Investigating LSTMs for joint extraction of opinion entities and relations

Arzoo Katiyar, Claire Cardie

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

34 Scopus citations

Abstract

We investigate the use of deep bidirectional LSTMs for joint extraction of opinion entities and the IS-FROM and IS-ABOUT relations that connect them - the first such attempt using a deep learning approach. Perhaps surprisingly, we find that standard LSTMs are not competitive with a state-of-the-art CRF+ILP joint inference approach (Yang and Cardie, 2013) to opinion entities extraction, performing below even the standalone sequencetagging CRF. Incorporating sentence-level and a novel relation-level optimization, however, allows the LSTM to identify opinion relations and to perform within 1-3% of the state-of-the-art joint model for opinion entities and the IS-FROM relation; and to perform as well as the state-of-theart for the IS-ABOUT relation - all without access to opinion lexicons, parsers and other preprocessing components required for the feature-rich CRF+ILP approach.

Original languageEnglish (US)
Title of host publication54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages919-929
Number of pages11
ISBN (Electronic)9781510827585
DOIs
StatePublished - 2016
Event54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Berlin, Germany
Duration: Aug 7 2016Aug 12 2016

Publication series

Name54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers
Volume2

Other

Other54th Annual Meeting of the Association for Computational Linguistics, ACL 2016
CountryGermany
CityBerlin
Period8/7/168/12/16

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Linguistics and Language

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