Context-sensitive learning methods for text categorization

@article{Cohen1999ContextsensitiveLM,
  title={Context-sensitive learning methods for text categorization},
  author={William W. Cohen and Yoram Singer},
  journal={ACM Trans. Inf. Syst.},
  year={1999},
  volume={17},
  pages={141-173}
}
  • William W. Cohen, Yoram Singer
  • Published in TOIS 1999
  • Computer Science
  • Two recently implemented machine-learning algorithms, RIPPERand sleeping-experts for phrases, are evaluated on a number of large text categorization problems. These algorithms both construct classifiers that allow the “context” of a word w to affect how (or even whether) the presence or absence of w will contribute to a classification. However, RIPPER and sleeping-experts differ radically in many other respects: differences include different notions as to what constitutes a context, different… CONTINUE READING

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