Incorporating Hierarchical Dirichlet Process into Tag Topic Model
The Latent Dirichlet Allocation (LDA) is a parametric approach and the number of topics must be predefined. So it is natural to try to capture uncertainty regarding the number of topics. This paper proposes a Tag Hierarchical Dirichlet Process (THDP) that automatically infers the number of topics while also leveraging the tag information associated with each document. In this model, we assume that an author is clear in his mind that the content will contains which aspects and for each aspect he will choose a tag to describe it, and then we consider problems involving groups of tag, where each tag within a group is a draw from a mixture model and it is desirable to share topic between groups. In this setting it is natural to consider Hierarchical Dirichlet Process, Where the well-known clustering property of the Dirichlet process provides a nonparametric prior for the number of topic within each tag. Experimental results on corpora demonstrate superior performance over the THDP model.