The latent topic model plays an important role in the unsupervised learning from a corpus, which provides a probabilistic interpretation of the corpus in terms of the latent topic...
We develop latent Dirichlet allocation with WORDNET (LDAWN), an unsupervised probabilistic topic model that includes word sense as a hidden variable. We develop a probabilistic po...
We develop the syntactic topic model (STM), a nonparametric Bayesian model of parsed documents. The STM generates words that are both thematically and syntactically constrained, w...
Analyzing the author and topic relations in email corpus is an important issue in both social network analysis and text mining. The AuthorTopic model is a statistical model that id...
—Probabilistic topic models were originally developed and utilised for document modeling and topic extraction in Information Retrieval. In this paper we describe a new approach f...