Towards a general theory of action and time
Artificial Intelligence
Possible Worlds, Artificial Intelligence, and Narrative Theory
Possible Worlds, Artificial Intelligence, and Narrative Theory
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Machine learning of temporal relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Linguistically motivated large-scale NLP with C&C and boxer
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
A computational model of narrative generation for suspense
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
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This paper sketches a theory of how readers (or users, or players, or viewers) respond to narratives. Such a theory can be useful for developing evaluation functions to allow for narrative outcomes that have maximum impact at particular times on the reader. The reader's response is viewed in terms of character evaluations, namely judgments of sympathy or antipathy for the agent involved in that outcome. Character development is computed in terms of transitions in reader evaluations for an agent over the time course of the narrative. To formally model character evaluations, we begin with a representation of the narrative fabula in terms of the events, their participant roles, and their temporal relations. This representation is implemented on a corpus of narratives with existing tools and standards. Reader evaluations are annotated on events in the fabula. Once high reliability in human character evaluations has been proven, a character evaluation tagger will be trained on these evaluations.