This paper establishes a theoretical connection between two very different kinds of probabilistic models: Probabilistic Context-Free Grammars (PCFGs) and a class of models known as Latent Dirichlet Allocation (Blei et al., 2003;Griffiths and Steyvers, 2004) models that have been used for a variety of tasks in machine learning.
we show that an LDA model can be expressed as a certain kind of PCFG, so Bayesian inference for PCFGs can be used to learn LDA topic models as well.