Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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
Design of a multi-lingual, parallel-processing statistical parsing engine
HLT '02 Proceedings of the second international conference on Human Language Technology Research
The impact of parse quality on syntactically-informed statistical machine translation
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Parser combination by reparsing
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Products of random latent variable grammars
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Self-training with products of latent variable grammars
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Heterogeneous parsing via collaborative decoding
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Bayesian symbol-refined tree substitution grammars for syntactic parsing
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Higher-order constituent parsing and parser combination
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Combine constituent and dependency parsing via reranking
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Statistical parsing with probabilistic symbol-refined tree substitution grammars
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Combining the 1-best output of multiple parsers via parse selection or parse hybridization improves f-score over the best individual parser (Henderson and Brill, 1999; Sagae and Lavie, 2006). We propose three ways to improve upon existing methods for parser combination. First, we propose a method of parse hybridization that recombines context-free productions instead of constituents, thereby preserving the structure of the output of the individual parsers to a greater extent. Second, we propose an efficient linear-time algorithm for computing expected f-score using Minimum Bayes Risk parse selection. Third, we extend these parser combination methods from multiple 1-best outputs to multiple n-best outputs. We present results on WSJ section 23 and also on the English side of a Chinese-English parallel corpus.