TY - JOUR
T1 - Combining prior knowledge and boosting for call classification in spoken language dialogue
AU - Rochery, M.
AU - Schapire, R.
AU - Rahim, M.
AU - Gupta, N.
AU - Riccardi, G.
AU - Bangalore, S.
AU - Alshawi, H.
AU - Douglas, S.
PY - 2002
Y1 - 2002
N2 - 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.
AB - 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.
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U2 - 10.1109/ICASSP.2002.5743646
DO - 10.1109/ICASSP.2002.5743646
M3 - Article
AN - SCOPUS:0036293687
SN - 1520-6149
VL - 1
SP - 29
EP - 32
JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -