Dynamic Topic Models

Blei, David M.; Lafferty, John D.

doi:10.1145/1143844.1143859

Dynamic Topic Models

David M. Blei, John D. Lafferty

ICML 2006 pp. 113-120

doi:10.1145/1143844.1143859 /icml/2006/blei2006icml-dynamic/

Abstract

A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics. Variational approximations based on Kalman filters and nonparametric wavelet regression are developed to carry out approximate posterior inference over the latent topics. In addition to giving quantitative, predictive models of a sequential corpus, dynamic topic models provide a qualitative window into the contents of a large document collection. The models are demonstrated by analyzing the OCR'ed archives of the journal Science from 1880 through 2000.

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Cite

Text

Blei and Lafferty. "Dynamic Topic Models." International Conference on Machine Learning, 2006. doi:10.1145/1143844.1143859

Markdown

[Blei and Lafferty. "Dynamic Topic Models." International Conference on Machine Learning, 2006.](https://mlanthology.org/icml/2006/blei2006icml-dynamic/) doi:10.1145/1143844.1143859

BibTeX

@inproceedings{blei2006icml-dynamic,
  title     = {{Dynamic Topic Models}},
  author    = {Blei, David M. and Lafferty, John D.},
  booktitle = {International Conference on Machine Learning},
  year      = {2006},
  pages     = {113-120},
  doi       = {10.1145/1143844.1143859},
  url       = {https://mlanthology.org/icml/2006/blei2006icml-dynamic/}
}