Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Filtering-Ranking Perceptron Learning for Partial Parsing
Machine Learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for 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
A joint model for parsing syntactic and semantic dependencies
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
A second-order joint eisner model for syntactic and semantic dependency parsing
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Nbest dependency parsing with linguistically rich models
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Dependency parsing with second-order feature maps and annotated semantic information
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Dependency parsing and projection based on word-pair classification
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Syntactic processing using the generalized perceptron and beam search
Computational Linguistics
Getting more from morphology in multilingual dependency parsing
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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We describe an online learning dependency parser for the CoNLL-X Shared Task, based on the bottom-up projective algorithm of Eisner (2000). We experiment with a large feature set that models: the tokens involved in dependencies and their immediate context, the surface-text distance between tokens, and the syntactic context dominated by each dependency. In experiments, the treatment of multilingual information was totally blind.