Hierarchical probabilistic modeling of discrete data has emerged as a powerful tool for text analysis. Posterior inference in such models is intractable, and practitioners rely on...
This paper explores correspondence and mixture topic modeling of documents tagged from two different perspectives. There has been ongoing work in topic modeling of documents with...
Contextual text mining is concerned with extracting topical themes from a text collection with context information (e.g., time and location) and comparing/analyzing the variations...
Abstract--Statistical approaches to document content modeling typically focus either on broad topics or on discourselevel subtopics of a text. We present an analysis of the perform...
Leonhard Hennig, Thomas Strecker, Sascha Narr, Ern...
In this paper, we propose a new variant of Latent Dirichlet Allocation(LDA): Collective LDA (C-LDA), for multiple corpora modeling. C-LDA combines multiple corpora during learning...