A maximum entropy approach to natural language processing
Computational Linguistics
Foundations of statistical natural language processing
Foundations of statistical natural language processing
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Proceedings of the fifth international conference on Autonomous agents
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ECDL '97 Proceedings of the First European Conference on Research and Advanced Technology for Digital Libraries
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
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CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
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Towards the rapid development of a natural language understanding module
IVA'11 Proceedings of the 10th international conference on Intelligent virtual agents
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Natural language understanding is an essential module in any dialogue system. To obtain satisfactory performance levels, a dialogue system needs a semantic parser/natural language understanding system (NLU) that produces accurate and detailed dialogue oriented semantic output. Recently, a number of semantic parsers trained using either the FrameNet (Baker et al., 1998) or the Prop-Bank (Kingsbury et al., 2002) have been reported. Despite their reasonable performances on general tasks, these parsers do not work so well in specific domains. Also, where these general purpose parsers tend to provide case-frame structures, that include the standard core case roles (Agent, Patient, Instrument, etc.), dialogue oriented domains tend to require additional information about addressees, modality, speech acts, etc. Where general-purpose resources such as PropBank and Framenet provide invaluable training data for general case, it tends to be a problem to obtain enough training data in a specific dialogue oriented domain.