Density maximization in context-sense metric space for all-words WSD

Koichi Tanigaki, Mitsuteru Shiba, Tatsuji Munaka, Yoshinori Sagisaka

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

This paper proposes a novel smoothing model with a combinatorial optimization scheme for all-words word sense disambiguation from untagged corpora. By generalizing discrete senses to a continuum, we introduce a smoothing in context-sense space to cope with data-sparsity resulting from a large variety of linguistic context and sense, as well as to exploit senseinterdependency among the words in the same text string. Through the smoothing, all the optimal senses are obtained at one time under maximum marginal likelihood criterion, by competitive probabilistic kernels made to reinforce one another among nearby words, and to suppress conflicting sense hypotheses within the same word. Experimental results confirmed the superiority of the proposed method over conventional ones by showing the better performances beyond most-frequent-sense baseline performance where none of SemEval-2 unsupervised systems reached.

本文言語English
ホスト出版物のタイトルLong Papers
出版社Association for Computational Linguistics (ACL)
ページ884-893
ページ数10
ISBN(印刷版)9781937284503
出版ステータスPublished - 2013
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月 42013 8月 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
1

Conference

Conference51st 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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