Paper: Global Learning of Typed Entailment Rules

ACL ID P11-1062
Title Global Learning of Typed Entailment Rules
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011

Extensive knowledge bases of entailment rules between predicates are crucial for applied se- mantic 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 meth- ods 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 re- cently released (Schoenmackers et al., 2010). Our results show that using global transitiv- ity information substantially improves perfor- mance over this resource and several base- lines, and that our scaling methods allow us to increase the scope of global learning of entailment-rule ...