Hierarchical topic models and the nested Chinese restaurant process

David M. Blei; Thomas L. Griffiths; Michael I. Jordan; Joshua B. Tenenbaum

Hierarchical topic models and the nested Chinese restaurant process

David M. Blei, Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003
Publisher	Neural information processing systems foundation
ISBN (Print)	0262201526, 9780262201520
State	Published - 2004
Externally published	Yes
Event	17th Annual Conference on Neural Information Processing Systems, NIPS 2003 - Vancouver, BC, Canada Duration: Dec 8 2003 → Dec 13 2003

Publication series

Name	Advances in Neural Information Processing Systems
ISSN (Print)	1049-5258

Other

Other	17th Annual Conference on Neural Information Processing Systems, NIPS 2003
Country/Territory	Canada
City	Vancouver, BC
Period	12/8/03 → 12/13/03

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

@inproceedings{019e26f661f544b59ef8e457c9fbcd04,

title = "Hierarchical topic models and the nested Chinese restaurant process",

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.",

author = "Blei, {David M.} and Griffiths, {Thomas L.} and Jordan, {Michael I.} and Tenenbaum, {Joshua B.}",

year = "2004",

language = "English (US)",

isbn = "0262201526",

series = "Advances in Neural Information Processing Systems",

publisher = "Neural information processing systems foundation",

booktitle = "Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003",

note = "17th Annual Conference on Neural Information Processing Systems, NIPS 2003 ; Conference date: 08-12-2003 Through 13-12-2003",

}

Blei, DM, Griffiths, TL, Jordan, MI & Tenenbaum, JB 2004, Hierarchical topic models and the nested Chinese restaurant process. in Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003. Advances in Neural Information Processing Systems, Neural information processing systems foundation, 17th Annual Conference on Neural Information Processing Systems, NIPS 2003, Vancouver, BC, Canada, 12/8/03.

Hierarchical topic models and the nested Chinese restaurant process. / Blei, David M.; Griffiths, Thomas L.; Jordan, Michael I. et al.
Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003. Neural information processing systems foundation, 2004. (Advances in Neural Information Processing Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 -

Hierarchical topic models and the nested Chinese restaurant process

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

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