (9) Various parameters now control the behavior of the modules. These parameters are passed to the modules through 'configuration files'. =back =back =head1 AUTHORS Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Jason Michelizzi =head1 SEE ALSO L =head1 COPYRIGHT Copyright (c) 2005, Ted Pedersen, Siddharth Patwardhan, Satanjeev Banerjee and Jason Michelizzi Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. Note: a copy of the GNU Free Documentation License is available on the web at L and is included in this distribution as FDL.txt. =cut WordNet-Similarity-2.05/doc/developers.pod0000644000175000017500000001460611015336622016715 0ustar sidsid# run perldoc on this file to get nicely formatted output. =head1 NAME DEVELOPERS - [documentation] Instructions on how to write a new measure for WordNet::Similarity =head1 WRITING A NEW MEASURE All the existing measures are written in an object-oriented manner, and if you are writing your own measure, you will need to write your measure in a like manner. If object-oriented Perl is new to you, see the perldoc pages relating to object-oriented Perl: perlboot, perltoot, perltooc, and perlbot. The existing measure modules are found in lib/WordNet when you unpack the source tarball. The following methods are defined in WordNet::Similarity and are available to you: sub new sub initialize sub configure sub traceOptions sub getTraceString sub getError sub getRelatedness sub printSet sub fetchFromCache sub storeToCache sub traceOptions If you are writing a measure based on information content, the module WordNet::Similarity::ICFinder defines some extra methods: sub probability sub IC sub getFrequency And if you are writing a measure that does some sort of path-finding, the WordNet::Similarity::PathFinder module supplies some extra methods as well. sub getShortestPath sub getAllPaths If you are writing a measure where you need to know the depth of a synset in the WordNet taxonomies or the maximum depth of a particular taxonomy, the WordNet::Similarity::DepthFinder module has methods that will be useful. sub getSynsetDepth sub getTaxonomyDepth sub getTaxonomyRoot If you want to find LCSs (Least Common Subsumers), there are three different ways of doing so, depending upon whether you want to use path length, depth, or information content. The three methods for finding LCSs are: sub getLCSbyPath sub getLCSbyDepth sub getLCSbyIC They are found in WordNet::Similarity::PathFinder, WordNet::Similarity::DepthFinder, and WordNet::Similarity::ICFinder. For writing a measure that uses glosses (like vector, vector_pairs, and lesk), the WordNet::Similarity::GlossFinder module may be useful. The documentation for the respective modules has detailed descriptions of how each methods works, what parameters each one expects, etc. =head1 STEP BY STEP INSTRUCTIONS The following steps should get you started. =over =item 1. Create a file ending in .pm, such as newmeasure.pm. =item 2. Declare the name of the package. This should be the same name as your filename (except for the .pm): package newmeasure; =item 3. We need to 'use' WordNet::Similarity, or a sub-class of it. We also need to declare that our module is-a (is inherited from) WordNet::Similarity. We do this by adding WordNet::Similarity to the ISA array in your module. If your measure uses information content, then you probably want to use WordNet::Similarity::ICFinder instead. If you are doing some type of path-finding, then you might want to use WordNet::Similarity::PathFinder. Both PathFinder and ICFinder are sub-classes of Similarity, so if you put one of them in your @ISA array, you don't need WordNet::Similarity. In our case, let's try making a new information content measure: use WordNet::Similarity::ICFinder; our @ISA = qw/WordNet::Similarity::ICFinder/; =item 4. The Similarity.pm module provides a 'new' method for us, and it does everything for us that we need. =item 5. You need to write a getRelatedness function that actually computes the relatedness of two word senses. In our example here, we'll define relatedness as the average information content of the two input synsets. # a simple example sub getRelatedness { my $self = shift; # $wps1 and $wps2 need to be strings in # word#part_of_speech#sense format my $wps1 = shift; my $wps2 = shift; my $ref = $self->parseWps ($wps1, $wps2); # if ref is not a reference, that means an error has occured; # parseWps will have already set the error level to non-zero # and generated an error string ref $ref or return $ref; # now from ref, get all the elements of the array my (undef, $pos1, undef, $offset1, undef, $pos2, undef, $offset2) = @$ref; my $score; # first we check to see if relatedness was already computed if ($self->{doCache}) { $score = $self->fetchFromCache ($wps1, $wps2); defined $score and return $score; } my $wn = $self->{wn}; # get reference to WordNet::QueryData # here's where we do the real work of finding relatedness my $ic1 = $self->IC ($offset1); my $ic2 = $self->IC ($offset2); $score = ($ic1 + $ic2) / 2; # if tracing in enabled, print some information to traceString if ($self->{trace}) { $self->{traceString} .= "IC("; $self->printSet ($pos1, 'offset', $offset1); $self->{traceString} .