The syntactic process
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Supertagging: an approach to almost parsing
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Investigating GIS and smoothing for maximum entropy taggers
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Deterministic dependency parsing of English text
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
The importance of supertagging for wide-coverage CCG parsing
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Wide-coverage semantic representations from a CCG parser
COLING '04 Proceedings of the 20th international conference on 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
Evaluating the accuracy of an unlexicalized statistical parser on the PARC DepBank
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank
Computational Linguistics
Wide-coverage efficient statistical parsing with ccg and log-linear models
Computational Linguistics
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
Improving the efficiency of a wide-coverage CCG parser
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Identifying interesting assertions from the web
Proceedings of the 18th ACM conference on Information and knowledge management
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Faster parsing by supertagger adaptation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Computational linguistics and natural language processing
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Beam-width prediction for efficient context-free parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Efficient CCG parsing: A* versus adaptive supertagging
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Joint training of dependency parsing filters through latent support vector machines
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
Finite-state chart constraints for reduced complexity context-free parsing pipelines
Computational Linguistics
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Given the increasing need to process massive amounts of textual data, efficiency of NLP tools is becoming a pressing concern. Parsers based on lexicalised grammar formalisms, such as TAG and CCG, can be made more efficient using supertagging, which for CCG is so effective that every derivation consistent with the supertagger output can be stored in a packed chart. However, wide-coverage CCG parsers still produce a very large number of derivations for typical newspaper or Wikipedia sentences. In this paper we investigate two forms of chart pruning, and develop a novel method for pruning complete cells in a parse chart. The result is a wide-coverage CCG parser that can process almost 100 sentences per second, with little or no loss in accuracy over the baseline with no pruning.