Supervised model learning with feature grouping based on a discrete constraint

Jun Suzuki, Masaaki Nagata

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルShort Papers
出版社Association for Computational Linguistics (ACL)
ページ18-23
ページ数6
ISBN(印刷版)9781937284510
出版ステータスPublished - 2013 1 1
外部発表はい
イベント51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 - Sofia, Bulgaria
継続期間: 2013 8 42013 8 9

出版物シリーズ

名前ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
2

Other

Other51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
国/地域Bulgaria
CitySofia
Period13/8/413/8/9

ASJC Scopus subject areas

  • 言語および言語学
  • 言語学および言語

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