BoosTexter: a boosting-based system for text categorization

Robert E. Schapire, Yoram Singer

Research output: Contribution to journalArticle

1539 Scopus citations

Abstract

This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms. We describe in detail an implementation, called BoosTexter, of the new boosting algorithms for text categorization tasks. We present results comparing the performance of BoosTexter and a number of other text-categorization algorithms on a variety of tasks. We conclude by describing the application of our system to automatic call-type identification from unconstrained spoken customer responses.

Original languageEnglish (US)
Pages (from-to)135-168
Number of pages34
JournalMachine Learning
Volume39
Issue number2
DOIs
StatePublished - 2000

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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