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Unpublished Paper
Topics over Time: A NonMarkov ContinuousTime Model of Topical Trends
  • Xuerui Wang
  • Andrew McCallum, University of Massachusetts - Amherst
This paper presents an LDA-style topic model that captures not only the low-dimensional structure of data, but also how the structure changes over time. Unlike other recent work that relies on Markov assumptions or discretization of time, here each topic is associated with a continuous distribution over timestamps, and for each generated document, the mixture distribution over topics is influenced by both word co-occurrences and the document's timestamp. Thus, the meaning of a particular topic can be relied upon as constant, but the topics' occurrence and correlations change significantly over time. We present results on nine months of personal email, 17 years of NIPS research papers and over 200 years of presidential state-of-the-union addresses, showing improved topics, better timestamp prediction, and interpretable trends.
  • Graphical Models,
  • Temporal Analysis,
  • Topic Modeling,
  • Artificial Intelligence,
  • Database Management,
  • Database Applications,
  • data mining
Publication Date
This is the pre-published version harvested from CIIR.
Citation Information
Xuerui Wang and Andrew McCallum. "Topics over Time: A NonMarkov ContinuousTime Model of Topical Trends" (2006)
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