Pseudo-projective dependency parsing
ACL '05 Proceedings of the 43rd 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
Mildly non-projective dependency structures
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
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
A latent variable model for generative dependency parsing
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Concise integer linear programming formulations for dependency parsing
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
Dynamic programming for linear-time incremental parsing
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A transition-based parser for 2-planar dependency structures
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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We introduce a new approach to transition-based dependency parsing in which the parser does not directly construct a dependency structure, but rather an undirected graph, which is then converted into a directed dependency tree in a post-processing step. This alleviates error propagation, since undirected parsers do not need to observe the single-head constraint. Undirected parsers can be obtained by simplifying existing transition-based parsers satisfying certain conditions. We apply this approach to obtain undirected variants of the planar and 2-planar parsers and of Covington's non-projective parser. We perform experiments on several datasets from the CoNLL-X shared task, showing that these variants outperform the original directed algorithms in most of the cases.