新型快速中文文本分类器的设计与实现

Design and implementation of new Chinese text classier

为了提高中文文本分类的效率与精度,设计了一种新型的分类器。该分类器采用基于词频、互信息和类别信息的综合评估函数进行选择特征;在特征权重计算上,由于传统TF-IDF方法没有考虑特征类间和类内分布,提出了一种将词频和综合评估函数值相结合的权重计算方法;最后设计了一种基于贝叶斯原理的快速分类器。实验证明该分类器简单有效。

For improving the efficiency and accuracy of Chinese text categorization,this paper presents a new Chinese text classier,in which a novel feature selection is proposed according to word frequency,mutual information and classificatory information,and after analyzing the hypostasis of the traditional TF-IDF,a weight adjustment method is put forward in which the IDF function is replaced by function used in feature selection.Finally a fast Bayes theory classier is designed.Experiments prove this classier is simple and effective.