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
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
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 :
To subscribe to a low volume news mailing list, visit :
To subscribe to the developer's mailing list, visit :
Visit the SenseClusters project home page :
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.
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