Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
A statistical parser for Czech
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Converting dependency structures to phrase structures
HLT '01 Proceedings of the first international conference on Human language technology research
Combining deep and shallow approaches in parsing German
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Trace prediction and recovery with unlexicalized PCFGs and slash features
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the 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
Annotation strategies for probabilistic parsing in German
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Computational Linguistics
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Unlexicalized probabilistic context-free parsing is a general and flexible approach that sometimes reaches competitive results in multilingual dependency parsing even if a minimum of language-specific information is supplied. Furthermore, integrating parser results (good at long dependencies) and tagger results (good at short range dependencies, and more easily adaptable to treebank peculiarities) gives competitive results in all languages.