Continuous Time Dynamic Topic Models
Conference paper preprint
PRINCETON UNIV NJ DEPT OF COMPUTER SCIENCE
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In this paper, we develop the continuous time dynamic topic model cDTM. The cDTM is a dynamic topic model that uses Brownian motion to model the latent topics through a sequential collection of documents, where a topic is a pattern of word use that we expect to evolve over the course of the collection. We derive an efficient variational approximate inference algorithm that takes advantage of the sparsity of observations in text, a property that lets us easily handle many time points. In contrast to the cDTM, the original discrete-time dynamic topic model dDTM requires that time be discretized. Moreover, the complexity of variational inference for the dDTM grows quickly as time granularity increases, a drawback which limits fine-grained discretization. We demonstrate the cDTM on two news corpora reporting both predictive perplexity and the novel task of time stamp prediction.
- Information Science
- Statistics and Probability