TY - CONF TI - Dynamic topic models AU - Blei, David M. AU - Lafferty, John D. T2 - the 23rd international conference AB - 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. C1 - Pittsburgh, Pennsylvania C3 - Proceedings of the 23rd international conference on Machine learning - ICML '06 DA - 2006/// PY - 2006 DO - 10.1145/1143844.1143859 DP - Crossref SP - 113 EP - 120 LA - en PB - ACM Press SN - 978-1-59593-383-6 UR - http://portal.acm.org/citation.cfm?doid=1143844.1143859 Y2 - 2019/01/12/07:42:33 KW - Topic model longitudinal over time KW - Topic modeling ER -