The domain dependence of parsing
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
A transformational-based learner for dependency grammars in discharge summaries
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Treebank grammar techniques for non-projective dependency parsing
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Correcting a PoS-tagged corpus using three complementary methods
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Using a dependency parser to improve SMT for subject-object-verb languages
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Parsed corpora for linguistics
ILCL '09 Proceedings of the EACL 2009 Workshop on the Interaction between Linguistics and Computational Linguistics: Virtuous, Vicious or Vacuous?
Corrective modeling for non-projective dependency parsing
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
DEPEVAL(summ): dependency-based evaluation for automatic summaries
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
Inspecting the structural biases of dependency parsing algorithms
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Detecting dependency parse errors with minimal resources
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
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We outline different methods to detect errors in automatically-parsed dependency corpora, by comparing so-called dependency rules to their representation in the training data and flagging anomalous ones. By comparing each new rule to every relevant rule from training, we can identify parts of parse trees which are likely erroneous. Even the relatively simple methods of comparison we propose show promise for speeding up the annotation process.