2006 DynamicTopicModels

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Subject Headings: Topic Modeling Algorithm, Topic Tracking Modeling Algorithm, Document Topic Evolution.

Notes

Cited by

2009

Quotes

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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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2006 DynamicTopicModelsDavid M. Blei
John D. Lafferty
Dynamic Topic ModelsICML 2006http://www.cs.princeton.edu/~blei/papers/BleiLafferty2006a.pdf10.1145/1143844.11438592006