TY - GEN
T1 - A probabilistic modeling framework for lexical entailment
AU - Shnarch, Eyal
AU - Goldberger, Jacob
AU - Dagan, Ido
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84859082050&partnerID=8YFLogxK
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AN - SCOPUS:84859082050
SN - 9781932432886
T3 - ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
SP - 558
EP - 563
BT - ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics
T2 - 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011
Y2 - 19 June 2011 through 24 June 2011
ER -