Temporal discourse models for narrative structure

  • Authors:
  • Inderjeet Mani;James Pustejovsky

  • Affiliations:
  • Georgetown University, Washington, DC;Brandeis University, Waltham, Massachusetts

  • Venue:
  • DiscAnnotation '04 Proceedings of the 2004 ACL Workshop on Discourse Annotation
  • Year:
  • 2004

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Abstract

Getting a machine to understand human narratives has been a classic challenge for NLP and AI. This paper proposes a new representation for the temporal structure of narratives. The representation is parsimonious, using temporal relations as surrogates for discourse relations. The narrative models, called Temporal Discourse Models, are tree-structured, where nodes include abstract events interpreted as pairs of time points and where the dominance relation is expressed by temporal inclusion. Annotation examples and challenges are discussed, along with a report on progress to date in creating annotated corpora.