Filling knowledge base gaps for distant supervision of relation extraction

Wei Xu, Raphael Hoffmann, Le Zhao, Ralph Grishman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Distant supervision has attracted recent interest for training information extraction systems because it does not require any human annotation but rather isting knowledge bases to he bel a training corpus. Howe work has failed to address of false negative training exa beled due to the incompleten edge bases. To tackle this propose a simple yet novel fr combines a passage retrieval coarse features into a state-o tion extractor using multi-in ing with fine features. We formation retrieval techniqu relevance feedback to expan bases, assuming entity pairs i passages are more likely to e tion. Our proposed technique improves the quality of dis vised relation extraction, bo from 47.7% to 61.2% with a high level of precision of around 93% in the experiments.

Original languageEnglish (US)
Title of host publicationShort Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages665-670
Number of pages6
ISBN (Print)9781937284510
StatePublished - 2013
Event51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 - Sofia, Bulgaria
Duration: Aug 4 2013Aug 9 2013

Publication series

NameACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Volume2

Other

Other51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
CountryBulgaria
CitySofia
Period8/4/138/9/13

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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