Tag-topic model for semantic knowledge acquisition from blogs

This paper proposed a tag-topic model for semantic knowledge acquisition from blogs. The model extends the Latent Dirichlet Allocation by adding a tag layer between the document and topic layer, it represents each document with a mixture of tags, each tag is associated with a multinomial distribution over topics and each topic is associated with a multinomial distribution over words. After parameters estimating, the tags are regarded as concepts, the top words arranged to the top topics are selected as related words of the concepts, and PMI-IR is utilized for filtering out noisy words to improve the quality of the semantic knowledge. Experimental results show that the tag-topic model can effectively capture semantic knowledge.