Shallow parsing using noisy and non-stationary training material
The Journal of Machine Learning Research
On the evaluation and comparison of taggers: the effect of noise in testing corpora
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
A generative constituent-context model for improved grammar induction
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Inductive Dependency Parsing (Text, Speech and Language Technology)
Inductive Dependency Parsing (Text, Speech and Language Technology)
Detecting errors in discontinuous structural annotation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Dependency parsing by inference over high-recall dependency predictions
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Multilingual dependency analysis with a two-stage discriminative parser
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Learning reliable information for dependency parsing adaptation
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
Reducing the need for double annotation
LAW V '11 Proceedings of the 5th Linguistic Annotation Workshop
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Building on work detecting errors in dependency annotation, we set out to correct local dependency errors. To do this, we outline the properties of annotation errors that make the task challenging and their existence problematic for learning. For the task, we define a feature-based model that explicitly accounts for non-relations between words, and then use ambiguities from one model to constrain a second, more relaxed model. In this way, we are successfully able to correct many errors, in a way which is potentially applicable to dependency parsing more generally.