Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Multilingual dependency parsing using Bayes Point Machines
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Discriminative learning and spanning tree algorithms for dependency parsing
Discriminative learning and spanning tree algorithms for dependency parsing
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Chinese dependency parsing with large scale automatically constructed case structures
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Cross language dependency parsing using a bilingual lexicon
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
An empirical study of semi-supervised structured conditional models for dependency parsing
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Improving dependency parsing with subtrees from auto-parsed data
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Beyond chart parsing: an analytic comparison of dependency chart parsing algorithms
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
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This paper proposes an approach to improve graph-based dependency parsing by using decision history. We introduce a mechanism that considers short dependencies computed in the earlier stages of parsing to improve the accuracy of long dependencies in the later stages. This relies on the fact that short dependencies are generally more accurate than long dependencies in graph-based models and may be used as features to help parse long dependencies. The mechanism can easily be implemented by modifying a graph-based parsing model and introducing a set of new features. The experimental results show that our system achieves state-of-the-art accuracy on the standard PTB test set for English and the standard Penn Chinese Treebank (CTB) test set for Chinese.