Efficient tree-based approximation for entailment graph learning

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Learning entailment rules is fundamental in many semantic-inference applications and has been an active field of research in recent years. In this paper we address the problem of learning transitive graphs that describe entailment rules between predicates (termed entailment graphs). We first identify that entailment graphs exhibit a "tree-like" property and are very similar to a novel type of graph termed forest-reducible graph. We utilize this property to develop an iterative efficient approximation algorithm for learning the graph edges, where each iteration takes linear time. We compare our approximation algorithm to a recently-proposed state-of-the-art exact algorithm and show that it is more efficient and scalable both theoretically and empirically, while its output quality is close to that given by the optimal solution of the exact algorithm.

Original languageEnglish
Title of host publication50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference
Pages117-125
Number of pages9
StatePublished - 2012
Event50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Jeju Island, Korea, Republic of
Duration: 8 Jul 201214 Jul 2012

Publication series

Name50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference
Volume1

Conference

Conference50th Annual Meeting of the Association for Computational Linguistics, ACL 2012
Country/TerritoryKorea, Republic of
CityJeju Island
Period8/07/1214/07/12

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Software

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