= ") = $ic1\n"; $self->{traceString} .= "IC("; $self->printSet ($pos2, 'offset', $offset2); $self->{traceString} .= ") = $ic2\n"; } $self->storeToCache ($wps1, $wps2, $score) if $self->{doCache}; return $score; } =back =head1 SUMMARY You should follow the same conventions for error handling and tracing as the other measure modules do. Be sure to support cache as well (as demonstrated above). If you would like to contribute to the project, please see our SourceForge page: http://wn-similarity.sourceforge.net as well as our current todo list (in doc/todo.pod). We especially welcome contributions of new measures of relatedness! =head1 SEE ALSO L Mailing list: L Project Home page: L =head1 AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Jason Michelizzi =head1 COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen, Siddharth Patwardhan, Satanjeev Banerjee and Jason Michelizzi Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. Note: a copy of the GNU Free Documentation License is available on the web at L and is included in this distribution as FDL.txt. =cut WordNet-Similarity-2.05/doc/modules.pod0000644000175000017500000003272511015336622016217 0ustar sidsid# run perldoc on this file to get nice, formatted documentation =head1 NAME modules - [documentation] Overview of WordNet::Similarity measure modules =head1 DESCRIPTION All the modules that will be installed in the Perl system directory are present in the '/lib' directory tree of the package. These include the semantic relatedness modules -- jcn.pm, res.pm, lin.pm, lch.pm, hso.pm, lesk.pm, vector_pairs.pm, wup.pm, path.pm and random.pm -- present in the /lib/WordNet/Similarity subdirectory and the supporting modules PathFinder, ICFinder, DepthFinder, GlossFinder, et_wn_info.pm, and string_compare.pm. There also exists a WordNet/Similarity.pm module that currently serves as a base class for all the relatedness modules and contains Perl documentation. All these modules, once installed in the Perl system directory, can be directly used by Perl programs. A UML diagram showing how all the classes are organized is available from L =head2 Using the relatedness modules The semantic relatedness modules in this distribution are built as classes that define the following methods: new() getRelatedness() getError() getTraceString() =head3 new() The first thing that is done in order to use one of the semantic relatedness measures is to create an object of the measure. This is done by calling the 'new' method of that measure or module. For all the semantic relatedness measures provided in this package, the 'new' method takes two parameters -- =over =item (a) a WordNet::QueryData object (REQUIRED) =item (b) the name of a configuration file for that module (Optional) =back This method initializes an object of the requested measure, using the configuration file data, or with default values if a configuration file is not provided. A reference to this object is returned by the 'new' method and must be saved by the calling program, if any of the other methods of this module are to be called. It is possible to create multiple objects of the same module (possibly initialized differently by specifying different configuration files for each). The format of the configuration files is discussed later in this section. An 'undef' value returned by the 'new' method, indicates that it was unable to create an object. It is also possible that non-fatal errors occur during the creation of the object. In such a case an object is created by the 'new' method using default conditions. However, a non-fatal error condition flag is set within the object, which can be retrieved using the getError() method. It is advisable to check for this error condition after the creation of every such object. =head3 getRelatedness() The 'getRelatedness' method is called on the created object to determine the semantic relatedness of two concepts (synsets in WordNet) as computed by that measure. The input parameters are two WordNet synsets, represented in the word#pos#sense format returned/used by WordNet::QueryData. In this format each synset is represented by a word from that synset, its part-of-speech and its sense number. For example, if the second sense of 'teacher' as a noun occurs in a synset containing synonyms for 'teacher', then this synset can be represented by the string 'teacher#n#2'. The 'getRelatedness' method takes as input two strings of this form and returns a floating point value, which is the semantic relatedness of these (as computed by the measure). =head3 getError() During a call to either the 'new' method or the 'getRelatedness' method of a measure, if a fatal or non-fatal error occurs, the module sets an error flag within the created object and sets an error string within (the exception to this is when the module is unable to create an object upon a call to the 'new' method, in which case it simply returns 'undef'). Both the error condition flag and the error string can be retrieved using the 'getError' method on the created object. The method is called without any parameters and it returns an array containing the error flag as the first element and the error string as the second element. The error flag can take the values 0, 1 or 2. A value of 0 indicates that there was no error or warning since the last call to 'getError'. 