Accounting for Burstiness in Topic Models
published: Aug. 26, 2009, recorded: June 2009, views: 4821
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.
Many different topic models have been used successfully for a variety of applications. However, even state-of-the-art topic models suffer from the important ﬂaw that they do not capture the tendency of words to appear in bursts; it is a fundamental property of language that if a word is used once in a document, it is more likely to be used again. We introduce a topic model that uses Dirichlet compound multinomial (DCM) distributions to model this burstiness phenomenon. On both text and non-text datasets, the new model achieves better held-out likelihood than standard latent Dirichlet allocation (LDA). It is straightforward to incorporate the DCM extension into topic models that are more complex than LDA.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !