Chinese question classification from approach and semantic views

Authors:
Youzheng Wu;Jun Zhao;Bo Xu
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing
Venue:
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Year:
2005

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Abstract

This paper presents a new Chinese question taxonomy respectively from approach and semantic viewpoints, and a SVM classification algorithm based on multiple features and hybrid feature weighting. The experimental results show that: (1) Lexical semantic features and structural features are the guarantee of high performance of question classification; (2) The contribution of dependency relation extracted from our current parser is no better than that of Bi-gram. (3) Our proposed feature weighting is effective for question classification.