Users of topic modeling methods often have knowledge about the composition of words that should have high or low probability in various topics. We incorporate such domain knowledg...
Previously topic models such as PLSI (Probabilistic Latent Semantic Indexing) and LDA (Latent Dirichlet Allocation) were developed for modeling the contents of plain texts. Recent...
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...
Multinomial distributions over words are frequently used to model topics in text collections. A common, major challenge in applying all such topic models to any text mining proble...
In this paper we extend the PAC learning algorithm due to Clark and Thollard for learning distributions generated by PDFA to automata whose transitions may take varying time length...