文章基本信息

标题：Learning Entailment Relations by Global Graph Structure Optimization
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作者：Jonathan Berant ; Ido Dagan ; Jacob Goldberger 等
期刊名称：Computational Linguistics
印刷版ISSN：0891-2017
电子版ISSN：1530-9312
出版年度：2012
卷号：38
期号：1
页码：73-111
DOI：10.1162/COLI_a_00085
语种：English
出版社：MIT Press
摘要：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%.