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ICML
2009
IEEE

Incorporating domain knowledge into topic modeling via Dirichlet Forest priors

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Incorporating domain knowledge into topic modeling via Dirichlet Forest priors
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 knowledge using a novel Dirichlet Forest prior in a Latent Dirichlet Allocation framework. The prior is a mixture of Dirichlet tree distributions with special structures. We present its construction, and inference via collapsed Gibbs sampling. Experiments on synthetic and real datasets demonstrate our model's ability to follow and generalize beyond userspecified domain knowledge.
David Andrzejewski, Xiaojin Zhu, Mark Craven
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2009
Where ICML
Authors David Andrzejewski, Xiaojin Zhu, Mark Craven
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