Incremental joint extraction of entity mentions and relations

Qi Li, Heng Ji

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

Abstract

We present an incremental joint framework to simultaneously extract entity mentions and relations using structured perceptron with efficient beam-search. A segment-based decoder based on the idea of semi-Markov chain is adopted to the new framework as opposed to traditional token-based tagging. In addition, by virtue of the inexact search, we developed a number of new and effective global features as soft constraints to capture the interdependency among entity mentions and relations. Experiments on Automatic Content Extraction (ACE)1 corpora demonstrate that our joint model significantly outperforms a strong pipelined baseline, which attains better performance than the best-reported end-to-end system.

Original languageEnglish (US)
Title of host publicationLong Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages402-412
Number of pages11
ISBN (Print)9781937284725
DOIs
StatePublished - 2014
Externally publishedYes
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
Volume1

Other

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

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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