Global Learning of Typed Entailment Rules

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 610–619, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Global Learning of Typed Entailment Rules Jonathan Berant Tel Aviv University Tel Aviv, Israel jonatha6@post.tau.ac.il Ido Dagan Bar-Ilan University Ramat-Gan, Israel dagan@cs.biu.ac.il Jacob Goldberger Bar-Ilan University Ramat-Gan, Israel goldbej@eng.biu.ac.il Abstract 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 graphs. 1 Introduction Generic approaches for applied semantic infer- ence from text gained growing attention in recent years, particularly under the Textual Entailment (TE) framework (Dagan et al., 2009). TE is a generic paradigm for semantic inference, where the objective is to recognize whether a target meaning can be inferred from a given text. A crucial com- ponent of inference systems is extensive resources of entailment rules, also known as inference rules, i.e., rules that specify a directional inference rela- tion between fragments of text. One important type of rule is rules that specify entailment relations be- tween predicates and their arguments. For example, the rule ‘X annex Y → X control Y’ helps recognize that the text ‘Japan annexed Okinawa’ answers the question ‘Which country controls Okinawa?’. Thus, acquisition of such knowledge received considerable attention in the last decade (Lin and Pantel, 2001; Sekine, 2005; Szpektor and Dagan, 2009; Schoen- mackers et al., 2010). Most past work took a “local learning” approach, learning each entailment rule independently of oth- ers. It is clear though, that there are global inter- actions between predicates. Notably, entailment is a transitive relation and so the rules A → B and B → C imply A → C. Recently, Berant et al. (2010) proposed a global graph optimization procedure that uses Integer Lin- ear Programming (ILP) to find the best set of entail- ment rules under a transitivity constraint. Imposing this constraint raised two challenges. The first of ambiguity: transitivity does not always hold when predicates are ambiguous, e.g., X buy Y → X acquire Y and X acquire Y → X learn Y, but X buy Y ↛ X learn Y since these two rules correspond to two dif- ferent senses of acquire. The second challenge is scalability: ILP solvers do not scale well since ILP is an NP-complete problem. Berant et al. circum- vented these issues by learning rules where one of the predicate’s arguments …

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