Explanation-based generalisation = partial evaluation
Artificial Intelligence
Explanation-based learning: a problem solving perspective
Artificial Intelligence
Using explanation-based learning to increase performance in a large-scale NL query system
HLT '90 Proceedings of the workshop on Speech and Natural Language
PROLOG and Natural Language Analysis
PROLOG and Natural Language Analysis
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Applying Explanation-Based Learning to Natural language Processing (2)
Applying Explanation-Based Learning to Natural language Processing (2)
Inferring Attribute Grammars with Structured Data for Natural Language Processing
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Inductive learning of search control rules for planning
Artificial Intelligence
Efficient large-scale parsing: a survey
Proceedings of the COLING-2000 Workshop on Efficiency In Large-Scale Parsing Systems
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This paper describes the application of explanation-based learning, a machine learning technique, to the SRI Core Language Engine, a large scale general purpose natural language analysis system. The idea is to bypass normal morphological, syntactic and (partly) semantic processing, for most input sentences, instead using a set of learned rules. Explanation-based learning is used to extract the learned rules automatically from sample sentences submitted by a user and thus tune the system for that particular user. By indexing the learned rules efficiently, it is possible to achieve dramatic speedups. Performance measurements were carried out using a training set of 1500 sentences and a separate test set of 100 sentences, all from the ATIS corpus. A set of 680 learned rules was derived from the training set. These rules covered 90 percent of the test sentences and reduced the total processing time to a third. An overall speed-up of 50 percent was accomplished using a set of only 250 learned rules.