The syntactic process
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A statistical parser for Czech
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Building deep dependency structures with a wide-coverage CCG parser
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Generative models for statistical parsing with Combinatory Categorial Grammar
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Parsing the WSJ using CCG and log-linear models
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Enriching the output of a parser using memory-based learning
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Deep linguistic analysis for the accurate identification of predicate-argument relations
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank
Computational Linguistics
Feature forest models for probabilistic hpsg parsing
Computational Linguistics
Wide-coverage deep statistical parsing using automatic dependency structure annotation
Computational Linguistics
Deterministic shift-reduce parsing for unification-based grammars by using default unification
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Extremely lexicalized models for accurate and fast HPSG parsing
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A log-linear model with an n-gram reference distribution for accurate HPSG parsing
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Porting a lexicalized-grammar parser to the biomedical domain
Journal of Biomedical Informatics
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
SCHWA: PETE using CCG dependencies with the C&C parser
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Research on Language and Computation
Modeling morphosyntactic agreement in constituency-based parsing of modern Hebrew
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
Syntax-based grammaticality improvement using CCG and guided search
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Dependency hashing for n-best CCG parsing
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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The model used by the CCG parser of Hockenmaier and Steedman (2002b) would fail to capture the correct bilexical dependencies in a language with freer word order, such as Dutch. This paper argues that probabilistic parsers should therefore model the dependencies in the predicate-argument structure, as in the model of Clark et al. (2002), and defines a generative model for CCG derivations that captures these dependencies, including bounded and unbounded long-range dependencies.