TY - GEN
T1 - Hierarchical topic models and the nested Chinese restaurant process
AU - Blei, David M.
AU - Griffiths, Thomas L.
AU - Jordan, Michael I.
AU - Tenenbaum, Joshua B.
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:8644225400
SN - 0262201526
SN - 9780262201520
T3 - Advances in Neural Information Processing Systems
BT - Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003
PB - Neural information processing systems foundation
T2 - 17th Annual Conference on Neural Information Processing Systems, NIPS 2003
Y2 - 8 December 2003 through 13 December 2003
ER -