Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Discriminative learning and spanning tree algorithms for dependency parsing
Discriminative learning and spanning tree algorithms for dependency parsing
Algorithms for deterministic incremental dependency parsing
Computational Linguistics
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Incrementality in deterministic dependency parsing
IncrementParsing '04 Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together
Parser combination by reparsing
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Bilingually-constrained (monolingual) shift-reduce parsing
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
The simple truth about dependency and phrase structure representations: an opinion piece
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Detecting errors in automatically-parsed dependency relations
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Detecting dependency parse errors with minimal resources
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
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We propose the notion of a structural bias inherent in a parsing system with respect to the language it is aiming to parse. This structural bias characterizes the behaviour of a parsing system in terms of structures it tends to under- and over- produce. We propose a Boosting-based method for uncovering some of the structural bias inherent in parsing systems. We then apply our method to four English dependency parsers (an Arc-Eager and Arc-Standard transition-based parsers, and first- and second-order graph-based parsers). We show that all four parsers are biased with respect to the kind of annotation they are trained to parse. We present a detailed analysis of the biases that highlights specific differences and commonalities between the parsing systems, and improves our understanding of their strengths and weaknesses.