1 indicates that there was/were non-fatal error(s) (warnings) since the last call to 'getError'. A value of 2 usually indicates that the errors were serious enough to warrant the termination of the program. However, how these errors are handled is completely upto the programmer writing the Perl program. It is advisable that the error flag be checked after every call to either 'new' or 'getRelatedness', but this is not a necessary step and the error condition may be tested at less regular intervals also. =head3 getTraceString() If traces are enabled, a trace string generated during the last call to the 'getRelatedness' method is stored within the object. This trace string can be retrieved using the 'getTraceString' method. This method is called with no parameters and returns a scalar containing the most recently generated trace string. By default traces are not enabled. Traces can be enabled by specifying this as an option in the configuration file for the measure. Instructions for writing configuration files for the measures follow later in this section. =head2 Examples of typical usage To create an object of the Resnik measure, we would have the following lines of code in the Perl program. use WordNet::Similarity::res; $object = WordNet::Similarity::res->new($wn, '/home/sid/resnik.conf'); The reference of the initialized object is stored in the scalar variable '$object'. '$wn' contains a WordNet::QueryData object that should have been created earlier in the program. The second parameter to the 'new' method is the path of the configuration file for the resnik measure. If the 'new' method is unable to create the object, '$object' would be undefined. This, as well as any other error/warning may be tested. die "Unable to create resnik object.\n" if(!defined $object); ($err, $errString) = $object->getError(); die $errString."\n" if($err); To create a Leacock-Chodorow measure object, using default values, i.e. no configuration file, we would have the following: use WordNet::Similarity::lch; $measure = WordNet::Similarity::lch->new($wn); To find the semantic relatedness of the first sense of the noun 'car' and the second sense of the noun 'bus' using the resnik measure, we would write the following piece of code: $relatedness = $object->getRelatedness('car#n#1', 'bus#n#2'); To get traces for the above computation: print $object->getTraceString(); However, traces must be enabled using configuration files. By default traces are turned off. =head2 Configuration files The behavior of the measures of semantic relatedness can be controlled by using configuration files. These configuration files specify how certain parameters are initialized within the object. A configuration file may be specified as a parameter during the creation of an object using the new method. The configuration files follow a fixed file format. Every configuration file starts with the name of the module ON THE FIRST LINE of the file. For example, a configuration file for the Resnik module will have on the first line 'WordNet::Similarity::res'. This is followed by the various parameters, each on a new line and having the form 'name::value'. The 'value' of a parameter is optional (in case of boolean parameters). In case 'value' is omitted, we would have just 'name::' on that line. Comments are supported in the configuration file. Anything following a '#' is ignored in the configuration file. Sample configuration files are present in the '/samples' subdirectory of the package. Each of the modules has specific parameters that can be set/reset using the configuration files. Please read the manpages or the perldocs of the respective modules for details on the parameters specific to each of the modules. For instance, 'man WordNet::Similarity::res' or 'perldoc WordNet::Similarity::res' should display the documentation for the Resnik module. =head2 Information Content Three of the measures provided within the package require information content values of concepts (WordNet synsets) for computing the semantic relatedness of concepts. Resnik (1995) describes a method for computing the information content of concepts from large corpora of text. In order to compute information content of concepts, according to the method described in the paper, we require the frequency of occurrence of every concept in a large corpus of text. We provide these frequency counts to the three measures (Resnik, Jiang-Conrath and Lin measures) in files that we call information content files. These files contain a list of WordNet synset offsets along with their part of speech and frequency count. The files are also used to determine the topmost node of the noun and verb 'is-a' hierarchies in WordNet. The information content file that should be used by a module is specified in the configuration file of that module. If no information content file is specified, then the default information content file, generated at the time of the installation of the WordNet::Similarity modules, is used. A description of the format of these files follows. The FIRST LINE of this file must contain the version of WordNet that the file was created with. This should be present as a string of the form wnver:: For example, if WordNet version 2.1 with a hash-code LL1BZMsWkr0YOuiewfbiL656+Q4 was used for creation of the information content file, the following line would be present at the start of the information content file. wnver::LL1BZMsWkr0YOuiewfbiL656+Q4 The rest of the file contains on each line a WordNet synset offset, part-of-speech and a frequency count, in the form [ROOT] without any leading or trailing spaces. For example, one of the lines of an information content file may be as follows. 