Supervised model learning with feature grouping based on a discrete constraint

Jun Suzuki, Masaaki Nagata

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

4 Citations (Scopus)

Abstract

This paper proposes a framework of supervised model learning that realizes feature grouping to obtain lower complexity models. The main idea of our method is to integrate a discrete constraint into model learning with the help of the dual decomposition technique. Experiments on two well-studied NLP tasks, dependency parsing and NER, demonstrate that our method can provide state-of-The-art performance even if the degrees of freedom in trained models are surprisingly small, i.e., 8 or even 2. This significant benefit enables us to provide compact model representation, which is especially useful in actual use.

Original languageEnglish
Title of host publicationShort Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages18-23
Number of pages6
ISBN (Print)9781937284510
Publication statusPublished - 2013 Jan 1
Externally publishedYes
Event51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 - Sofia, Bulgaria
Duration: 2013 Aug 42013 Aug 9

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
Country/TerritoryBulgaria
CitySofia
Period13/8/413/8/9

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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