title: Combining background knowledge and learned topics.
abstract: Statistical topic models provide a general data-driven framework for automated discovery of high-level knowledge from large collections of text documents. Although topic models can potentially discover a broad range of themes in a data set, the interpretability of the learned topics is not always ideal. Human-defined concepts, however, tend to be semantically richer due to careful selection of words that define the concepts, but they may not span the themes in a data set exhaustively. In this study, we review a new probabilistic framework for combining a hierarchy of human-defined semantic concepts with a statistical topic model to seek the best of both worlds. Results indicate that this combination leads to systematic improvements in generalization performance as well as enabling new techniques for inferring and visualizing the content of a document.
authors.affiliation: Department of Cognitive Sciences, University of California, IrvineDepartment of Computer Science, University of California, Irvine.
authors.forename: Mark
authors.initials: M
authors.lastname: Steyvers
authors.forename: Padhraic
authors.initials: P
authors.lastname: Smyth
authors.forename: Chaitanya
authors.initials: C
authors.lastname: Chemuduganta
mesh_terms: D003210:Concept Formation / Q000502:physiology*; D006801:Humans; D007802:Language; D007858:Learning / Q000502:physiology*; D015233:Models, Statistical; D012660:Semantics
keywords: Background knowledge; Bayesian models; Concept-topic model; Concepts; Data-driven learning; Hierarchical concept-topic model; Human-defined knowledge; Topic model