Active learning for spoken language understanding

Gokhan Tur, Robert E. Schapire, Dilek Hakkani-Tür

Research output: Contribution to journalConference articlepeer-review

60 Scopus citations


In this paper, we describe active learning methods for reducing the labeling effort in a statistical call classification system. Active learning aims to minimize the number of labeled utterances by automatically selecting for labeling the utterances that are likely to be most informative. The first method, inspired by certainty-based active learning, selects the examples that the classifier is least confident about. The second method, inspired by committee-based active learning, selects the examples that multiple classifiers do not agree on. We have evaluated these active learning methods using a call classification system used for AT&T customer care. Our results indicate that it is possible to reduce human labeling effort at least by a factor of two.

Original languageEnglish (US)
Pages (from-to)276-279
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 2003
Externally publishedYes
Event2003 IEEE International Conference on Accoustics, Speech, and Signal Processing - Hong Kong, Hong Kong
Duration: Apr 6 2003Apr 10 2003

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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