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.
Domain knowledge is incorporated in REF in the form of Dirichlet forest priors.
Incorporating domain knowledge into topic modeling via Dirichlet Forest priors
ICML '09
Computer Science Medicine