Learning from labeled features using generalized expectation criteria

  title={Learning from labeled features using generalized expectation criteria},
  author={Gregory Druck and Gideon S. Mann and Andrew McCallum},
It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domain knowledge in the form of affinities between input features and classes. For example, in a baseball vs. hockey text classification problem, even without any labeled data, we know that the presence of the word puck is a strong indicator of hockey. We refer to this type of domain knowledge as a labeled feature. In this… CONTINUE READING
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