基于多重假设检验市长公开电话文本的自动分类

提出一种基于多重假设检验的特征加权朴素贝叶斯分类算法, 该算法通过特征选择方法得到多个特征词集合, 再按多重假设检验错误率为每个特征词集合配以不同的权重系数并参与到分类器的构建中. 该方法已经应用到市长公开电话的文本分类中, 通过构建的3个特征加权朴素贝叶斯分类器实现了投诉文本的计算机自动分类, 且相对传统方法提高了分类器的效率和精度.

Text Automatic Classification Based on Multiple Hypothesis Testing in the Mayor's Public Access Line Project

On the basis of multiple hypothesis testing, we proposed a feature weighted naive Bayesian algorithm, which outputs many sets of feature words by means of feature selection, and assigns a coefficient to each set of feature words which is used to construct the classifier in terms of the error rate of multiple hypothesis testing. This algorithm was used in the text classification of the mayor’s public access line project, where we realized the automatic classification of complaint texts by constructing three feature weighted naive Bayesian classifiers. Compared with those of the traditional methods, the efficiency and accuracy of our classifier are higher.