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NAMEWordNet::Similarity::ICFinder - a module for finding the information content of concepts in WordNetSYNOPSISuse 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[0]->[0] with IC $lcsbyic[0]->[1]\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[0]->[0] with IC $lcsbyic[0]->[1]\n"; DESCRIPTIONIntroductionThree 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 formwnver::<hashcode> 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. wnver::LL1BZMsWkr0YOuiewfbiL656+Q4 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. MethodsThe following methodes are provided by this module.Public Methods
Private Methods
AUTHORSTed 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 BUGSNone.To report a bug e-mail tpederse at d.umn.edu or go to http://groups.yahoo.com/group/wn-similarity/. SEE ALSOWordNet::Similarity(3) WordNet::Similarity::res(3) WordNet::Similarity::lin(3) WordNet::Similarity::jcn(3)COPYRIGHTCopyright (c) 2005, Ted Pedersen, Jason Michelizzi and Siddharth PatwardhanThis 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. Note: a copy of the GNU General Public License is available on the web at <http://www.gnu.org/licenses/gpl.txt> and is included in this distribution as GPL.txt.
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