A computational theory of grounding in natural language conversation
A computational theory of grounding in natural language conversation
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AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
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Data mining: practical machine learning tools and techniques with Java implementations
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Computational Linguistics
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GoDiS: an accommodating dialogue system
ANLP/NAACL-ConvSyst '00 Proceedings of the 2000 ANLP/NAACL Workshop on Conversational systems - Volume 3
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Classifying ellipsis in dialogue: a machine learning approach
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Quantifying ellipsis in dialogue: an index of mutual understanding
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Semantic representation of non-sentential utterances in dialog
SRSL '09 Proceedings of the 2nd Workshop on Semantic Representation of Spoken Language
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In this article we use well-known machine learning methods to tackle a novel task, namely the classification of non-sentential utterances (NSUs) in dialogue. We introduce a fine-grained taxonomy of NSU classes based on corpus work, and then report on the results of several machine learning experiments. First, we present a pilot study focused on one of the NSU classes in the taxonomy---bare wh-phrases or “sluices”---and explore the task of disambiguating between the different readings that sluices can convey. We then extend the approach to classify the full range of NSU classes, obtaining results of around an 87% weighted F-score. Thus our experiments show that, for the taxonomy adopted, the task of identifying the right NSU class can be successfully learned, and hence provide a very encouraging basis for the more general enterprise of fully processing NSUs.