Latent Dirichlet Allocation with Topic-in-Set Knowledge

Latent Dirichlet Allocation is an unsupervised graphical model which can discover latent topics in unlabeled data. We propose a mechanism for adding partial supervision, called topic-in-set knowledge, to latent topic modeling. This type of supervision can be used to encourage the recovery of topics which are more relevant to user modeling goals than the topics which would be recovered otherwise. Preliminary experiments on text datasets are presented to demonstrate the potential effectiveness of this method.