Incorporating Domain Knowledge into Topic Modeling via Dirichlet Forest Priors
published: Aug. 26, 2009, recorded: June 2009, views: 412
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !