Dynamic Topic Models

Dynamic Topic Models. Blei, D., M. & Lafferty, J., D.

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 param-eters of the multinomial distributions that repre-sent the topics. Variational approximations based on Kalman filters and nonparametric wavelet re-gression are developed to carry out approximate posterior inference over the latent topics. In addi-tion 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 demon-strated by analyzing the OCR'ed archives of the journal Science from 1880 through 2000.

@article{
 title = {Dynamic Topic Models},
 type = {article},
 websites = {https://mimno.infosci.cornell.edu/info6150/readings/dynamic_topic_models.pdf},
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 created = {2018-02-05T16:52:59.655Z},
 accessed = {2018-02-05},
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 profile_id = {371589bb-c770-37ff-8193-93c6f25ffeb1},
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 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 param-eters of the multinomial distributions that repre-sent the topics. Variational approximations based on Kalman filters and nonparametric wavelet re-gression are developed to carry out approximate posterior inference over the latent topics. In addi-tion 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 demon-strated by analyzing the OCR'ed archives of the journal Science from 1880 through 2000.},
 bibtype = {article},
 author = {Blei, David M and Lafferty, John D}
}

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