HLT '91 Proceedings of the workshop on Speech and Natural Language
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
New figures of merit for best-first probabilistic chart parsing
Computational Linguistics
PCFG models of linguistic tree representations
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A parsing: fast exact Viterbi parse selection
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Learning as search optimization: approximate large margin methods for structured prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Probabilistic CFG with latent annotations
ACL '05 Proceedings of the 43rd Annual Meeting on Association for 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
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
Algorithms for deterministic incremental dependency parsing
Computational Linguistics
Classifying chart cells for quadratic complexity context-free inference
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Learning and inference for hierarchically split PCFGs
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Linear complexity context-free parsing pipelines via chart constraints
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Learning to translate with source and target syntax
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Uptraining for accurate deterministic question parsing
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Chart pruning for fast lexicalised-grammar parsing
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Unary constraints for efficient context-free parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Unary constraints for efficient context-free parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Efficient matrix-encoded grammars and low latency parallelization strategies for CYK
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
Robust kaomoji detection in Twitter
LSM '12 Proceedings of the Second Workshop on Language in Social Media
Generalized higher-order dependency parsing with cube pruning
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Finite-state chart constraints for reduced complexity context-free parsing pipelines
Computational Linguistics
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Efficient decoding for syntactic parsing has become a necessary research area as statistical grammars grow in accuracy and size and as more NLP applications leverage syntactic analyses. We review prior methods for pruning and then present a new framework that unifies their strengths into a single approach. Using a log linear model, we learn the optimal beam-search pruning parameters for each CYK chart cell, effectively predicting the most promising areas of the model space to explore. We demonstrate that our method is faster than coarse-to-fine pruning, exemplified in both the Charniak and Berkeley parsers, by empirically comparing our parser to the Berkeley parser using the same grammar and under identical operating conditions.