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Combining Extra Information into Topic Models
TANG Shoucheng * #
BUPT School,PRIS lab
*Correspondence author
#Submitted by
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Funding: none
Opened online: 3 December 2010
Accepted by: none
Citation: TANG Shoucheng.Combining Extra Information into Topic Models[OL]. [ 3 December 2010] http://en.paper.edu.cn/en_releasepaper/content/4393355
 
 
Statistical topic models are attractive because they allow for a rapid analysis and understanding of new collections of text. However, this framework cannot provide sufficient information for the problem of learning a topic hierarchy from data. It has been shown recently that the data-driven learning approaches combined with some structure and prior knowledge can be a satisfactory solution. In this paper, we review a new probabilistic framework which adds the hierarchical information within document frequency into topics to seek the more semantic structure. The hierarchical topics created by DF topic model have a natural relationship beyond the tree structure. We illustrate our approach on 20 Newsgroups to show the performance of our model in extracting hierarchy of topics.
Keywords:LDA; document frequency; topic model
 
 
 

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