Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Automatic grammar generation from two different perspectives
Automatic grammar generation from two different perspectives
New figures of merit for best-first probabilistic chart parsing
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Is it harder to parse Chinese, or the Chinese Treebank?
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Two statistical parsing models applied to the Chinese Treebank
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
Phrase structure parsing with dependency structure
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Parsing the penn chinese treebank with semantic knowledge
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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We use prior and boundary estimates as the approximation of outside probability and establish our beam thresholding strategies based on these estimates. Lexical items, e.g. head word and head tag, are also incorporated to lexicalized prior and boundary estimates. Experiments on the Penn Chinese Treebank show that beam thresholding with lexicalized prior works much better than that with unlexicalized prior. Differentiating completed edges from incomplete edges paves the way for using boundary estimates in the edge-based beam chart parsing. The beam thresholding based on lexicalized prior, combined with unlexicalized boundary, runs faster than that only with lexicalized prior by a factor of 1.5, at the same performance level.