Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
Incremental interpretation: applications, theory, and relationship to dynamic semantics
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Understanding unsegmented user utterances in real-time spoken dialogue systems
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Multifunctionality in dialogue
Computer Speech and Language
Toward joint segmentation and classification of dialog acts in multiparty meetings
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
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This paper presents a machine learning-based approach to the incremental understanding of dialogue utterances, with a focus on the recognition of their communicative functions. A token-based approach combining the use of local classifiers, which exploit local utterance features, and global classifiers which use the outputs of local classifiers applied to previous and subsequent tokens, is shown to result in excellent dialogue act recognition scores for unsegmented spoken dialogue. This can be seen as a significant step forward towards the development of fully incremental, on-line methods for computing the meaning of utterances in spoken dialogue.