Sequence and tree kernels with statistical feature mining

Jun Suzuki, Hideki Isozaki

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

21 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference
Pages1321-1328
Number of pages8
Publication statusPublished - 2005 Dec 1
Externally publishedYes
Event2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 - Vancouver, BC, Canada
Duration: 2005 Dec 52005 Dec 8

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

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

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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  • Cite this

    Suzuki, J., & Isozaki, H. (2005). Sequence and tree kernels with statistical feature mining. In Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference (pp. 1321-1328). (Advances in Neural Information Processing Systems).