Linguistic Extensions of Topic Models

@inproceedings{BoydGraber2014LinguisticEO,
  title={Linguistic Extensions of Topic Models},
  author={Jordan L. Boyd-Graber},
  year={2014}
}
Topic models like latent Dirichlet allocation (LDA) provide a framework for analyzing large datasets where observations are collected into groups. Although topic modeling has been fruitfully applied to problems social science, biology, and computer vision, it has been most widely used to model datasets where documents are modeled as exchangeable groups of words. In this context, topic models discover topics, distributions over words that express a coherent theme like “business” or “politics… CONTINUE READING
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  • 2008
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