Chinese Question Classification Based on Question Property Kernel

Li Liu, Zhengtao Yu, Jianyi Guo, Cunli Mao, Xudong Hong

Abstract: Support vector machine have been widely used in classification tasks, however, the structure of the question is ignored while using the standard kernel function in the question classification. To solve the problem, a question property kernel function which combines syntactic dependency relationship and POS (part of speech) is proposed in this paper. Firstly we extract the term, POS, dependency relationship of "HED" words and dependency relationship of "question words" from questions. And then we adopt the value of kernel function by computing the dependency relationship of the term, POS, and the dependency path which the two terms shared. At last we get the support vectors by SMO algorithm. The results of experiments show that the kernel function proposed in this paper which implicated the effective utilization of the question structure can improves the accuracy of the classification.

External IDs:dblp:journals/mlc/LiuYGMH14