Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Wide-coverage efficient statistical parsing with ccg and log-linear models
Computational Linguistics
Pruning the search space of a hand-crafted parsing system with a probabilistic parser
DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
Efficiency in unification-based N-best parsing
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Improving data-driven dependency parsing using large-scale LFG grammars
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
A cross-lingual induction technique for German adverbial participles
NLPLING '10 Proceedings of the 2010 Workshop on NLP and Linguistics: Finding the Common Ground
Constraining robust constructions for broad-coverage parsing with precision grammars
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Parser evaluation over local and non-local deep dependencies in a large corpus
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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In this paper we present a method for greatly reducing parse times in LFG parsing, while at the same time maintaining parse accuracy. We evaluate the methodology on data from English, German and Norwegian and show that the same patterns hold across languages. We achieve a speedup of 67% on the English data and 49% on the German data. On a small amount of data for Norwegian, we achieve a speedup of 40%, although with more training data we expect this figure to increase.