Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Discriminative Reranking for Natural Language Parsing
Computational Linguistics
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Learning accurate, compact, and interpretable tree annotation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Effective self-training for parsing
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
K-best combination of syntactic parsers
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Event extraction as dependency parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Event extraction as dependency parsing for BioNLP 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Exploiting parse structures for native language identification
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Statistical dependency parsing in Korean: from corpus generation to automatic parsing
SPMRL '11 Proceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages
Low-dimensional discriminative reranking
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Chinese-English mixed text normalization
Proceedings of the 7th ACM international conference on Web search and data mining
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The Brown and the Berkeley parsers are two state-of-the-art generative parsers. Since both parsers produce n-best lists, it is possible to apply reranking techniques to the output of both of these parsers, and to their union. We note that the standard reranker feature set distributed with the Brown parser does not do well with the Berkeley parser, and propose an extended set that does better. An ablation experiment shows that different parsers benefit from different reranker features.