@inproceedings{a02fa341b2f4466282d20bb3183b505b,
title = "A probabilistic modeling framework for lexical entailment",
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.",
author = "Eyal Shnarch and Jacob Goldberger and Ido Dagan",
year = "2011",
language = "الإنجليزيّة",
isbn = "9781932432886",
series = "ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
pages = "558--563",
booktitle = "ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics",
note = "49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011 ; Conference date: 19-06-2011 Through 24-06-2011",
}