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A Multimodal Discourse Ontology for Meeting Understanding

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Machine Learning for Multimodal Interaction (MLMI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3869))

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Abstract

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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Author information

Authors and Affiliations

  1. Center for the Study of Language and Information, Stanford University, Cordura Hall, 210 Panama St., Stanford, CA, 94305-4115, USA

    John Niekrasz & Matthew Purver

Authors
  1. John Niekrasz
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  2. Matthew Purver
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Editor information

Editors and Affiliations

  1. University of Edinburgh, Edinburgh, Scotland

    Steve Renals

  2. IDIAP Research Institute, Martigny, Switzerland

    Samy Bengio

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© 2006 Springer-Verlag Berlin Heidelberg

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Niekrasz, J., Purver, M. (2006). A Multimodal Discourse Ontology for Meeting Understanding. In: Renals, S., Bengio, S. (eds) Machine Learning for Multimodal Interaction. MLMI 2005. Lecture Notes in Computer Science, vol 3869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11677482_14

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  • DOI: https://doi.org/10.1007/11677482_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32549-9

  • Online ISBN: 978-3-540-32550-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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