Word Features for Latent Dirichlet Allocation
James Petterson, Wray Buntine, Shravan Narayanamurthy, Tibério S. Caetano, Alex Smola · 2010
We extend Latent Dirichlet Allocation (LDA) by explicitly allowing for the en-coding of side information in the distribution over words. This results in a variety of new capabilities, such as improved estimates for infrequently occurring words, as well as the ability to leverage thesauri and dictionaries in order to boost topic cohesion within and across languages. We present experiments on multi-language topic synchronisation where dictionary information is used to bias correspond-ing words towards similar topics. Results indicate that our model substantially improves topic cohesion when compared to the standard LDA model. 1