NAME
    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 <http://search.cpan.org>.

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: <http://groups.yahoo.com/group/wn-similarity>

    Project Home page: <http://wn-similarity.sourceforge.net>

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 <http://www.gnu.org/copyleft/fdl.html> and is included in this
    distribution as FDL.txt.

