NAME
    WordNet-SenseRelate version 0.01

OVERVIEW
    Selecting the correct sense of a word in a context is called word sense
    disambiguation (WSD). The correct sense is selected from a set of
    predefined senses for that word (i.e., from a dictionary).

SYNOPSIS
        use WordNet::SenseRelate;
        use WordNet::QueryData;

        my $qd = WordNet::QueryData->new;
    
        my %options = (wordnet => $qd,
                       measure => 'WordNet::Similarity::lesk'
                       );

        my $wsd = WordNet::SenseRelate->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";
   
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 implements an extension of the algorithm described
    by Pedersen, Banerjee, and Patwardhan [2]. This implementation is
    similar to the original SenseRelate package. The original SenseRelate
    was intended for a "lexical sample" situation where the goal is to
    disambiguate only one word (specified by markup tags) in a given
    context.

    The goal of WordNet::SenseRelate is to disambiguate every word in a
    context or document.

    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.

  Algorithm
      for each word w in input
        disambiguate-single-word (w)

      disambiguate-single-word
        for each sense s_ti of target word t
            let socre_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 > threshold
                    score_i = score_i + best-score
        return i s.t. score_i > score_j for all j in {s_t0, ..., s_tN}

  The Context Window
    The size of the context window can be specified by the user. A context
    window of size 3 means that the 3 words to the left and the 3 words to
    the right of the target word will be in the context window; however, the
    algorithm will expand the context window so that the 3 words to the left
    will be words known 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.

    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.

SEE ALSO
    WordNet::SenseRelate(3) wsd.pl(1)

AUTHORS
    Jason Michelizzi <jmichelizzi at users.sourceforge.net>

    Ted Pedersen <tpederse at d.umn.edu>

COPYRIGHT AND LICENSE
    Copyright (C) 2004 by Jason Michelizzi 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. 2003.
        Maximizing Semantic Relatedness to Perform Word Sense
        Disambiguation.