Topic Models Conditioned on Relations

Latent Dirichlet allocation is a fully generative statistical language model that has been proven to be successful in capturing both the content and the topics of a corpus of documents. Recently, it was even shown that relations among documents such as hyper-links or citations allow one to share information between documents and in turn to improve topic generation. Although fully generative, in many situations we are actually not interested in predicting relations among documents. In this paper, we therefore present a Dirichlet-multinomial nonparametric regression topic model that includes a Gaussian process prior on joint document and topic distributions that is a function of document relan networks of scientific abstracts and of Wikipedia documents we show that this approach meets or exceeds the performance of several baseline topic models.

• Type: Journal
• Year: 2010
• Where: PKDD
• Authors: Mirwaes Wahabzada, Zhao Xu, Kristian Kersting
• Keywords: Data Mining, Documents, Latent Dirichlet Allocation, PKDD 2010, Topic Models