Sequence and tree kernels with statistical feature mining

Jun Suzuki, Hideki Isozaki

研究成果: Conference contribution

21 被引用数 (Scopus)

抄録

This paper proposes a new approach to feature selection based on a statistical feature mining technique for sequence and tree kernels. Since natural language data take discrete structures, convolution kernels, such as sequence and tree kernels, are advantageous for both the concept and accuracy of many natural language processing tasks. However, experiments have shown that the best results can only be achieved when limited small sub-structures are dealt with by these kernels. This paper discusses this issue of convolution kernels and then proposes a statistical feature selection that enable us to use larger sub-structures effectively. The proposed method, in order to execute efficiently, can be embedded into an original kernel calculation process by using sub-structure mining algorithms. Experiments on real NLP tasks confirm the problem in the conventional method and compare the performance of a conventional method to that of the proposed method.

本文言語English
ホスト出版物のタイトルAdvances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference
ページ1321-1328
ページ数8
出版ステータスPublished - 2005 12月 1
外部発表はい
イベント2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 - Vancouver, BC, Canada
継続期間: 2005 12月 52005 12月 8

出版物シリーズ

名前Advances in Neural Information Processing Systems
ISSN(印刷版)1049-5258

Other

Other2005 Annual Conference on Neural Information Processing Systems, NIPS 2005
国/地域Canada
CityVancouver, BC
Period05/12/505/12/8

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 情報システム
  • 信号処理

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