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We present NESL (the Neuro-Episodic Schema Learner), an event schema learning system that combines large language models, FrameNet parsing, a powerful logical representation of language, and a set of simple behavioral schemas meant to bootstrap the learning process.
The New McGuffey First Reader
William Holmes McGuffey. 1901 · 1901
Earlier work this paper cites.
Generalization and Memory in an Integrated Understanding System
Michael Lebowitz. 1980 · 1980
Earlier work this paper cites.
Maintaining knowledge about temporal intervals
James F. Allen. 1983 · 1983
Earlier work this paper cites.
Inference in text understanding
Peter Norvig. 1987 · 1987
Earlier work this paper cites.
A general explanation-based learning mechanism and its application to narrative understanding
Raymond J Mooney. 1990 · 1990
Earlier work this paper cites.
Episodic logic: A situational logic for natural language processing
Chung Hee Hwang and Lenhart K Schubert. 1993 · 1993
Earlier work this paper cites.
The berkeley framenet project
Collin F. Baker, Charles J. Fillmore, and John B. Lowe. 1998 · 1998
Earlier work this paper cites.
Unsupervised learning of narrative event chains
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Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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