The distributional inclusion hypotheses and lexical entailment

Maayan Geffet, Ido Dagan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

170 Scopus citations

Abstract

This paper suggests refinements for the Distributional Similarity Hypothesis. Our proposed hypotheses relate the distributional behavior of pairs of words to lexical entailment - a tighter notion of semantic similarity that is required by many NLP applications. To automatically explore the validity of the defined hypotheses we developed an inclusion testing algorithm for characteristic features of two words, which incorporates corpus and web-based feature sampling to overcome data sparseness. The degree of hypotheses validity was then empirically tested and manually analyzed with respect to the word sense level. In addition, the above testing algorithm was exploited to improve lexical entailment acquisition.

Original languageEnglish
Title of host publicationACL-05 - 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages107-114
Number of pages8
ISBN (Print)1932432515, 9781932432510
DOIs
StatePublished - 2005
Event43rd Annual Meeting of the Association for Computational Linguistics, ACL-05 - Ann Arbor, MI, United States
Duration: 25 Jun 200530 Jun 2005

Publication series

NameACL-05 - 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Conference

Conference43rd Annual Meeting of the Association for Computational Linguistics, ACL-05
Country/TerritoryUnited States
CityAnn Arbor, MI
Period25/06/0530/06/05

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