一种面向隐含主题的上下文树核

该文针对上下文树核用于文本表示时缺乏语义信息的问题，提出了一种面向隐含主题的上下文树核构造方法。首先采用隐含狄利克雷分配将文本中的词语映射到隐含主题空间，然后以隐含主题为单位建立上下文树模型，最后利用模型间的互信息构造上下文树核。该方法以词的语义类别来定义文本的生成模型，解决了基于词的文本建模时所遇到的统计数据的稀疏性问题。在文本数据集上的聚类实验结果表明，文中提出的上下文树核能够更好地度量文本间主题的相似性，提高了文本聚类的性能。

A Context Tree Kernel Based on Latent Semantic Topic

The lack of semantic information is a critical problem of context tree kernel in text representation. A context tree kernel method based on latent topics is proposed. First, words are mapped to latent topic space through Latent Dirichlet Allocation(LDA). Then, context tree models are built using latent topics. Finally, context tree kernel for text is defined through mutual information between the models. In this approach, document generative models are defined using semantic class instead of words, and the issue of statistic data sparse is solved. The clustering experiment results on text data set show, the proposed context tree kernel is a better measure of topic similarity between documents, and the performance of text clustering is greatly improved.