A topological embedding of the lexicon for semantic distance computation
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AbstractWe show how a quantitative context may be established for what is essentially qualitative in nature by topologically embedding a lexicon (here, WordNet) in a complete metric space. This novel transformation establishes a natural connection between the order relation in the lexicon (e.g., hyponymy) and the notion of distance in the metric space, giving rise to effective word-level and document-level lexical semantic distance measures. We provide a formal account of the topological transformation and demonstrate the value of our metrics on several experiments involving information retrieval and document clustering tasks.
2019 ◽
Vol 10
(7)
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pp. 1419-1425
1992 ◽
Vol 35
(4)
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pp. 439-448
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1987 ◽
Vol 106
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pp. 113-119
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1978 ◽
Vol 21
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pp. 7-11
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1975 ◽
Vol 19
(4)
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pp. 426-430
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