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 proceedingConference contribution

368 Scopus citations

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 languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003
PublisherNeural information processing systems foundation
ISBN (Print)0262201526, 9780262201520
StatePublished - 2004
Externally publishedYes
Event17th Annual Conference on Neural Information Processing Systems, NIPS 2003 - Vancouver, BC, Canada
Duration: Dec 8 2003Dec 13 2003

Publication series

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

Other

Other17th Annual Conference on Neural Information Processing Systems, NIPS 2003
CountryCanada
CityVancouver, BC
Period12/8/0312/13/03

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Blei, D. M., Griffiths, T. L., Jordan, M. I., & Tenenbaum, J. B. (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.