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Computational Linguistics

Paola Merlo, Editor
March 2012, Vol. 38, No. 1, Pages 73-111
(doi: 10.1162/COLI_a_00085)
© 2012 Association for Computational Linguistics
Learning Entailment Relations by Global Graph Structure Optimization
Article PDF (628.6 KB)
Abstract

Identifying entailment relations between predicates is an important part of applied semantic inference. In this article we propose a global inference algorithm that learns such entailment rules. First, we define a graph structure over predicates that represents entailment relations as directed edges. Then, we use a global transitivity constraint on the graph to learn the optimal set of edges, formulating the optimization problem as an Integer Linear Program. The algorithm is applied in a setting where, given a target concept, the algorithm learns on the fly all entailment rules between predicates that co-occur with this concept. Results show that our global algorithm improves performance over baseline algorithms by more than 10%.