Description:

The Hidden Topic Markov Model We propose modeling the topics of words in the document as a Markov chain. Specifically, we assume that all words in the same sentence have the same topic, and successive sentences are more likely to have the same topics. Since the topics are hidden, this leads to using the well-known tools of Hidden Markov Models for learning and inference. We show that incorporating this dependency allows us to learn better topics and to disambiguate words that can belong to different topics. Quantitatively, we show that we obtain better perplexity in modeling documents with only a modest increase in learning and inference complexity.

Changes to previous version:

Initial Announcement on mloss.org.

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Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Linux

Data Formats: None

Tags: Lda, Latent Semantic Analysis, Topic Modeling, Graphical Models, Em

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