63723n 667 where '63723' is a 'noun' synset offset and 667 is its frequency count. Suppose the noun synset with offset 1740 is the root node of one of the noun taxonomies and has a frequency count of 17625. Then this synset would appear in an information content file as follows: 1740n 17625 ROOT The ROOT tags are extremely significant in determining the top of the hierarchies and must not be omitted. Typically, frequency counts for the noun and verb hierarchies are present in each information content file. A number of support programs to generate these files from various corpora are present in the '/utils' directory of the package. A sample information content file has been provided in the '/samples' directory of the package. NOTE: Using the "Resnik" counting it is possible to get fractional values for the frequency counts. =head2 Depths Files The lch and wup measures make use of auxiliary files to determine certain types of depths information. The lch measure uses a file to determine the maximum depth of each WordNet taxonomy. The wup measure used a file to determine the depth of each synset in its taxonomy. The files used by these measures are produced by the wnDepths.pl program that is in the 'utils' directory. The file used by lch has the version of WordNet that was used when generating the file on the first line of the file, similar to the information content files. For example: wnver::LL1BZMsWkr0YOuiewfbiL656+Q4 The remaining lines of the file give the part of speech for each taxonomy, the offset of the root of each taxonomy, and the maximum depth of the taxonomy. For example, the taxonomy that has the root entity#n#1 has this line: n 00001740 17 The file used by wup has also has the version of WordNet that was used when generating the file on the first line of the file. The remaining lines have the part of speech for the synset, the offset of the synset, and a list of depths and offsets of taxonomy roots. For example, the synset amphibious_landing#n#1, has the following line: n 00052540 4:00025950 4:00026194 5:00026194 The first item is the part of speech, the second is the offset of amphibious_landing#n#1. The next item, '4:00025950' means that there is a path from amphibious_landing#n#1 to the taxonomy root 00025950 (event#n#1) whose length is 4. The next item means that there is a path from amphibious_landing#n#1 to a different taxonomy root, 00026194 (act#n#2) whose length is 4. The last item means that there is another path to act#n#2 whose length is 5. From this we can infer that the synset belongs to two different taxonomies, and there there are multiple paths to the root of the second taxonomy. =head1 SEE ALSO L Mailing list: L Project Home page: L =head1 AUTHORS Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Jason Michelizzi =head1 COPYRIGHT AND LICENSE Copyright (c) 2005-2008, Ted Pedersen, Siddharth Patwardhan, Satanjeev Banerjee, and Jason Michelizzi Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. Note: a copy of the GNU Free Documentation License is available on the web at L and is included in this distribution as FDL.txt. =cut WordNet-Similarity-2.05/README0000644000175000017500000002550211015336623014152 0ustar sidsidNAME README - [documentation] Introduction to WordNet::Similarity SYNOPSIS There are a number of documentation files covering different aspects of WordNet::Similarity available: * intro.pod Introduction to WordNet::Similarity (this document) * install.pod How to install * utils.pod How to use the utility programs in /utls * modules.pod How the measure modules are designed * developers.pod How to write your own measure of semantic relatedness * config.pod How to set configuration options for the measures You can use pod2html, pod2latex, pod2man, or pod2text to get this documentation in a different format. See the man pages for pod2html, etc. These translators should come with Perl, but you can also download them from . DESCRIPTION This package consists of Perl modules along with supporting Perl programs that implement the semantic relatedness measures described by Leacock & Chodorow (1998), Jiang & Conrath (1997), Resnik (1995), Lin (1998), Hirst & St-Onge (1998), Wu & Palmer (1994), the extended gloss overlap measure by Banerjee and Pedersen (2002), and two measures based on context vectors by Patwardhan (2003). The details of the vector measure are described in the Master's thesis work of Patwardhan (2003) at the University of Minnesota Duluth, and the vector_pairs measure is derived from that. The Perl modules are designed as objects with methods that take as input two word senses. The semantic