Artificial Intelligence - Special volume on empirical methods
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Foundations of statistical natural language processing
Foundations of statistical natural language processing
The syntactic process
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Supertagging: an approach to almost parsing
Computational Linguistics
A lightweight dependency analyzer for partial parsing
Natural Language Engineering
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
Generative models for statistical parsing with Combinatory Categorial Grammar
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Parsing the WSJ using CCG and log-linear models
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
The importance of supertagging for wide-coverage CCG parsing
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
On the Identification of Goals in Stakeholders' Dialogs
Innovations for Requirement Analysis. From Stakeholders' Needs to Formal Designs
Linguistically motivated large-scale NLP with C&C and boxer
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Translation of Textual Specifications to Automata by Means of Discourse Context Modeling
REFSQ '09 Proceedings of the 15th International Working Conference on Requirements Engineering: Foundation for Software Quality
Perceptron training for a wide-coverage lexicalized-grammar parser
DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
TBL-improved non-deterministic segmentation and POS tagging for a Chinese parser
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Adapting a lexicalized-grammar parser to contrasting domains
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Improving the efficiency of a wide-coverage CCG parser
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
A syntactic resource for Thai: CG treebank
ALR7 Proceedings of the 7th Workshop on Asian Language Resources
Perceptron reranking for CCG realization
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Morphological analysis can improve a CCG parser for English
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Machine learning for high-quality tokenization replicating variable tokenization schemes
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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With performance above 97% accuracy for newspaper text, part of speech (POS) tagging might be considered a solved problem. Previous studies have shown that allowing the parser to resolve POS tag ambiguity does not improve performance. However, for grammar formalisms which use more fine-grained grammatical categories, for example TAG and CCG, tagging accuracy is much lower. In fact, for these formalisms, premature ambiguity resolution makes parsing infeasible.We describe a multi-tagging approach which maintains a suitable level of lexical category ambiguity for accurate and efficient CCG parsing. We extend this multi-tagging approach to the POS level to overcome errors introduced by automatically assigned POS tags. Although POS tagging accuracy seems high, maintaining some POS tag ambiguity in the language processing pipeline results in more accurate CCG supertagging.