WordNet::Similarity::vector_pairs 1.04
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WordNet::Similarity::vector_pairs 1.04 Ranking & Summary
File size:
0.63 MB
Platform:
Any Platform
License:
GPL (GNU General Public License)
Price:
Downloads:
802
Date added:
2007-08-18
WordNet::Similarity::vector_pairs 1.04 description
WordNet::Similarity::vector_pairs is a Perl module for computing semantic relatedness of word senses using second order co-occurrence vectors of glosses of the word senses.
SYNOPSIS
use WordNet::Similarity::vector_pairs;
use WordNet::QueryData;
my $wn = WordNet::QueryData->new();
my $vector_pairs = WordNet::Similarity::vector_pairs->new($wn);
my $value = $vector_pairs->getRelatedness("car#n#1", "bus#n#2");
($error, $errorString) = $vector_pairs->getError();
die "$errorStringn" if($error);
print "car (sense 1) <-> bus (sense 2) = $valuen";
Schütze (1998) creates what he calls context vectors (second order co-occurrence vectors) of pieces of text for the purpose of Word Sense Discrimination. This idea is adopted by Patwardhan and Pedersen to represent the word senses by second-order co-occurrence vectors of their dictionary (WordNet) definitions. The relatedness of two senses is then computed as the cosine of their representative gloss vectors.
A concept is represented by its own gloss, as well as the glosses of the neighboring senses as specified in the vector-relation.dat file. Each gloss is converted into a second order vector by replacing the words in the gloss with co-occurrence vectors for those words. The overall measure of relatedness between two concepts is determined by taking the pairwise cosines between these expanded glosses. If vector-relation.dat consists of:
example-example
glos-glos
hypo-hypo
then three pairwise cosine measurements are made to determine the relatedness of concepts A and B. The examples found in the glosses of A and B are expanded and measured, then the glosses themselves are expanded and measured, and then the hyponyms of A and B are expanded and measured. Then, the values of these three pairwise measures are summed to create the overall relatedness score.
$measure->initialize($file)
Overrides the initialize method in the parent class (GlossFinder.pm). This method essentially initializes the measure for use.
Parameters: $file -- configuration file.
Returns: none.
$measure->traceOptions()
This method is internally called to determine the extra options specified by this measure (apart from the default options specified in the WordNet::Similarity base class).
Parameters: none.
Returns: none.
$vector_pairs->getRelatedness
Computes the relatedness of two word senses using the Vector Algorithm.
Parameters: two word senses in "word#pos#sense" format.
Returns: Unless a problem occurs, the return value is the relatedness score, which is greater-than or equal-to 0. If an error occurs, then the error level is set to non-zero and an error string is created (see the description of getError()).
SYNOPSIS
use WordNet::Similarity::vector_pairs;
use WordNet::QueryData;
my $wn = WordNet::QueryData->new();
my $vector_pairs = WordNet::Similarity::vector_pairs->new($wn);
my $value = $vector_pairs->getRelatedness("car#n#1", "bus#n#2");
($error, $errorString) = $vector_pairs->getError();
die "$errorStringn" if($error);
print "car (sense 1) <-> bus (sense 2) = $valuen";
Schütze (1998) creates what he calls context vectors (second order co-occurrence vectors) of pieces of text for the purpose of Word Sense Discrimination. This idea is adopted by Patwardhan and Pedersen to represent the word senses by second-order co-occurrence vectors of their dictionary (WordNet) definitions. The relatedness of two senses is then computed as the cosine of their representative gloss vectors.
A concept is represented by its own gloss, as well as the glosses of the neighboring senses as specified in the vector-relation.dat file. Each gloss is converted into a second order vector by replacing the words in the gloss with co-occurrence vectors for those words. The overall measure of relatedness between two concepts is determined by taking the pairwise cosines between these expanded glosses. If vector-relation.dat consists of:
example-example
glos-glos
hypo-hypo
then three pairwise cosine measurements are made to determine the relatedness of concepts A and B. The examples found in the glosses of A and B are expanded and measured, then the glosses themselves are expanded and measured, and then the hyponyms of A and B are expanded and measured. Then, the values of these three pairwise measures are summed to create the overall relatedness score.
$measure->initialize($file)
Overrides the initialize method in the parent class (GlossFinder.pm). This method essentially initializes the measure for use.
Parameters: $file -- configuration file.
Returns: none.
$measure->traceOptions()
This method is internally called to determine the extra options specified by this measure (apart from the default options specified in the WordNet::Similarity base class).
Parameters: none.
Returns: none.
$vector_pairs->getRelatedness
Computes the relatedness of two word senses using the Vector Algorithm.
Parameters: two word senses in "word#pos#sense" format.
Returns: Unless a problem occurs, the return value is the relatedness score, which is greater-than or equal-to 0. If an error occurs, then the error level is set to non-zero and an error string is created (see the description of getError()).
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WordNet::Similarity::vector_pairs 1.04 Keywords
WordNet
computing semantic relatedness
word senses
Semantic relatedness
Perl module
pairs
senses
relatedness
word
vector
vectors
WordNet::Similarity::vector_pairs
WordNetSimilarityvectorpairs
WordNet::Similarity::vector_pairs 1.04
Libraries
Programming
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