Finite-state transducers in language and speech processing
Computational Linguistics
Natural Language Engineering
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
What is the minimal set of fragments that achieves maximal parse accuracy?
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
A parsing: fast exact Viterbi parse selection
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Statistical parsing with an automatically-extracted tree adjoining grammar
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Efficient parsing of highly ambiguous context-free grammars with bit vectors
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Synchronous binarization for machine translation
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Better binarization for the CKY parsing
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Efficient parsing for transducer grammars
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Binarization, synchronous binarization, and target-side binarization
SSST '07 Proceedings of the NAACL-HLT 2007/AMTA Workshop on Syntax and Structure in Statistical Translation
Bayesian learning of a tree substitution grammar
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Pushing for weighted tree automata
MFCS'11 Proceedings of the 36th international conference on Mathematical foundations of computer science
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We apply the idea of weight pushing (Mohri, 1997) to CKY parsing with fixed context-free grammars. Applied after rule binarization, weight pushing takes the weight from the original grammar rule and pushes it down across its binarized pieces, allowing the parser to make better pruning decisions earlier in the parsing process. This process can be viewed as generalizing weight pushing from transducers to hypergraphs. We examine its effect on parsing efficiency with various binarization schemes applied to tree substitution grammars from previous work. We find that weight pushing produces dramatic improvements in efficiency, especially with small amounts of time and with large grammars.