Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
ACL '02 Proceedings of the 40th 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
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
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
SCHWA: PETE using CCG dependencies with the C&C parser
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Robust conversion of CCG derivations to phrase structure trees
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Iterative annotation transformation with predict-self reestimation for Chinese word segmentation
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Hi-index | 0.00 |
We compare the CCG parser of Clark and Curran (2007) with a state-of-the-art Penn Treebank (PTB) parser. An accuracy comparison is performed by converting the CCG derivations into PTB trees. We show that the conversion is extremely difficult to perform, but are able to fairly compare the parsers on a representative subset of the PTB test section, obtaining results for the CCG parser that are statistically no different to those for the Berkeley parser.