Our main contribution is that we propose two new latent feature topic models which integrate latent feature word representations into two Dirichlet multinomial topic models: a Latent Dirichlet Allocation (LDA) model (Blei et al., 2003) and a onetopic-per-document Dirichlet Multinomial Mixture (DMM) model (Nigam et al., 2000).
we replace the topic-to-word Dirichlet multinomial component which generates the words from topics in each Dirichlet multinomial topic model by a twocomponent mixture of a Dirichlet multinomial component and a latent feature component.