Using explanation-based learning to increase performance in a large-scale NL query system

Authors:
Manny Rayner;Christer Samuelsson
Affiliations:
-;-
Venue:
HLT '90 Proceedings of the workshop on Speech and Natural Language
Year:
1990

Citing 2
Cited 2

Explanation-based learning: a problem solving perspective

Artificial Intelligence
Applying Explanation-Based Learning to Natural language Processing (2)

Applying Explanation-Based Learning to Natural language Processing (2)

Fast parsing using pruning and grammar specialization

ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Quantitative evaluation of explanation-based learning as an optimization tool for a large-scale natural language system

IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2

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Abstract

Explanation-based learning (EBL) is a machine-learning technique, closely connected to other techniques like macro-operator learning, chunking, and partial evaluation; a phrase we have found useful for describing the method to logic programmers is example-guided partial evaluation. The basic ideas of the method are well-described in an overview article which recently appeared in Artificial Intelligence [1], to which we refer the reader who wants to understand the theoretical principles; here, we will only summarize briefly what EBL means in the context of natural-language processing. A detailed presentation can be found in [3] and [4].