Three generative, lexicalised models for statistical parsing
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 statistical parser for Czech
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Probabilistic parsing for German using sister-head dependencies
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Intricacies of Collins' Parsing Model
Computational Linguistics
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
Lexicalization in crosslinguistic probabilistic parsing: the case of French
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
What to do when lexicalization fails: parsing German with suffix analysis and smoothing
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Design of a multi-lingual, parallel-processing statistical parsing engine
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Out-of-the-box robust parsing of Portuguese
PROPOR'10 Proceedings of the 9th international conference on Computational Processing of the Portuguese Language
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We present the first results for recovering word-word dependencies from a probabilistic parser for Portuguese trained on and evaluated against human annotated syntactic analyses. We use the Floresta Sintá(c)tica with the Bikel multi-lingual parsing engine and evaluate performance on both PARSEVAL and unlabeled dependencies. We explore several configurations, both in terms of parameterizing the parser and in terms of enhancements to the trees used for training the parser. Our best configuration achieves 80.6% dependency accuracy on unseen test material, well above adjacency baselines and on par with previous results for unlabeled dependencies.