Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Detecting errors within a corpus using anomaly detection
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
ACM Transactions on Asian Language Information Processing (TALIP)
Detecting errors in part-of-speech annotation
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Detecting errors in corpora using support vector machines
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Learning non-isomorphic tree mappings for machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Sentence compression as tree transduction
Journal of Artificial Intelligence Research
Using derivation trees for treebank error detection
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Simultaneous error detection at two levels of syntactic annotation
LAW VI '12 Proceedings of the Sixth Linguistic Annotation Workshop
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This paper proposes a method of correcting annotation errors in a treebank. By using a synchronous grammar, the method transforms parse trees containing annotation errors into the ones whose errors are corrected. The synchronous grammar is automatically induced from the treebank. We report an experimental result of applying our method to the Penn Treebank. The result demonstrates that our method corrects syntactic annotation errors with high precision.