Employing Em and Pool-based Active Learning for Text Classiication

  title={Employing Em and Pool-based Active Learning for Text Classiication},
  author={Kachites McCallumzymccallum and Kamal Nigamyknigam},
  • Kachites McCallumzymccallum, Kamal Nigamyknigam
  • Published 1998
This paper shows how a text classiier's need for labeled training documents can be reduced by taking advantage of a large pool of unlabeled documents. We modify the Query-by-Committee (QBC) method of active learning to use the unlabeled pool for explicitly estimating document density when selecting examples for labeling. Then active learning is combined with Expectation-Maximization in order to \\ll in" the class labels of those documents that remain unla-beled. Experimental results show that… CONTINUE READING