Infusion of labeled data into distant supervision for relation extraction

Maria Pershina, Bonan Min, Wei Xu, Ralph Grishman

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

Abstract

Distant supervision usually utilizes only unlabeled data and existing knowledge bases to learn relation extraction models. However, in some cases a small amount of human labeled data is available. In this paper, we demonstrate how a state-of-theart multi-instance multi-label model can be modified to make use of these reliable sentence-level labels in addition to the relation-level distant supervision from a database. Experiments show that our approach achieves a statistically significant increase of 13.5% in F-score and 37% in area under the precision recall curve.

Original languageEnglish (US)
Title of host publicationLong Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages732-738
Number of pages7
ISBN (Print)9781937284732
DOIs
StatePublished - 2014
Event52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Baltimore, MD, United States
Duration: Jun 22 2014Jun 27 2014

Publication series

Name52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference
Volume2

Other

Other52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014
CountryUnited States
CityBaltimore, MD
Period6/22/146/27/14

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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    Pershina, M., Min, B., Xu, W., & Grishman, R. (2014). Infusion of labeled data into distant supervision for relation extraction. In Long Papers (pp. 732-738). (52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference; Vol. 2). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/p14-2119