NAME
WordNet::Similarity::res - Perl module for computing semantic relatedness of word senses using an information content based measure described by Resnik (1995).
SYNOPSIS
use WordNet::Similarity::res;
use WordNet::QueryData;
my $wn = WordNet::QueryData->new();
my $object = WordNet::Similarity::res->new($wn);
my $value = $object->getRelatedness("car#n#1", "bus#n#2");
($error, $errorString) = $object->getError();
die "$errorString\n" if($error);
print "car (sense 1) <-> bus (sense 2) = $value\n";
DESCRIPTION
Resnik (1995) uses the information content of concepts, computed from their frequency of occurrence in a large corpus, to determine the semantic relatedness of word senses. This module implements this measure of semantic relatedness.
USAGE
The semantic relatedness modules in this distribution are built as classes
that expose the following methods:
new()
getRelatedness()
getError()
getTraceString()See the WordNet::Similarity(3) documentation for details of these methods.
TYPICAL USAGE EXAMPLES
To create an object of the res measure, we would have the following
lines of code in the perl program.
use WordNet::Similarity::res;
$measure = WordNet::Similarity::res->new($wn, '/home/sid/res.conf');The reference of the initialized object is stored in the scalar variable '$measure'. '$wn' contains a WordNet::QueryData object that should have been created earlier in the program. The second parameter to the 'new' method is the path of the configuration file for the res measure. If the 'new' method is unable to create the object, '$measure' would be undefined. This, as well as any other error/warning may be tested.
die "Unable to create object.\n" if(!defined $measure);
($err, $errString) = $measure->getError();
die $errString."\n" if($err);To find the sematic relatedness of the first sense of the noun 'car' and the second sense of the noun 'bus' using the measure, we would write the following piece of code:
$relatedness = $measure->getRelatedness('car#n#1', 'bus#n#2');To get traces for the above computation:
print $measure->getTraceString();However, traces must be enabled using configuration files. By default traces are turned off.
CONFIGURATION FILE
The behaviour of the measures of semantic relatedness can be controlled by
using configuration files. These configuration files specify how certain
parameters are initialized within the object. A configuration file may be
specififed as a parameter during the creation of an object using the new
method. The configuration files must follow a fixed format.
Every configuration file starts the name of the module ON THE FIRST LINE of
the file. For example, a configuration file for the res module will have
on the first line 'WordNet::Similarity::res'. This is followed by the various
parameters, each on a new line and having the form 'name::value'. The
'value' of a parameter is optional (in case of boolean parameters). In case
'value' is omitted, we would have just 'name::' on that line. Comments are
supported in the configuration file. Anything following a '#' is ignored till
the end of the line.
The module parses the configuration file and recognizes the following
parameters:
(a) 'trace::' -- can take values 0, 1 or 2 or the value can be omitted,
in which case it sets the trace level to 1. Trace level 0 implies
no traces. Trace level 1 and 2 imply tracing is 'on', the only
difference being the way in which the synsets are displayed in the
traces. For trace level 1, the synsets are represented as word#pos#sense
strings, while for level 2, the synsets are represented as
word#pos#offset strings.
(b) 'cache::' -- can take values 0 or 1 or the value can be omitted, in
which case it takes the value 1, i.e. switches 'on' caching. A value of
0 switches caching 'off'. By default caching is enabled.
(c) 'infocontent::' -- The value for this parameter should be a string that
specifies the path of an information content file containing the
frequency of occurrence of every WordNet concept in a large corpus. The
format of this file is specified in a later section.INFORMATION CONTENT
Three of the measures provided within the package require information content values of concepts (WordNet synsets) for computing the semantic relatedness of concepts. Resnik (1995) describes a method for computing the information content of concepts from large corpora of text. In order to compute information content of concepts, according to the method described in the paper, we require the frequency of occurrence of every concept in a large corpus of text. We provide these frequency counts to the three measures (Resnik, Jiang-Conrath and Lin measures) in files that we call information content files. These files contain a list of WordNet synset offsets along with their part of speech and frequency count. The files are also used to determine the topmost nodes of the noun and verb 'is-a' hierarchies in WordNet. The information content file to be used is specified in the configuration file for the measure. If no information content file is specified, then the default information content file, generated at the time of the installation of the WordNet::Similarity modules, is used. A description of the format of these files follows. The FIRST LINE of this file MUST contain the version of WordNet that the file was created with. This should be present as a string of the form
wnver::<version>
For example, if WordNet version 1.7.1 was used for creation of the information content file, the following line would be present at the start of the information content file.
wnver::1.7.1
The rest of the file contains on each line a WordNet synset offset, part-of-speech and a frequency count, in the form
<offset><part-of-speech> <frequency> [ROOT]
without any leading or trailing spaces. For example, one of the lines of an information content file may be as follows.
63723n 667
where '63723' is a noun synset offset and 667 is its frequency count. Suppose the noun synset with offset 1740 is the root node of one of the noun taxonomies and has a frequency count of 17625. Then this synset would appear in an information content file as follows:
1740n 17625 ROOT
The ROOT tags are extremely significant in determining the top of the hierarchies and must not be omitted. Typically, frequency counts for the noun and verb hierarchies are present in each information content file.
SEE ALSO
perl(1), WordNet::Similarity(3), WordNet::QueryData(3)
http://www.d.umn.edu/~patw0006
http://www.cogsci.princeton.edu/~wn/
http://www.ai.mit.edu/people/jrennie/WordNet/
AUTHORS
Siddharth Patwardhan, <patw0006@d.umn.edu>
Ted Pedersen, <tpederse@d.umn.edu>COPYRIGHT AND LICENSE
Copyright 2003 by Siddharth Patwardhan and Ted Pedersen
This library is free software; you can redistribute it and/or modify it under the same terms as Perl itself.