NAME
    SenseClusters - Cluster similar contexts using co-occurrence matrices
    and Latent Semantic Analysis

SYNOPSIS
    SenseClusters is a suite of Perl programs that supports unsupervised
    clustering of similar contexts. It relies on it's own native
    methodology, and also provides support for Latent Semantic Analysis.

    SenseClusters is a complete system that takes users from preprocessing
    of raw text to providing clustered output. It supports the selection of
    features, the creation of various kinds of context representations,
    dimensionality reduction by singular value decomposition, clustering,
    and analysis of results.

    SenseClusters integrates specialized tools such as the Ngram Statistics
    Package (Text::NSP), SVDPACK, the Perl Data Language (PDL) and CLUTO to
    provide a variety of choices and high efficiency at each step in its
    processing.

OVERVIEW
    SenseClusters supports several different methods of clustering contexts.
    These include the native SenseClusters methodology, which is based on
    the use of first and second order representations of contexts. It also
    includes support for clustering lexical features using the native
    SenseClusters methodology or Latent Semantic Analysis.

    SenseClusters is based strictly on lexical features and does not rely on
    any manually created training data or external knowledge sources, and as
    such is language independent. The only requirement is that the language
    should be able to be tokenized via Perl regular expressions, which can
    be specified by the user. In fact, tokenization is so flexible that
    features could consist of characters, pairs of characters, etc.

    SenseClusters can be applied to the problem of discriminating word
    meanings or ambiguous names, using the target or head word
    representation. This is sometimes also called "headed" data, where each
    context is centered around the given target whose meanings are to be
    discovered. In this case the contexts that contain the given target word
    are clustered, and each cluster is assumed to correspond to a different
    meaning of that word.

    SenseClusters can also be applied to the problem of grouping short units
    of text that have no target or head (which is sometimes referred to as a
    "headless" representation. In this case there is no head or center to
    the context, so the entire context is being clustered to determine the
    meaning or topic of the context as a whole. Email categorization or news
    article clustering are examples of problems that could be approached
    using headless data.

    SenseClusters will automatically determine the number of clusters in the
    data based on a number of different automatic stopping measures we have
    developed, three of which are based on clustering criterion function,
    and one which is an adaptation of the well-known Gap Statistic.

    SenseClusters can also be applied to the problem of clustering words or
    lexical features, in hopes of discovering synonyms, antonyms, or other
    classes of words.

    Broadly speaking, SenseClusters can be used for any task that requires
    the recognition of contextually similar units of text, or words that
    occur in similar contexts.

DOCUMENTATION
    All programs have inline source code documentation written in pod style
    and this can be browsed from command line as a man page or using the
    'perldoc' command. For example, 'man bitsimat.pl' or 'perldoc
    bitsimat.pl' will displayed the documentation for the bitsimat.pl
    program. Each program also has a --help option to provide information
    about program options.

    You can see all of the modules and their associated documentation at
    README.Toolkit.

GETTING STARTED
    You might first like to run the demonstration scripts in samples/
    directory to get an idea of SenseClusters' usage and functionality, or
    try the web interface that is provided at
    <http://senseclusters.sourceforge.net>.

    samples/ contains scripts that utilize the wrapper program
    discriminate.pl that calls various other programs from the package to
    run a complete experiment. It also contains examples where specialized
    experiments are constructed directly from the programs provided in the
    package. In general it would be useful to consult the flowcharts in
    doc/Flowcharts to understand the overall structure of the package.

    The web interface provides an intuitive means of formulating and running
    discriminate.pl commands, so the use of the web interface and certainly
    be instructive in terms of how to formulate discriminate.pl commands.

    The contexts that you wish to cluster must be in Senseval-2 format. This
    is a simple XML markup that indicates the beginning and end of each
    context, and allows you to specify a target word and a "correct"
    categorization of the context, if you know that information. There is a
    pre-processing program text2sval.pl in Toolkit/preprocess/plain/ that
    converts plain text data (with a single context on each line) into
    Senseval-2 format. There is also a large amount of sample data that is
    already in Senseval-2 format available at
    <http://senseclusters.sourceforge.net>

    You can also (optionally) provide a separate training file in plain text
    format to be used as the feature selection data. If you don't do this,
    then the features will be selected from the contexts to be clustered.

PACKAGE ORGANIZATION
    After downloading and unpacking SenseClusters, you should find following
    files/directories within SenseClusters' directory.

    README, INSTALL, CHANGES, TODO, FAQ
        Read-only copies of documentation found in doc/*.pod

    GPL.txt
        A copy of the GNU General Public License, the terms under which
        SenseClusters is distributed.

    FDL.txt
        A copy of the GNU Free Documentation License, the terms under which
        the documentation of SenseClusters is distributed.

    discriminate.pl
        A wrapper program that acts as a driver for many other programs in
        the package. It clusters the given text instances based on their
        contextual similarities.

    Makefile.PL
        Generates a Makefile on running 'perl Makefile.PL'.

    doc/
        Contains various *.pod files that are kept in a read only form in
        the top level directory.

        doc/Flowcharts/ contains flow diagrams that illustrate how to put
        together the programs provided in SenseClusters' Toolkit with other
        packages like NSP, SVDPACK and CLUTO to run experiments without
        wrappers.

    * Testing/
        A directory of test cases written as C-shell scripts that will test
        if the package is installed properly or not.

    lib/
        A stub for the Text::Similarity perl module. At present
        SenseClusters is oriented about the command line, so this is mostly
        for the benefit of CPAN indexing.

    t/  A stub directory created by h2xs - future site of test cases rather
        than /Testing

    samples/
        A directory of scripts that demonstrate SenseClusters' usage and
        functionality.

    External/
        Contains a modified version of SVDPACKC, and a script that can be
        run to automatically install it, and retrieve and install Cluto.

    Toolkit/
        A directory of Perl programs implemented and used by SenseClusters.
        Users who are interested to use SenseClusters' tools individually
        and separately without using the wrapper programs are encouraged to
        browse through the Toolkit and Toolkit.pod.

    Web/
        Contains an easy to use and install web interface for SenseClusters.

SEE ALSO
    Please join our mailing lists to participate in the package related
    discussions, to post your questions or bugs and also to suggest
    enhancements to the package functionality.

    To subscribe to the user's mailing list, visit :

     L<http://lists.sourceforge.net/lists/listinfo/senseclusters-users>

    To subscribe to a low volume news mailing list, visit :

     L<http://lists.sourceforge.net/lists/listinfo/senseclusters-news>

    To subscribe to the developer's mailing list, visit :

     L<http://lists.sourceforge.net/lists/listinfo/senseclusters-developers>

    Visit the SenseClusters project home page :

     L<http://senseclusters.sourceforge.net/>

ACKNOWLEDGMENTS
    The SenseClusters project has been partially supported by a National
    Science Foundation Faculty Early CAREER Development award (Grant
    #0092784). This award funded the work of Amruta Purandare (2002-2004)
    and Anagha Kulkarni (2004-2006).

AUTHORS
     Ted Pedersen
     University of Minnesota, Duluth
     tpederse at d.umn.edu

     Amruta Purandare
     University of Pittsburgh

     Anagha Kulkarni
     Carnegie-Mellon University

     Mahesh Joshi
     Carnegie-Mellon University

COPYRIGHT
    Copyright (c) 2003-2008, Ted Pedersen, Amruta Purandare, Anagha
    Kulkarni, and Mahesh Joshi

    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.