relatedness of these word senses is returned by these methods. A quantitative measure of the degree to which two word senses are related has wide ranging applications in numerous areas, such as word sense disambiguation, information retrieval, etc. For example, in order to determine which sense of a given word is being used in a particular context, the sense having the highest relatedness with its context word senses is most likely to be the sense being used. Similarly, in information retrieval, retrieving documents containing highly related concepts are more likely to have higher precision and recall values. A command line interface to these modules is also present in the package. The simple, user-friendly interface returns the relatedness measure of two given words. A number of switches and options have been provided to modify the output and enhance it with trace information and other useful output. Details of the usage are provided in other sections of this README. Supporting utilities for generating information content files from various corpora are also available in the package. The information content files are required by three of the measures for computing the relatedness of concepts. The following sections describe the organization of this software package and how to use it. A few typical examples are given to help clearly understand the usage of the modules and the supporting utilities. SEMANTIC RELATEDNESS We observe that humans find it extremely easy to say if two words are related and if one word is more related to a given word than another. For example, if we come across two words -- 'car' and 'bicycle', we know they are related as both are means of transport. Also, we easily observe that 'bicycle' is more related to 'car' than 'fork' is. But is there some way to assign a quantitative value to this relatedness? Some ideas have been put forth by researchers to quantify the concept of relatedness of words, with encouraging results. A number of different measures of relatedness have been implemented in this software package. These include a simple edge counting approach and a random method for measuring relatedness. The measures rely heavily on the vast store of knowledge available in the online electronic dictionary -- WordNet. So, we use a Perl interface for WordNet called WordNet::QueryData to make it easier for us to access WordNet. The modules in this package REQUIRE that the WordNet::QueryData module be installed on the system before these modules are installed. CONTENTS The package contains the semantic relatedness modules, some support Perl utilities and some sample configuration files, data files and programs. Modules All the modules that will be installed in the Perl system directory are present in the '/lib' directory tree of the package. These include the semantic relatedness modules -- jcn.pm, res.pm, lin.pm, lch.pm, hso.pm, lesk.pm, vector.pm, vector_pairs.pm, wup.pm, path.pm and random.pm -- present in the /lib/WordNet/Similarity subdirectory and the supporting modules get_wn_info.pm and string_compare.pm. There also exists a WordNet/Similarity.pm module that currently serves as a base class for all the relatedness modules and contains Perl documentation. All these modules, once installed in the Perl system directory, can be directly used by Perl programs. Supporting Utilities The '/utils' subdirectory of the package contains supporting Perl programs. 'similarity.pl' is a command-line interface to the relatedness modules. A number of Perl programs, that generate information content files from various corpora, are provided. As part of the standard install, these are also installed into the system directories, and can be accessed from any working directory if the common system directories (/usr/bin, /usr/local/bin, etc) are in your path. Samples If you downloaded this package as a tar-gzipped file from the web, you will find a '/samples' subdirectory in the package. There is a separate README in that directory. The directory contains sample configuration files for the modules, sample programs showing usage of the modules and sample data files (e.g., relation files). REFERENCES 1 Wu Z. and Palmer M. 1994. Verb Semantics and Lexical Selection. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics. Las Cruces, New Mexico. 2 Resnik P. 1995. Using information content to evaluate semantic similarity. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, pages 448-453. Montreal. 3 Jiang J. and Conrath D. 1997. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of International Conference on Research in Computational Linguistics. Taiwan. 4 Fellbaum C., editor. WordNet: An electronic lexical database. MIT Press, 1998. 5 Leacock C. and Chodorow M. 1998. Combining local context and WordNet similarity for word sense identification. In Fellbaum 1998, pp. 265-283. 6 Lin D. 1998. An information-theoretic definition of similarity. In Proceedings of the 15th International Conference on Machine Learning. Madison, WI. 7 Hirst G. and St-Onge D. 1998. Lexical Chains as representations of context for the detection and correction of malapropisms. In Fellbaum 1998, pp. 305-332. 