问题分类是问答系统中重要的组成部分,问题分类结果的好坏直接影响问答系统的质量。本文提出了一种用于问题分类的特征提取的新方法,该方法主要使用句法分析的结果,提取问题的主干和疑问词及其附属成分作为分类的特征,此方法大幅度地减少了噪音,突出了问题分类的主要特征,利用贝叶斯分类器分类,有效地提高了问题分类的精度。实验结果证明了该方法的有效性,大类和小类的分类精度分别达到了86.62%和71.92%,取得了较好的效果。
Qnestion classification is very important for question answering, and the result of question classification directly affects the quality of question answering. This paper presents a new method on feature extraction for question classification. The output of syntactic parsing is used in this method to extract the Subject-Predieate structure as well as interrogative words and their adjunctive parts as features for elassifieation, leading to substantial roduetion in noise, and emphasis on the main features of question claasification. A bayesian claasifier is used in classification, which effectively increases the precision of question claasifieation. The experimental result validates the effeetiveness of this method: the classifieation precision of coarse classes and fine elasses reach 86.62% and 71.92% respectively, which attains the expected effects.