PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Probabilistic Modeling Framework for Lexical Entailment
Eyal Shnarch, Jacob Goldberger and Ido Dagan
In: ACL 2011, 19-24 June 2011, Portland, Oregon, USA.

Abstract

Recognizing entailment at the lexical level is an important and commonly-addressed component in textual inference. Yet, this task has been mostly approached by simplified heuristic methods. This paper proposes an initial probabilistic modeling framework for lexical entailment, with suitable EM-based parameter estimation. Our model considers prominent entailment factors, including differences in lexical-resources reliability and the impacts of transitivity and multiple evidence. Evaluations show that the proposed model outperforms most prior systems while pointing at required future improvements.

EPrint Type:Conference or Workshop Item (Poster)
Additional Information:A short paper which was extended to a long paper in TextInfer 2011
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:8840
Deposited By:Eyal Shnarch
Deposited On:21 February 2012