8 Schütze H. 1998. Automatic Word Sense Discrimination. Computational Linguistics, 24(1):97-123. 9 Resnik P. 1999. Semantic Similarity in a Taxonomy: An Information- Based Measure and its Applications to Problems of Ambiguity in Natural Language. Journal of Artificial Intelligence Research, 11, 95-130. 10 Budanitsky A. and Hirst G. 2001. Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures. In Workshop on WordNet and Other Lexical Resources, Second meeting of the North American Chapter of the Association for Computational Linguistics. Pittsburgh, PA. 11 Banerjee S. and Pedersen T. 2002. An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet. In Proceeding of the Fourth International Conference on Computational Linguistics and Intelligent Text Processing (CICLING-02). Mexico City. 12 Patwardhan S., Banerjee S. and Pedersen T. 2002. Using Semantic Relatedness for Word Sense Disambiguation. In Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics. Mexico City. 13 Banerjee S. and Pedersen T. 2003. Extended Gloss Overlaps as a Measure of Semantic Relatedness. In the Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Acapulco, Mexico. 14 Patwardhan S. and Pedersen T. 2006. Using WordNet-based Context Vectors to Estimate the Semantic Relatedness of Concepts. In the Proceedings of the EACL 2006 Workshop Making Sense of Sense - Bringing Computational Linguistics and Psycholinguistics Together. Trento, Italy. 15 Banerjee S. Adapting the Lesk algorithm for word sense disambiguation to WordNet. Master Thesis, University of Minnesota, Duluth, 2002. 16 Patwardhan S. Incorporating dictionary and corpus information into a vector measure of semantic relatedness. Master Thesis, University of Minnesota, Duluth, 2003. SEE ALSO intro.pod Mailing list: Project Home page: ACKNOWLEDGEMENTS We would like to thank the following for their support and contribution towards the development of this package. We thank Jason Rennie for his QueryData package, the WordNet guys at Princeton for WordNet, Resnik, Hirst, St-Onge, Jiang, Conrath, Lin, Wu, Palmer, Leacock, and Chodorow for their algorithms and work on the relatedness measures. We also thank Bano (Satanjeev Banerjee) for his work on the extended gloss overlap module. We are grateful to Wybo Wiersma for contributing his optimizations to the GlossFinder code. We also appreciate the many helpful suggestions and bug patches from Ben Haskell. AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Jason Michelizzi COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen, Siddharth Patwardhan, Satanjeev Banerjee, and Jason Michelizzi Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. Note: a copy of the GNU Free Documentation License is available on the web at and is included in this distribution as FDL.txt. WordNet-Similarity-2.05/INSTALL0000644000175000017500000002203111020102764014306 0ustar sidsidNAME INSTALL - [documentation] How to install WordNet::Similarity SYNOPSIS perl Makefile.PL make make test make install DESCRIPTION Prerequisites You need to have WordNet (version 3.0) installed, WordNet::QueryData (version 1.40 or later), and Text::Similarity (version 0.04 or later) installed. [NOTE: However, if you're using WordNet 3.0, you must use WordNet::QueryData v1.46 or above. WordNet::QueryData v1.40 through v1.45 can only be used with WordNet 2.1.] WordNet is available at , WordNet::QueryData is available from , and Text::Similarity is available from . You should set the WNHOME environment variable to the location where you have WordNet installed; see the WordNet::QueryData documentation for more information. Installing The usual way to install the package is to run the following commands: perl Makefile.PL make make test make install If you can't set the WNHOME environment variable, you can use the WNHOME option when running perl Makefile.PL. For example, perl Makefile.PL WNHOME=/usr/local/WordNet-3.0 You will often need root access/superuser priviledges to run make install. The module can also be installed locally. To do a local install, you need to specify a PREFIX option when you run 'perl Makefile.PL'. For example, perl Makefile.PL PREFIX=/home/sid or perl Makefile.PL LIB=/home/sid/lib PREFIX=/home/sid will install Similarity into /home/sid. The first method above will install the modules in /home/sid/lib/perl5/site_perl/5.8.3 (assuming you are using version 5.8.3 of Perl; otherwise, the directory will be slightly different). The second method will install the modules in /home/sid/lib. In either case the executable scripts will be installed in /home/sid/bin and the man pages will be installed in home/sid/share. Warning: do not put a dash or hyphen in front of PREFIX, LIB or WNHOME. In your perl programs that you may write using the modules, you may need to add a line like so use lib '/home/sid/lib/perl5/site_perl/5.8.3'; if you used the first method or use lib '/home/sid/lib'; if you used the second method. By doing this, the installed modules are found by your program. To run the similarity.pl program, you would need to do perl -I/home/sid/lib/perl5/site_perl/5.8.3 similarity.pl or perl -I/home/sid/lib Of course, you could also add the 'use lib' line to the top of the program yourself, but you might not want to do that. You will need