Taxonomy building and machine learning based automatic classification for knowledge-oriented chinese questions

Authors:
Yunhua Hu;Qinghua Zheng;Huixian Bai;Xia Sun;Haifeng Dang
Affiliations:
Computer Department of Xi'an Jiaotong University, Xi'an, Shaanxi, China;Computer Department of Xi'an Jiaotong University, Xi'an, Shaanxi, China;Computer Department of Xi'an Jiaotong University, Xi'an, Shaanxi, China;Computer Department of Xi'an Jiaotong University, Xi'an, Shaanxi, China;Computer Department of Xi'an Jiaotong University, Xi'an, Shaanxi, China
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

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Abstract

In this paper, we propose a taxonomy for knowledge-oriented question, and study the machine learning based classification for knowledge-oriented Chinese questions. By knowledge-oriented questions, we mean questions carrying information or knowledge about something, which cannot be well described by previous taxonomies. We build the taxonomy after the study of previous work and analysis of 6776 Chinese knowledge-oriented questions collected from different realistic sources. Then we investigate the new task of knowledge-oriented Chinese questions classification based on this taxonomy. In our approach, the popular SVM learning method is employed as classification algorithm. We explore different features and their combinations and different kernel functions for the classification, and use different performance metrics for evaluation. The results demonstrate that the proposed approach is desirable and robust. Thorough error analysis is also conduced.