Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
Prosody-based automatic segmentation of speech into sentences and topics
Speech Communication - Special issue on accessing information in spoken audio
A critique and improvement of an evaluation metric for text segmentation
Computational Linguistics
The Role of Pause Occurrence and Pause Duration in the Signaling of Narrative Structure
PorTAL '02 Proceedings of the Third International Conference on Advances in Natural Language Processing
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
HLT '01 Proceedings of the first international conference on Human language technology research
Discourse segmentation of multi-party conversation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Computer Supported Cooperative Work
Locating case discussion segments in recorded medical team meetings
SSCS '09 Proceedings of the third workshop on Searching spontaneous conversational speech
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
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This paper presents a comparison of two similar dialogue analysis tasks: segmenting real-life medical team meetings into patient case discussions, and segmenting scenario-based meetings into topics. In contrast to other methods which use transcribed content and prosodic features (such as pitch, loudness etc), the method used in this comparison employs only the duration of the prosodic units themselves as the basis for dialogue representation. A concept of Vocalisation Horizon (VH) allows us to treat segmentation as a classification task where each instance to be classified is represented by the duration of a talk spurt, pause or speech overlap event in the dialogue. We report on the results this method yielded in segmentation of medical meetings, and on the implications of the results of further experiments on a larger corpus, the Augmented Multiparty Meeting corpus, to our ongoing efforts to support data collection and information retrieval in medical team meetings.