Abstract
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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© 1986 Kluwer Academic Publishers
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Mooney, R.J. (1986). Generalizing Explanations of Narratives into Schemata. In: Machine Learning. The Kluwer International Series in Engineering and Computer Science, vol 12. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-2279-5_45
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DOI: https://doi.org/10.1007/978-1-4613-2279-5_45
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4612-9406-1
Online ISBN: 978-1-4613-2279-5
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