C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
The Philips automatic train timetable information system
Speech Communication - Special issue on interactive voice technology for telecommunication applications
Speech Communication - Special issue on interactive voice technology for telecommunication applications (IVITA '96)
The String-to-String Correction Problem
Journal of the ACM (JACM)
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Vector-based natural language call routing
Computational Linguistics
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Gemini: a natural language system for spoken-language understanding
HLT '93 Proceedings of the workshop on Human Language Technology
Effective utterance classification with unsupervised phonotactic models
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
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We describe an approach ("variant transduction") aimed at reducing the effort and skill involved in building spoken language interfaces. Applications are created by specifying a relatively small set of example utterance-action pairs grouped into contexts. No intermediate semantic representations are involved in the specification, and the confirmation requests used in the dialog are constructed automatically. These properties of variant transduction arise from combining techniques for paraphrase generation, classification, and example-matching. We describe how a spoken dialog system is constructed with this approach and also provide some experimental results on varying the number of examples used to build a particular application.