The syntactic process
Supertagging: an approach to almost parsing
Computational Linguistics
Efficient normal-form parsing for combinatory categorial grammar
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Example selection for bootstrapping statistical parsers
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Unsupervised Multilingual Sentence Boundary Detection
Computational Linguistics
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
Porting a lexicalized-grammar parser to the biomedical domain
Journal of Biomedical Informatics
SCHWA: PETE using CCG dependencies with the C&C parser
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Cross-Domain Effects on Parse Selection for Precision Grammars
Research on Language and Computation
Data mining from a patient safety database: the lessons learned
Data Mining and Knowledge Discovery
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The vast majority of parser evaluation is conducted on the 1984 Wall Street Journal (WSJ). In-domain evaluation of this kind is important for system development, but gives little indication about how the parser will perform on many practical problems. Wikipedia is an interesting domain for parsing that has so far been under-explored. We present statistical parsing results that for the first time provide information about what sort of performance a user parsing Wikipedia text can expect. We find that the C&C parser's standard model is 4.3% less accurate on Wikipedia text, but that a simple self-training exercise reduces the gap to 3.8%. The self-training also speeds up the parser on newswire text by 20%.