The syntactic process
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
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
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
The role of lexico-semantic feedback in open-domain textual question-answering
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Probabilistic disambiguation models for wide-coverage HPSG parsing
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
QuestionBank: creating a corpus of parse-annotated questions
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Multi-tagging for lexicalized-grammar parsing
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
The second release of the RASP system
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation 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
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Extremely lexicalized models for accurate and fast HPSG parsing
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Unbounded dependency recovery for parser evaluation
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Faster parsing by supertagger adaptation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Creating robust supervised classifiers via web-scale N-gram data
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Unsupervised parse selection for HPSG
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Reducing overdetections in a French symbolic grammar checker by classification
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Parsing natural language queries for life science knowledge
BioNLP '11 Proceedings of BioNLP 2011 Workshop
Semi-supervised CCG lexicon extension
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Minimally supervised domain-adaptive parse reranking for relation extraction
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
Improved parsing and POS tagging using inter-sentence consistency constraints
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Most state-of-the-art wide-coverage parsers are trained on newspaper text and suffer a loss of accuracy in other domains, making parser adaptation a pressing issue. In this paper we demonstrate that a CCG parser can be adapted to two new domains, biomedical text and questions for a QA system, by using manually-annotated training data at the pos and lexical category levels only. This approach achieves parser accuracy comparable to that on newspaper data without the need for annotated parse trees in the new domain. We find that retraining at the lexical category level yields a larger performance increase for questions than for biomedical text and analyze the two datasets to investigate why different domains might behave differently for parser adaptation.