Combining prior knowledge and boosting for call classification in spoken language dialogue

M. Rochery, R. Schapire, M. Rahim, N. Gupta, G. Riccardi, S. Bangalore, H. Alshawi, S. Douglas

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

Data collection and annotation are major bottlenecks in rapid development of accurate syntactic and semantic models for natural-language dialogue systems. In this paper we show how human knowledge can be used when designing a language understanding system in a manner that would alleviate the dependence on large sets of data. In particular, we extend BoosTexter, a member of the boosting family of algorithms, to combine and balance hand-crafted rules with the statistics of available data. Experiments on two voice-enabled applications for customer care and help desk are presented.

Original languageEnglish (US)
Pages (from-to)29-32
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume1
DOIs
StatePublished - Jan 1 2002
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Combining prior knowledge and boosting for call classification in spoken language dialogue'. Together they form a unique fingerprint.

  • Cite this