PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Lexical Alignment Model for Probabilistic Textual Entailment
Ido Dagan, Oren Glickman and Moshe Koppel
In: Machine Learning Challenges, Evaluating Predictive Uncertainty, Visual Object Classification and Recognizing Textual Entailment, First PASCAL Machine Learning Challenges Workshop Lecture Notes in Computer Science , 3944 . (2006) Springer , pp. 287-298. ISBN 3540334270

Abstract

This paper describes the Bar-Ilan system participating in the Recognising Textual Entailment Challenge. The paper proposes first a general probabilistic setting that formalizes the notion of textual entailment. We then describe a concrete alignment-based model for lexical entailment, which utilizes web co-occurrence statistics in a bag of words representation. Finally, we report the results of the model on the Recognising Textual Entailment challenge dataset along with some analysis.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:2860
Deposited By:Oren Glickman
Deposited On:22 November 2006