- SEE ALSO
WordNet::Similarity::ICFinder - a module for finding the information content of concepts in WordNet
use WordNet::QueryData; my $wn = WordNet::QueryData->new; defined $wn or die "Construction of WordNet::QueryData failed"; use WordNet::Similarity::ICFinder; my $obj = WordNet::Similarity::ICFinder->new ($wn); my ($err, $errString) = $obj->getError (); $err and die $errString; my $wps1 = 'cat#n#1'; my $wps2 = 'feline#n#1'; my $offset1 = $wn -> offset ($wps1); my $offset2 = $wn -> offset ($wps2); # using the wps mode my $ic = $obj->IC ($wps1, 'n', 'wps'); my $prob = $obj->probability ($wps1, 'n', 'wps'); my $freq = $obj->getFrequency ($wps1, 'n', 'wps'); print "$wps1 has frequency $freq, probability $prob, and IC $ic\n"; my $ic = $obj->IC ($wps2, 'n', 'wps'); my $prob = $obj->probability ($wps2, 'n', 'wps'); my $freq = $obj->getFrequency ($wps2, 'n', 'wps'); print "$wps2 has frequency $freq, probability $prob, and IC $ic\n"; my @lcsbyic = $obj -> getLCSbyIC($wps1,$wps2,'n','wps'); print "$wps1 and $wps2 have LCS $lcsbyic-> with IC $lcsbyic->\n"; # doing the same thing in the offset mode my $ic = $obj->IC ($offset1, 'n', 'offset'); my $prob = $obj->probability ($offset1, 'n', 'offset'); my $freq = $obj->getFrequency ($offset1, 'n', 'offset'); print "$offset1 has frequency $freq, probability $prob, and IC $ic\n"; my $ic = $obj->IC ($offset2, 'n', 'offset'); my $prob = $obj->probability ($offset2, 'n', 'offset'); my $freq = $obj->getFrequency ($offset2, 'n', 'offset'); print "$offset2 has frequency $freq, probability $prob, and IC $ic\n"; my @lcsbyic = $obj -> getLCSbyIC($offset1,$offset2,'n','wps'); print "$offset1 and $offset2 have LCS $lcsbyic-> with IC $lcsbyic->\n";
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 hash-code of WordNet the the file was created with. This should be present as a string of the form
For example, if WordNet version 2.1 with the hash-code LL1BZMsWkr0YOuiewfbiL656+Q4 was used for creation of the information content file, the following line would be present at the start of the information content file.
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.
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.
The following methodes are provided by this module.
- $module->traceOptions ()
Prints status of configuration options specific to this module to the trace string. This module has only one such options: infocontent.
- $module->probability ($synset, $pos, $mode)
Returns the probability of $synset in a corpus (using frequency values from whatever information content file is being used). If $synset is a wps string, then $mode must be 'wps'; if $synset is an offset, then $mode must be 'offset'.
- $module->IC ($synset, $pos, $mode)
Returns the information content of $synset. If $synset is a wps string, then $mode must be 'wps'; if $synset is an offset, then $mode must be 'offset'.
- $module->getFrequency ($synset, $pos, $mode)
Returns the frequency of $synset in whatever information content file is currently being used.
If $synset is a wps string, then the mode must be 'wps'; if $synset is an offset, then $mode must be 'offset'.
probability()methods will be more useful than this method. This method is useful in determining if the frequency of a synset was 0.
- getLCSbyIC($synset1, $synset2, $pos, $mode)
Given two input synsets, finds the least common subsumer (LCS) of them. If there are multiple candidates for the LCS, the the candidate with the greatest information content.
Parameters: two synsets, a part of speech, and a mode.
Returns: a list of the form ($lcs, $ic) where $lcs is the LCS and $ic is the information content of the LCS.
- $module->configure ()
Overrides the configure method of WordNet::Similarity to process the information content file (also calles WordNet::Similarity::configure() so that all the work done by that method is still accomplished).
- $module->_loadInfoContentFile ($file)
Subroutine to load frequency counts from an information content file.
- $module->_isValidInfoContentFile ($filename)
Subroutine that checks the validity of an information content file.
Ted Pedersen, University of Minnesota Duluth tpederse at d.umn.edu Jason Michelizzi, Univeristy of Minnesota Duluth mich0212 at d.umn.edu Siddharth Patwardhan, University of Utah, Salt Lake City sidd at cs.utah.edu
To report a bug e-mail tpederse at d.umn.edu or go to http://groups.yahoo.com/group/wn-similarity/.
WordNet::Similarity(3) WordNet::Similarity::res(3) WordNet::Similarity::lin(3) WordNet::Similarity::jcn(3)
Copyright (c) 2005, Ted Pedersen, Jason Michelizzi and Siddharth Patwardhan
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