NAME WordNet-SenseRelate-AllWords version 0.07 OVERVIEW This module carries out word sense disambiguation (WSD), which is the process of selcting the correct sense for a word in a given context. The correct sense is selected from a sense inventory which lists the possible meanings of a word. This module uses the WordNet lexical database as it's sense inventory. SYNOPSIS use WordNet::SenseRelate::AllWords; use WordNet::QueryData; my $qd = WordNet::QueryData::AllWords->new; my %options = (wordnet => $qd, measure => 'WordNet::Similarity::lesk' ); my $wsd = WordNet::SenseRelate::AllWords->new (%options); my @words = qw/when in the course of human events/; my @res = $wsd->disambiguate (window => 2, tagged => 0, scheme => 'normal', context => [@words], ); print join (' ', @res), "\n"; CONTENTS When the distribution is unpacked, several subdirectories are created: /lib This directory contains the Perl modules that do the actual work of disambiguation. By default, these files are installed into /usr/local/lib/perl5/site_perl/PERL_VERSION (where PERL_VERSION is the version of Perl you are using). See the INSTALL file for more information. /utils This directoy contains a number of scripts that let you run word sense disambiguation experiments and reformat data. These scripts will be install when 'make install' is run. By default, these files are installed into your /usr/local/bin directory. See the INSTALL file for more information. The scripts in this directory are: wsd.pl This very useful script can be used to disambiguate a file of words. It is discussed in greater detail later in this document. semcor-reformat.pl This script will reformat a Semcor file so that it can be used as input to wsd.pl scorer2-format.pl This script will reformat the output of wsd.pl so that it can be used as input to the Senseval scorer2 program. Each of these scripts has detailed documentation. Run perldoc on a file to see the detailed documentation; for example, 'perldoc wsd.pl' shows the documentation for wsd.pl. /doc This directory contains all of the *pod files used to document the system. These are processed via pod2text and the output of this is placed in the top level directory, although these top level text files should be considered read only. /samples This directory contains examples of the different formats of data that are supported by this package. It also contains a sample stoplist. There is a README file in the directory that describes the contents in more detail. /t This directory contains test scripts. These scripts are run when you execute 'make test'. /web This directory contains the allwords web server and interface. There are detailed README and INSTALL instructions within this directory. Installing the web interface is optional, and is separate from installing the main package. DESCRIPTION Words can have multiple meanings or senses. For example, the word *glass* in WordNet [1] has seven senses as a noun and five senses as a verb. Glass can mean a clear solid, a container for drinking, the quantity a drinking container will hold, etc. WSD is the process of selecting the correct sense of a word when that word occurs in a specific context. For example, in the sentence, "the window is made of glass", the correct sense of glass is the first sense, a clear solid. WordNet::SenseRelate::AllWords extends a word sense disambiguation algorithm described by Pedersen, Banerjee, and Patwardhan [2] by making it disambiguate all words in text. The previous version of the algorithm was intended for lexical sample data, which means that a single word in a context is designated as the target word and is the only word to be disambiguated. By contrast, WordNet::SenseRelate::AllWords will assign a sense to every word known to WordNet that appears in a context. Prior to execution of the algorithm, we remove any word that is not known to WordNet, and any word that appears in a stoplist. The input to the algorithm is presumed to be a single sentence where non-WordNet words and stoplisted words have been removed. WordNet::SenseRelate::AllWords does not cross sentence boundaries when carrying out disambiguation. Algorithm for each word w in sentence disambiguate-single-word (w) disambiguate-single-word (w) for each sense s_ti of target word t, where i=0..N let score_i = 0 for each word w_j in context_window next if j = t for each sense s_jk of w_j temp-score_k = relatedness (s_ti, s_jk) best-score = max temp-score if best-score > pairScore score_i = score_i + best-score return s_ti s.t. score_i > score_j for all j in {s_t0, ..., s_tN} and score_i > contextScore The Context Window The size of the context window can be specified by the user. A context window of size 3 means that the context window will consist of three words, including the target word. Thus, the three words would be the word to the left of the target word, the target word itself, and the word to the right of the target word. The algorithm will expand the context window so that the three words will be words known to WordNet (the algorithm is unable to disambiguate words unknown to WordNet). For example, if the word 'the', occurs in the context window to the left of the target word, then the window will be expanded by one word to the left. If the window size is an even number, then there will be one more word to the left of the target word than to the right. For example, if the window size is 4, there will be two words to the left of the target word and one word to the right. Note that the context window will only include words in the same sentence as the target word. If, for example, the target word is the first word in the sentence, then there will be no words to left of the target word in the context window regarless of the specified window size. The minimum window size is 2 because a smaller window mean that there are no context words in the window. When the window size is 2, there is no context to use for disambiguating the first word in a sentence. To assign a sense number to that first word, the first sense of the word is chosen (i.e., sense number 1). Sense number 1 is usually the most frequent sense of a word. Part of Speech Coercion Certain measures of semantic similarity only work on noun-noun or verb-verb pairs; therefore, the usefulness of these measures for WSD is somewhat limited. As a way of coping with this problem, WordNet::SenseRelate::AllWords provides an option to "coerce" words in the context window to be of the same part of speech as the target word. When POS coercion is in effect, if the target word is a noun, then WordNet::SenseRelate::AllWords will attempt to convert non-nouns in the context window to noun forms of the same word. For example, if the target word is a noun and the verb *love* occurs in the window, the module might convert that word to the noun *love*. WordNet::SenseRelate::AllWords first uses the validForms method from WordNet::QueryData to find any valid forms of the word being coerced that are of the desired part of speech. In the case of part of speech tagged text, the POS tags are discarded. If validForms did not return any forms of the desired part of speech, then the derived forms relation in WordNet is used to find possible forms of the word. If neither of these methods returned usable forms, then no further attempt is made to coerce the word to be the desired part of speech. Tracing/Debugging Several different levels of trace output are available. The trace level can be specified as a command-line option to wsd.pl or as a parameter to the WordNet::SenseRelate::AllWords module. Trace Levels The trace levels are: 1 Show the context window for each pass through the algorithm. 2 Display winning score for each pass (i.e., for each target word). 4 Display the non-zero scores for each sense of each target word (overrides 2). 8 Display the non-zero values from the semantic relatedness measures. 16 Show the zero values as well when combined with either 4 or 8. When not used with 4 or 8, this has no effect. 32 Display traces from the semantic relatedness module. Different trace levels can be combined to achieve the desired behavior. For example, by specifying a trace level of 3, both level 1 and level 2 traces are generated (i.e., the context window will be shown along with the winning score for each pass). Using wsd.pl The wsd.pl script provides an easy method of performing disambiguation from the command line. The text to be disambiguated is read from a file provided by the user on the command line. Output The output of wsd.pl is simply the disambiguated words. The output will be in the form word#part_of_speech#sense_number. The part of speech will be one of 'n' for noun, 'v' for verb, 'a' for adjective, or 'r' for adverb. Words from other parts of speech are not disambiguated and are not found in WordNet. The sense number will be a WordNet sense number. WordNet sense numbers are assigned by frequency, so sense 1 of a word is more common than sense 2, etc. Sometimes when a word is disambiguated, a "different" but synonymous word will be found in the output. This is not a bug but is a consequence of how WordNet works. The word sense returned will always be the first word sense in a synset (synonym set) to which the original word belongs. Usage wsd.pl --context FILE --format FORMAT [--scheme SCHEME] [--type MEASURE] [--config FILE] [--compounds FILE] [--stoplist FILE] [--window INT] [--contextScore NUM] [--pairScore NUM] [--outfile FILE] [--trace INT] [--silent] | --help | --version The format option specifies one of the three different formats supported by wsd.pl. The three formats are: raw Raw text that is not part of speech tagged and needs undergo sentence boundary detection. Example: Red cars are faster than white cars. However, white cars are less expensive. parsed Parsed text is untagged text that has had all unwanted punctuation removed and has exactly one sentence per line. Example: Red cars are faster than white cars However white cars are less expensive tagged Tagged text is part of speech tagged text that has no unwanted punctuation and has exactly one sentence per line. Example: Red/JJ cars/NNS are/VBP faster/RBR than/IN white/JJ cars/NNS However/RB white/JJ cars/NNS are/VBP less/RBR expensive/JJ wntagged Similar to tagged, except that the input should only contain words known to WordNet, and each word should have a letter indicating the part of speech ('n', 'v', 'a', or 'r' for nouns, verbs, adjectives, and adverbs). For example: red#a car#n be#v faster#r white#a car#n white#a car#n be#v less#r expensive#a Additionally, no attempt will be made to search for other valid forms of the words in the input. For example, if 'dogs#n' is in the input, the program will not attempt to use other forms such as 'dog#n'. The different options and parameters for wsd.pl are discussed in detail in the documentation for wsd.pl. Run 'perldoc wsd.pl' to view the documentation. Usage Examples 1. wsd.pl --context input.txt --format raw 2. wsd.pl --trace 3 --context input.txt --format raw 3. wsd.pl --trace 3 --context input.txt --window 4 --format raw Using the Disambiguation Module The WordNet::SenseRelate::AllWords Perl module can be used in other Perl programs to perform word sense disambiguation. Example use WordNet::SenseRelate::AllWords; use WordNet::QueryData; my $qd = WordNet::QueryData->new; my $wsd = WordNet::SenseRelate::AllWords->new (wordnet => $qd, measure => 'WordNet::Similarity::lesk'); my @words = qw/this is a test/; my @results = $wsd->disambiguate (context => [@words]); print join (' ', @results), "\n"; The context parameter to disambiguate() specifies a set of words to disambiguate. The function treats the context as one sentence. To disambiguate multiple sentences, make a call to disambiguate() for each sentence. The usage of the disambiguation module is discussed in detail in the documentation for the module. Run 'perldoc WordNet::SenseRelate::AllWords' or 'man WordNet::SenseRelate::AllWords' (after installing the module) to view the documentation. To view the documentation before installing the module, run 'perldoc lib/WordNet/SenseRelate/AllWords.pm'. SEE ALSO WordNet::SenseRelate::AllWords(3) wsd.pl(1) The main web page for SenseRelate is There are several mailing lists for SenseRelate: AUTHORS Ted Pedersen Varada Kolhatkar Jason Michelizzi COPYRIGHT AND LICENSE Copyright (C) 2004-2005 by Jason Michelizzi and Ted Pedersen Copyright (C) 2005-2008 by Varada Kolhatkar and Ted Pedersen 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. REFERENCES 1. Christiane Fellbaum. 1998. WordNet: an Electronic Lexical Database. MIT Press. 2. Ted Pedersen, Satanjeev Banerjee, and Siddharth Patwardhan (2005) Maximizing Semantic Relatedness to Perform Word Sense Disambiguation, University of Minnesota Supercomputing Institute Research Report UMSI 2005/25, March.