Year: 2004
Type: conference-paper
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.
Authors: Inderjeet Mani, James Pustejovsky
Institutions: Georgetown University, Brandeis University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W2024710449