WordNet::Similarity - Perl extensions for computing semantic relatedness of word senses defined in WordNet.
use WordNet::QueryData; use WordNet::Similarity::jcn; my $wn = WordNet::QueryData->new(); my $measure = WordNet::Similarity::jcn->new($wn); my $value = $measure->getRelatedness("car#n#1", "bus#n#2"); ($error, $errorString) = $measure->getError(); die "$errorString\n" if($error); print "car (sense 1) <-> bus (sense 2) = $value\n";
use WordNet::Similarity::edge; my $sim = WordNet::Similarity::edge->new($wn, "/home/sid/edge.conf"); my $value = $sim->getRelatedness("dog#n#1", "cat#n#1"); ($error, $errorString) = $sim->getError(); die "$errorString\n" if($error); print "dog (sense 1) <-> cat (sense 1) = $value\n";
print "Trace String -> ".($sim->getTraceString())."\n";
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.
Seven of these different measures of relatedness have been implemented in this software package. A simple edge counting measure and a random measure have also been provided. These 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.
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), Wu Palmer (1993), Hirst St Onge (1998) the adapted Lesk measure by Banerjee and Pedersen (2002) and a Context Vector measure recently introduced by Patwardhan and Pedersen. The package contains Perl modules designed as object classes with methods that take as input two word senses. The semantic distance between 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 simply returns the relatedness measure of two given words. Number of switches and options have been provided to modify the output and enhance it with trace information and other useful output. Support programs 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 semantic relatedness modules in this distribution are built as classes that expose the following methods: new() getRelatedness() getError() getTraceString()
The first thing that is done in order to use one of the semantic relatedness measures is to create an object of the measure. This is done by calling the 'new' method of that measure or module. For all the semantic relatedness measures provided in this package, the 'new' method takes two parameters -- (a) a WordNet::QueryData object (REQUIRED) (b) the name of a configuration file for that module (Optional) This method initializes an object of the requested measure, using the configuration file data, or with default values if a configuration file is not provided. A reference to this object is returned by the 'new' method and must be saved by the calling program, if any of the other methods of this module are to be called. It is possible to create multiple objects of the same module (possibly initialized differently by specifying different configuration files for each). The format of the configuration files is discussed later in this documentation.
An 'undef' value returned by the 'new' method, indicates that it was unable to create an object. It is also possible that non-fatal errors occur during the creation of the object. In this case an object is created by the 'new' method using default conditions. However, a non-fatal error condition flag is set within the object, which can be retrieved using the getError() method. It is advisable to check for this error condition after the creation of every such object.
The 'getRelatedness' method is called on the created object to determine the semantic relatedness of two concepts (synsets in WordNet) as computed by that measure. The input parameters are two WordNet synsets, represented in the word#pos#sense format returned/used by WordNet::QueryData. In this format each synset is represented by a word from that synset, its part-of-speech and its sense number. For example, if the second sense of 'teacher' as a noun occurs in a synset containing synonyms for 'teacher', then this synset can be represented by the string 'teacher#n#2'. The 'getRelatedness' method takes as input two strings of this form and returns a floating point value, which is the semantic relatedness of these (as computed by the measure).
During a call to either the 'new' method or the 'getRelatedness' method of a measure, if a fatal or non-fatal error occurs, the module sets an error flag within the created object and sets an error string within (the exception to this is when the module is unable to create an object upon a call to the 'new' method, in which case it simply returns 'undef'). Both the error condition flag and the error string can be retrieved using the 'getError' method on the created object. The method is called without any parameters and it returns an array containing the error flag as the first element and the error string as the second element. The error flag can take the values 0, 1 or 2. A value of 0 indicates that there was no error or warning since the last call to 'getError'. 1 indicates that there was/were non-fatal error(s) (warnings) since the last call to 'getError'. A value of 2 usually indicates that the errors were serious enough to warrant the termination of the program. However, how these errors are handled is completely upto the programmer writing the Perl program. It is advisable that the error flag be checked after every call to either 'new' or 'getRelatedness', but this is not a necessary step and the error condition may be tested at less regular intervals also.
If traces are enabled, a trace string generated during the last call to the 'getRelatedness' method is stored within the object. This trace string can be retrieved using the 'getTraceString' method. This method is called with no parameters and returns a scalar containing the most recently generated trace string. By default traces are not enabled. Traces can be enabled by specifying this as an option in the configuration file for the measure. Instructions for writing configuration files for the measures follow later in the documentation.
To create an object of the Resnik measure, we would have the following lines of code in the Perl program.
use WordNet::Similarity::res; $object = WordNet::Similarity::res->new($wn, '/home/sid/resnik.conf');
The reference of the initialized object is stored in the scalar variable '$object'. '$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 resnik measure. If the 'new' method is unable to create the object, '$object' would be undefined. This, as well as any other error/warning may be tested.
die "Unable to create resnik object.\n" if(!defined $object); ($err, $errString) = $object->getError(); die $errString."\n" if($err);
To create a Leacock-Chodorow measure object, using default values, i.e. no configuration file, we would have the following:
use WordNet::Similarity::lch; $measure = WordNet::Similarity::lch->new($wn);
To find the semantic relatedness of the first sense of the noun 'car' and the second sense of the noun 'bus' using the resnik measure, we would write the following piece of code:
$relatedness = $object->getRelatedness('car#n#1', 'bus#n#2');
To get traces for the above computation:
print $object->getTraceString();
However, traces must be enabled using configuration files. By default traces are turned off.
The behavior 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 specified 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 with the name of the module ON THE FIRST LINE of the file. For example, a configuration file for the Resnik 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 in the configuration file.
Sample configuration files are present in the '/samples' subdirectory of the package. Each of the modules has specific parameters that can be set/reset using the configuration files. Please read the manpages or the perldocs of the respective modules for details on the parameters specific to each of the modules. For instance, 'man WordNet::Similarity::res' or 'perldoc WordNet::Similarity::res' should display the documentation for the Resnik module.
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 the 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, of 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. A number of support programs to generate these files from various corpora are present in the '/utils' directory of the package. A sample information content file has been provided in the '/samples' directory of the package.
perl(1), WordNet::Similarity::jcn(3), WordNet::Similarity::res(3), WordNet::Similarity::lin(3), WordNet::Similarity::lch(3), WordNet::Similarity::hso(3), WordNet::Similarity::lesk(3), WordNet::Similarity::wup(3), WordNet::Similarity::edge(3), WordNet::Similarity::random(3), WordNet::QueryData(3)
http://www.cs.utah.edu/~sidd
http://www.cogsci.princeton.edu/~wn
http://www.ai.mit.edu/~jrennie/WordNet
http://groups.yahoo.com/group/wn-similarity
Siddharth Patwardhan, <sidd@cs.utah.edu> Satanjeev Banerjee, <banerjee+@cs.cmu.edu> Ted Pedersen, <tpederse@d.umn.edu>
Copyright 2003 by Siddharth Patwardhan, Satanjeev Banerjee and Ted Pedersen
This library is free software; you can redistribute it and/or modify it under the same terms as Perl itself.
To install WordNet::Similarity, copy and paste the appropriate command in to your terminal.
cpanm
cpanm WordNet::Similarity
CPAN shell
perl -MCPAN -e shell install WordNet::Similarity
For more information on module installation, please visit the detailed CPAN module installation guide.