PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Global Learning of Typed Entailment Rules
Jonathan Berant, Jacob Goldberger and Ido Dagan
In: ACL 2011, 19-24 Jun 2011, Portland, OR.


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.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:8759
Deposited By:Jonathan Berant
Deposited On:21 February 2012