Improving Topic Coherence with Latent Feature Word Representations in MAP Estimation for Topic Modeling
Dat Quoc Nguyen, Kairit Sirts, Mark S. Johnson · 2015
Probabilistic topic models are widely used to discover latent topics in document col-lections, while latent feature word vec-tors have been used to obtain high per-formance in many natural language pro-cessing (NLP) tasks. In this paper, we present a new approach by incorporating word vectors to directly optimize the max-imum a posteriori (MAP) estimation in a topic model. Preliminary results show that the word vectors induced from the experi-mental corpus can be used to improve the assignments of topics to words.