结合中文分词的贝叶斯文本分类

文本分类是组织大规模文档数据的基础和核心。朴素贝叶斯文本分类方法是种简单且有效的文本分类算法，但是属性间强独立性的假设在现实中并不成立，借鉴概率论中的多项式模型，结合中文分词过程，引入特征词条权重，给出了改进Bayes方法。并由实验验证和应用本方法，文本分类的效率得到了提高。

Using Bayesian in Text Classification with Participle-method

Text classification is the base and core of processing large amount of document data. Native Bayes text classifier is a simple and effective text classification method. Text classification is the key technology in organizing and processing large amount of document data. The practical Bayes algorithm is an useful technique which has an assumption of strong independence of different properties. Based on the polynomial model, a way in feature abstraction considering word-weight and participle-method is introduced. At last the experiments show that efficiency of text classification is improved.