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.

中文翻译：

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基于问题属性核的中文问题分类

支持向量机已广泛用于分类任务，但是，在问题分类中使用标准核函数时，问题的结构会被忽略。为了解决这个问题，本文提出了一种结合了句法依赖关系和POS（词性）的问题属性核函数。首先，从问题中提取术语POS，“ HED”单词的依存关系和“疑问词”的依存关系。然后我们通过计算术语POS的依赖关系以及两个术语共享的依赖路径来采用内核函数的值。最后通过SMO算法得到支持向量。