PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Probabilistic Classification Approach for Lexical Textual Entailment
Oren Glickman, Ido Dagan and Moshe Koppel
In: AAAI 2005, 9-13 JUL 2005, Pittsburgh, Pennsylvania, USA.

Abstract

The textual entailment task – determining if a given text entails a given hypothesis – provides an abstraction of applied semantic inference. This paper describes first a general generative probabilistic setting for textual entailment. We then focus on the sub-task of recognizing whether the lexical concepts present in the hypothesis are entailed from the text. This problem is recast as one of text categorization in which the classes are the vocabulary words. We make novel use of Naïve Bayes to model the problem in an entirely unsupervised fashion. Empirical tests suggest that the method is effective and compares favorably with state-of-the-art heuristic scoring approaches.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:1291
Deposited By:Oren Glickman
Deposited On:28 November 2005