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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.

See:

Amit Gruber, Michal Rosen-Zvi and Yair Weiss, "Hidden Topic Markov Models," in Artificial Intelligence and Statistics (AISTATS), San Juan, Puerto Rico, March 2007.

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google graphicalmodels latentsemanticanalysis latenttopicmodel lda machinelearning statistics

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