PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Probabilistic Lexical Approach to Textual Entailment
Oren Glickman, Ido Dagan and Moshe Koppel
In: IJCAI 2005, 30 JULY - 5 AUGUST 2005, EDINBURGH, SCOTLAND.

Abstract

The textual entailment problem is to determine if a given text entails a given hypothesis. 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-theart heuristic scoring approaches.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:1290
Deposited By:Oren Glickman
Deposited On:28 November 2005