PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Probabilistic Setting and Lexical Cooccurrence Model for Textual Entailment
Oren Glickman and Ido Dagan
In: ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment, 30 June 2005, Ann Arbor, USA.

Abstract

This paper proposes a general probabilistic setting that formalizes a probabilistic notion of textual entailment. We further describe a particular preliminary model for lexical-level entailment, based on document cooccurrence probabilities, which follows the general setting. The model was evaluated on two application independent datasets, suggesting the relevance of such probabilistic approaches for entailment modeling.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:1260
Deposited By:Ido Dagan
Deposited On:28 November 2005