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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. . | A Probabilistic Modeling Framework for Lexical Entailment Eyal Shnarch Computer Science Department Bar-Ilan University Ramat-Gan Israel shey@cs.biu.ac.il Jacob Goldberger School of Engineering Bar-Ilan University Ramat-Gan Israel goldbej@eng.biu.ac.il Ido Dagan Computer Science Department Bar-Ilan University Ramat-Gan Israel dagan@cs.biu.ac.il 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. 1 Introduction and Background Textual Entailment was proposed as a generic paradigm for applied semantic inference Dagan et al. 2006 . This task requires deciding whether a textual statement termed the hypothesis-H can be inferred entailed from another text termed the text-T . Since it was first introduced the six rounds of the Recognizing Textual Entailment RTE chal-lenges1 currently organized under NIST have become a standard benchmark for entailment systems. These systems tackle their complex task at various levels of inference including logical representation Tatu and Moldovan 2007 MacCartney and Manning 2007 semantic analysis Burchardt et al. 2007 and syntactic parsing Bar-Haim et al. 2008 Wang et al. 2009 . Inference at these levels usually 1 http www.nist.gov tac 2010 RTE index.html 558 requires substantial processing and resources e.g. parsing aiming at high performance. Nevertheless simple entailment methods performing at the lexical level provide strong baselines which most systems did not outperform Mirkin et al. 2009