More accurate tests for the statistical significance of result differences
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Error mining for wide-coverage grammar engineering
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Effective self-training for parsing
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Using self-trained bilexical preferences to improve disambiguation accuracy
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Cross-domain dependency parsing using a deep linguistic grammar
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
A generalized method for iterative error mining in parsing results
GEAF '09 Proceedings of the 2009 Workshop on Grammar Engineering Across Frameworks
Adapting a probabilistic disambiguation model of an HPSG parser to a new domain
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Effective measures of domain similarity for parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Minimally supervised domain-adaptive parse reranking for relation extraction
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
Detecting dependency parse errors with minimal resources
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
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In the past decade several parsing systems for natural language have emerged, which use different methods and formalisms. For instance, systems that employ a handcrafted grammar and a statistical disambiguation component versus purely statistical data-driven systems. What they have in common is the lack of portability to new domains: their performance might decrease substantially as the distance between test and training domain increases. Yet, to which degree do they suffer from this problem, i.e. which kind of parsing system is more affected by domain shifts? Intuitively, grammar-driven systems should be less affected by domain changes. To investigate this hypothesis, an empirical investigation on Dutch is carried out. The performance variation of a grammar-driven versus two data-driven systems across domains is evaluated, and a simple measure to quantify domain sensitivity proposed. This will give an estimate of which parsing system is more affected by domain shifts, and thus more in need for adaptation techniques.