一种有效特征词发现的贝叶斯文本分类方法

The Bayesian Text Categorization Based on Extraction of Effectual Features

根据信息学理论和贝叶斯语义模型,提出一种有效特征词发现方法,利用该方法对训练文本的原始文本特征词集进行聚类,对测试文本进行分类计算和类别标注。闭式测试的结果表明,文本识别的正确率达到了90%以上,该算法对互联网信息处理有较好的应用价值。

According to information theory and Bayesian Semantic model, this paper puts forward an effective text feature extract method which clusters the original text features of the training documents into words clusters. With this method, the test documents are classified and labeled by the text categorization system. The close experiments show that the precision is more than 90%. It is supposed to have a good application prospect in the field of internet information processing.