Constructing informative prior distributions from domain knowledge in text classification

  title={Constructing informative prior distributions from domain knowledge in text classification},
  author={Aynur A. Dayanik and David D. Lewis and David Madigan and Vladimir Menkov and Alexander Genkin},
Supervised learning approaches to text classification are in practice often required to work with small and unsystematically collected training sets. The alternative to supervised learning is usually viewed to be building classifiers by hand, using a domain expert's understanding of which features of the text are related to the class of interest. This is expensive, requires a degree of sophistication about linguistics and classification, and makes it difficult to use combinations of weak… CONTINUE READING
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