IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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Latent topic models can be used to automatically decompose a collection of text documents into their constituent topics. This representation is useful for both exploratory browsing and other tasks such as informational retrieval. However, learned topics may not necessarily be meaningful to the user or well aligned with modeling goals. In this thesis we develop novel methods for enabling topic models to take advantage of side information, domain knowledge, and user guidance and feedback. These methods are used to enhance topic model analyses across a variety of datasets, including non-text domains.