Chinese Question Classification Based on Semantic Gram and SVM
Question classification plays a crucial important role in the question answering system. Recent research on question classification for open-domain mostly concentrates on using machine learning methods to resolve the special kind of text classification. This paper presents our research about Chinese question classification using machine learning method and gives our approach based on SVM and semantic gram extraction. SVM has been widely used for question classification and got good performances. We use SVM as the classifier and propose a new feature extraction method of Chinese questions which is called semantic gram extraction. The method is proposed based on the word semantics and N-gram. The experiment results show that the feature extraction can perform well with SVM and our approach can reach high classification accuracy.
Chinese question classification SVM feature extraction semantic gram
Liang Wang Hui Zhang Deqing Wang Jia Huang
State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
国际会议
重庆
英文
432-435
2009-12-25（万方平台首次上网日期，不代表论文的发表时间）