基于句法分析和二次贝叶斯模型的受限域问题分类

针对受限域的特殊性，提出了一种基于句法分析和二次贝叶斯模型的问题分类的新方法。该方法首先利用浅层句法分析的结果，抽取问题的主干部分和疑问词及其附属成分作为分类的特征，大大减少了噪声；然后，提出一种适用于受限域问题分类的改进的二次贝叶斯分类模型，并利用这一模型进行了大量的实验。实验结果表明了这一方法在受限域内的有效性，大类与小类问题的平均分类精度分别达到了89.66%和84.13%。

Question classification in restricted domain using syntactic parsing-based quadratic-Bayesian model

In this paper,a new method using syntactic parsing-based quadratic-Bayesian model was proposed to perform question classification in Chinese restricted domain.In this method,firstly,the shallow syntactic parsing on Chinese question sentences was performed.Secondly,the subject-predicate structures of all parsed question sentences,as well as interrogative words and their adjunctive parts,were extracted as the features in this constructed classifier,which greatly reduced the noise information.Thirdly,an advanced quadratic-Bayesian classification model for question classification in restricted domain was constructed.The experimental results show that the proposed question classification method is feasible in restricted domain and the average classification precisions of coarse classes and fine classes reach 89.66 percent and 84.13 percent respectively.