关键词: 潜在的狄利克雷分配（LDA）, 条件随机场, 主题

Abstract: Using the topic model to model text and extract latent topic for part-of-speech tagging and document classification is a hot spot in the machine learning and text mining areas. This paper proposes a new model which based on LDA and an assumption called “section of the bag” that paragraph has the same topic, and the successive paragraphs tend to have the same topic. For passages from the article, it uses Conditional Random Field（CRF） model to divide them and judge whether they have the same topic. Experiments show that the improved model compared with LDA model has better topic extraction ability and lower degree of perplexity. At the same time, the improved model has better performance in part-speech-tagging and document classification.

Key words: Latent Dirichlet Allocation（LDA）, conditional random fields, topic