Context-sensitive learning methods for text categorization

William W. Cohen, Yoram Singer

Research output: Contribution to journalArticlepeer-review

229 Scopus citations


Two recently implemented machine-learning algorithms, RIPPER and 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 ways of combining contexts to construct a classifier, different methods to search for a combination of contexts, and different criteria as to what contexts should be included in such a combination. In spite of these differences, both RIPPER and sleeping-experts perform extremely well across a wide variety of categorization problems, generally outperforming previously applied learning methods. We view this result as a confirmation of the usefulness of classifiers that represent contextual information.

Original languageEnglish (US)
Pages (from-to)141-173
Number of pages33
JournalACM Transactions on Information Systems
Issue number2
StatePublished - Apr 1999

All Science Journal Classification (ASJC) codes

  • Information Systems
  • General Business, Management and Accounting
  • Computer Science Applications


  • Algorithms
  • Experimentation
  • H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval
  • I.2.6 [Artificial Intelligence]: Learning - concept learning; parameter learning
  • I.5.4 [Pattern Recognition]: Applications - text processing


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