Learning to Classify Text from Labeled and Unlabeled

  title={Learning to Classify Text from Labeled and Unlabeled},
  author={DocumentsKamal Nigamyknigam and Andrew McCallumzymccallum and Sebastian Thrunythrun and Tom Mitchellymitchell and Henry StreetPittsburgh},
  • DocumentsKamal Nigamyknigam, Andrew McCallumzymccallum, +2 authors Henry StreetPittsburgh
  • Published 1998
In many important text classiication problems, acquiring class labels for training documents is costly, while gathering large quantities of unlabeled data is cheap. This paper shows that the accuracy of text classiiers trained with a small number of labeled documents can be improved by augmenting this small training set with a large pool of unlabeled documents. We present a theoretical argument showing that, under common assumptions , unlabeled data contain information about the target function… CONTINUE READING
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