基于隐Markov模型的文本分类

HMM based text categorization

把基于序列模型的隐Markov模型引入文本分类领域。把待分类文本描述成一系列状态演化的隐Markov过程,其中状态以特定的概率产生代表文本的特征项。用序列模式来描述文本类,文本序列通过与隐Markov模型的匹配,求出其对应状态序列和最大输出概率。比较各个文本类的结果,达到文本分类的目的。最后通过和简单向量算法,KNN,Naive Bayes分类算法的比较,说明本算法的在文本分类中的成功应用。

Presents the new method using Hidden Markov Models （HMM） to supervise document classification.Represents the document to be classified in a kind of hidden Markov models.The states of HMM eject the symbols with a certain probability. These symbols composes of the classified documents.The class of document is supposed to be composed by some character item series.By calculating the output probability of the HMM on the class character series can get the max corresponding output probability and the output series.Compares the result on all the class can decide the category of a certain document.The model is evaluated on the real dataset with Naive Bayes,KNN and simple vector models.It is shown to be successful method in text categorization.