PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The Distributional Inclusion Hypotheses and Lexical Entailment
Maayan Geffet and Ido Dagan
In: The 43rd Annual Meeting of the Association for Computational Linguistics, 25-30 Jun 2005, Ann Arbor, Michigan, USA.

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Natural Language Processing
ID Code:968
Deposited By:Maayan Geffet
Deposited On:28 November 2005