Topics over time: a non-Markov continuous-time model of topical trends

  title={Topics over time: a non-Markov continuous-time model of topical trends},
  author={Xuerui Wang and Andrew McCallum},
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… CONTINUE READING
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