nips nips2003 nips2003-83 nips2003-83-reference knowledge-graph by maker-knowledge-mining
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Author: Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum, David M. Blei
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, generating an appropriate prior via a distribution on partitions that we refer to as the nested Chinese restaurant process. This nonparametric prior allows arbitrarily large branching factors and readily accommodates growing 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. 1
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