Semantic distance measures with distributional profiles of coarse-grained concepts

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DOIResolve DOI: http://doi.org/10.1007/978-3-642-22613-7_4
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TypeBook Chapter
Book titleModeling, Learning, and Processing of Text Technological Data Structures
Series titleStudies in Computational Intelligence; no. 370
ISSN1860-949X
ISBN9783642226120
Pages6179
AbstractAlthough semantic distance measures are applied to words in textual tasks such as building lexical chains, semantic distance is really a property of concepts, not words. After discussing the limitations of measures based solely on lexical resources such as WordNet or solely on distributional data from text corpora, we present a hybrid measure of semantic distance based on distributional profiles of concepts that we infer from corpora. We use only a very coarse-grained inventory of concepts - each category of a published thesaurus is taken as a single concept - and yet we obtain results on basic semantic-distance tasks that are better than those of methods that use only distributional data and are generally as good as those that use fine-grained WordNet-based measures. Because the measure is based on naturally occurring text, it is able to find word pairs that stand in non-classical relationships not found in WordNet. It can be applied cross-lingually, using a thesaurus in one language to measure semantic distance between words in another. In addition, we show the use of the method in determining the degree of antonymy of word pairs.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology
Peer reviewedYes
NPARC number21271466
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Record identifiere9cbbcab-c192-4cab-8ec7-f869c70b7fca
Record created2014-03-24
Record modified2016-11-09
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