Combining Concept Hierarchies and Statistical Topic Models

Statistical topic models provide a general data-driven framework for automated discovery of high-level knowledge from large col-lections of text documents. While topic models can potentially dis-cover 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 qual-ity of the associated language models as well as enabling new tech-niques for inferring and visualizing the semantics of a document