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A nonparametric mixture model for topic modeling over time
[chapter]
2013
Proceedings of the 2013 SIAM International Conference on Data Mining
A single, stationary topic model such as latent Dirichlet allocation is inappropriate for modeling corpora that span long time periods, as the popularity of topics is likely to change over time. A number of models that incorporate time have been proposed, but in general they either exhibit limited forms of temporal variation, or require computationally expensive inference methods. In this paper we propose nonparametric Topics over Time (npTOT), a model for time-varying topics that allows an
doi:10.1137/1.9781611972832.59
dblp:conf/sdm/DubeyHWX13
fatcat:65qhb6yup5emzbmtddzs2ytthm