A Multimodal Discourse Ontology for Meeting Understanding


Niekrasz John, Purver Matthew. “A Multimodal Discourse Ontology for Meeting Understanding”in “Machine Learning for Multimodal Interaction: Second International Workshop, MLMI 2005, Edinburgh, UK, July 11-13, 2005, Revised Selected Papers”, pp. “162-173”, “Springer Berlin Heidelberg”, 2006.


In this paper, we present a multimodal discourse ontology that serves as a knowledge representation and annotation framework for the discourse understanding component of an artificial personal office assistant. The ontology models components of natural language, multimodal communication, multi-party dialogue structure, meeting structure, and the physical and temporal aspects of human communication. We compare our models to those from the research literature and from similar applications. We also highlight some annotations which have been made in conformance with the ontology as well as some algorithms which have been trained on these data and suggest elements of the ontology that may be of immediate interest for further annotation by human or automated means.

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