This paper describes a natural language system which improves its performance through learning. The system processes short English narratives and from a single narrative acquires a new schema for a stereotypical set of actions. During the understanding process, the system constructs explanations for characters' actions in terms of the goals they were meant to achieve. If a character achieves a common goal in a novel way, it generalizes the set of actions used to achieve this goal into a new schema. The generalization process is a knowledge-based analysis of the narrative's causal structure which removes unnecessary details while maintaining the validity of the explanation. The resulting generalized set of actions is then stored as a new schema and used by the system to process narratives which were previously beyond its capabilities.

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In Proceedings of the Third International Machine Learning Workshop, pp. 126--128, New Brunswick, New Jersey 1985.

@inproceedings{mooney:ml85, title={Generalizing Explanations of Narratives into Schemata}, author={Raymond J. Mooney}, booktitle={Proceedings of the Third International Machine Learning Workshop}, address={New Brunswick, New Jersey}, pages={126--128}, url="http://www.cs.utexas.edu/users/ai-lab?mooney:ml85", year={1985} }