Context-sensitive learning methods for text categorization

William W. Cohen, Yoram Singer

Research output: Contribution to journalArticle

158 Scopus citations

Abstract

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)307-316
Number of pages10
JournalSIGIR Forum (ACM Special Interest Group on Information Retrieval)
StatePublished - Dec 1 1996
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Hardware and Architecture

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