Paper: A Probabilistic Modeling Framework for Lexical Entailment

ACL ID P11-2098
Title A Probabilistic Modeling Framework for Lexical Entailment
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

Recognizing entailment at the lexical level is an important and commonly-addressed com- ponent in textual inference. Yet, this task has been mostly approached by simplified heuris- tic methods. This paper proposes an initial probabilistic modeling framework for lexical entailment, with suitable EM-based parame- ter estimation. Our model considers promi- nent entailment factors, including differences in lexical-resources reliability and the impacts of transitivity and multiple evidence. Evalu- ations show that the proposed model outper- forms most prior systems while pointing at re- quired future improvements.