Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Explanation-Based Generalization: A Unifying View
Machine Learning
Supertagging: an approach to almost parsing
Computational Linguistics
Memory-based learning: using similarity for smoothing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
A probabilistic corpus-driven model for lexical-functional analysis
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Some novel applications of Explanation-Based Learning to parsing Lexicalized Tree-Adjoining Grammars
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A Chinese corpus for linguistic research
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 4
Knowledge acquisition and Chinese parsing based on corpus
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 4
Sinica Treebank: design criteria, annotation guidelines, and on-line interface
CLPW '00 Proceedings of the second workshop on Chinese language processing: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 12
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Natural language parsing has to be accurate and quick. Explanation-based Learning (EBL) is a technique to speed-up parsing. The accuracy however often declines with EBL. The paper shows that this accuracy loss is not due to the EBL framework as such, but to deductive parsing. Abductive EBL allows extending the deductive closure of the parser. We present a Chinese parser based on abduction. Experiments show improvements in accuracy and efficiency.1