  Statistical topic models provide a general data-driven framework for
automated discovery of high-level knowledge from large collections of text
documents. While 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, on the other hand, tend to be semantically richer due
to careful selection of words to define concepts but they tend not to cover the
themes in a data set exhaustively. In this paper, we propose a probabilistic
framework to combine a hierarchy of human-defined semantic concepts with
statistical topic models to seek the best of both worlds. Experimental results
using two different sources of concept hierarchies and two collections of text
documents indicate that this combination leads to systematic improvements in
the quality of the associated language models as well as enabling new
techniques for inferring and visualizing the semantics of a document.
