A multimodal discourse ontology for meeting understanding

Authors:
John Niekrasz;Matthew Purver
Affiliations:
Center for the Study of Language and Information, Stanford University, Stanford, CA;Center for the Study of Language and Information, Stanford University, Stanford, CA
Venue:
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Year:
2005

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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.