to replace 5.8.3 with whatever version of Perl you are using. The preceeding instructions should be sufficient for standard and slightly non-standard installations. However, if you need to modify other makefile options you should look at the ExtUtils::MakeMaker documentation. Modifying other makefile options is not recommended unless you really, absolutely, and completely know what you're doing! NOTE: The information-content based measures (res, lin, jcn) are invoked using the default information content file generated during installation of the modules. If, however, the version of WordNet being used on your system has changed since that time, or for some reason the modules are unable to locate the default information content files, then alternate information content files can be specified only by using a configuration file corresponding to each of the modules. Format and creation of configuration files has been discussed in the documentation. Utilities to generate information content files have been provided in the package. NOTE: If one (or more) of the tests run by 'make test' fails, you will see a summary of the tests that failed, followed by a message of the form "make: *** [test_dynamic] Error Y" where Y is a number between 1 and 255 (inclusive). If the number is less than 255, then it indicates how many test failed (if more than 254 tests failed, then 254 will still be shown). If one or more tests died, then 255 will be shown. For more details, see: http://search.cpan.org/dist/Test-Simple/lib/Test/Builder.pm#EXIT_CODES System Requirements 1. Perl version 5.6 or later. This package has been written in Perl, which is freely available from www.perl.org. This package assumes that perl is installed in the directory /usr/bin. If this is where perl is on your computer, then the support programs can be run directly at the command line as 'similarity.pl ...' or 'semCorFreq.pl...', etc. However, if Perl is not installed at this location, you would need to explicitly invoke them as 'perl similarity.pl ... ' or 'perl freqCount.pl...', etc. 2. WordNet: All the measures are based on WordNet. WordNet must be installed on your system. WordNet is freely downloadable from . WordNet version 3.0 was used during the development and testing of the package. Unfortunately, due to major changes in WordNet (and WordNet::QueryData) from version 2.0 to 2.1, this version of WordNet::Similarity supports only WordNet 2.1 (or later). The WordNet::QueryData Perl module is used to access WordNet. This module requires that an environment variable 'WNHOME', containing the path to the WordNet files, be set up. For further details, please see the WordNet::QueryData documentation. 3. WordNet::QueryData: This is the Perl interface to WordNet written by Jason Rennie. QueryData should be accessible on the @INC path of Perl. (Can be freely downloaded from ). QueryData 1.46 was used during the development. Also we observed that that due to some major changes in QueryData from its previous versions, this software does not work with the earlier versions of QueryData. If you have an earlier version of QueryData (1.38 or earlier) you may need to upgrade QueryData. 4. Text::Similarity: This module is used by the WordNet::Similarity::lesk measure for finding runs of word overlaps in glosses. The Text::Similarity module can be downloaded from . Version 0.04 or better is required in order to be compatible with Perl version 5.6 and better. Optional Tests Running 'make install' after make will run a short series of tests. These tests should not take more than a few minutes to run. There is another series of more rigorous tests that may also be run; however, these tests can take quite some time to run (over an hour on some machines). To run these tests, run 'make test_all'. Example The following procedure will work on most Linux systems. cd /tmp wget http://wordnet.princeton.edu/3.0/WordNet-3.0.tar.gz wget http://search.cpan.org/CPAN/authors/id/J/JR/JRENNIE/WordNet-QueryData-1.46.tar.gz wget http://search.cpan.org/CPAN/authors/id/J/JA/JASONM/Text-Similarity-0.02.tar.gz wget http://search.cpan.org/CPAN/authors/id/T/TP/TPEDERSE/WordNet-Similarity-2.01.tar.gz Then unpack each one: tar -zxvf WordNet-3.0.tar.gz tar -zxvf WordNet-QueryData-1.46.tar.gz tar -zxvf Text-Similarity-0.02.tar.gz tar -zxvf WordNet-Similarity-2.01.tar.gz Install WordNet: cd /tmp/WordNet-3.0 ./configure make su make install exit Installing QueryData and Similarity: cd /tmp/WordNet-QueryData-1.46 perl Makefile.PL make make test su make install exit cd /tmp/Text-Similarity-0.02 perl Makefile.PL make make test su make install exit cd /tmp/WordNet-Similarity-2.01 perl Makefile.PL make make test su make install exit SEE ALSO intro.pod Mailing list: Project Home page: AUTHORS Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu Satanjeev Banerjee, Carnegie Mellon University, Pittsburgh banerjee+ at cs.cmu.edu Jason Michelizzi COPYRIGHT Copyright (c) 2005-2008, Ted Pedersen, Siddharth Patwardhan, Satanjeev Banerjee, and Jason Michelizzi Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. Note: a copy of the GNU Free Documentation License is available on the web at and is included in this distribution as FDL.txt.