Abstract: We address the problem of learning topic hierarchies from data. The model selection problem in this domain is daunting—which of the large collection of possible trees to use? We take a Bayesian approach, gen- erating an appropriate prior via a distribution on partitions that we refer to as the nested Chinese restaurant process. This nonparametric prior al- lows arbitrarily large branching factors and readily accommodates grow- ing data collections. We build a hierarchical topic model by combining this prior with a likelihood that is based on a hierarchical variant of latent Dirichlet allocation. We illustrate our approach on simulated data and with an application to the modeling of NIPS abstracts.
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