@inproceedings{9d92a1d0c15048fda1211e5b4e8cc5d0,
title = "Global learning of typed entailment rules",
abstract = "Extensive knowledge bases of entailment rules between predicates are crucial for applied semantic inference. In this paper we propose an algorithm that utilizes transitivity constraints to learn a globally-optimal set of entailment rules for typed predicates. We model the task as a graph learning problem and suggest methods that scale the algorithm to larger graphs. We apply the algorithm over a large data set of extracted predicate instances, from which a resource of typed entailment rules has been recently released (Schoenmackers et al., 2010). Our results show that using global transitivity information substantially improves performance over this resource and several baselines, and that our scaling methods allow us to increase the scope of global learning of entailment-rule graphs.",
author = "Jonathan Berant and Ido Dagan and Jacob Goldberger",
year = "2011",
language = "الإنجليزيّة",
isbn = "9781932432879",
series = "ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
pages = "610--619",
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",
}