Neural network-based model for Japanese predicate argument structure analysis

Tomohide Shibata, Daisuke Kawahara, Sadao Kurohashi

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

9 Citations (Scopus)

Abstract

This paper presents a novel model for Japanese predicate argument structure (PAS) analysis based on a neural network framework. Japanese PAS analysis is challenging due to the tangled characteristics of the Japanese language, such as case disappearance and argument omission. To unravel this problem, we learn selectional preferences from a large raw corpus, and incorporate them into a SOTA PAS analysis model, which considers the consistency of all PAS s in a given sentence. We demonstrate that the proposed PAS analysis model significantly outperforms the base SOTA system.

Original languageEnglish
Title of host publication54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages1235-1244
Number of pages10
ISBN (Electronic)9781510827585
DOIs
Publication statusPublished - 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
Volume3

Other

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

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Linguistics and Language

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