Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Topological dependency trees: a constraint-based account of linear precedence
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Pseudo-projective dependency parsing
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Experiments with a multilanguage non-projective dependency parser
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Labeled pseudo-projective dependency parsing with support vector machines
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Dependency-based syntactic-semantic analysis with PropBank and NomBank
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
The CoNLL-2009 shared task: syntactic and semantic dependencies in multiple languages
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
Efficient parsing of syntactic and semantic dependency structures
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
A dependency-driven parser for German dependency and constituency representations
PaGe '08 Proceedings of the Workshop on Parsing German
The PaGe 2008 shared task on parsing German
PaGe '08 Proceedings of the Workshop on Parsing German
A latent variable model for generative dependency parsing
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Non-projective dependency parsing in expected linear time
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
Hard constraints for grammatical function labelling
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Random projection, margins, kernels, and feature-selection
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
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We investigate a series of targeted modifications to a data-driven dependency parser of German and show that these can be highly effective even for a relatively well studied language like German if they are made on a (linguistically and methodologically) informed basis and with a parser implementation that allows for fast and robust training and application. Making relatively small changes to a range of very different system components, we were able to increase labeled accuracy on a standard test set (from the CoNLL 2009 shared task), ignoring gold standard part-of-speech tags, from 87.64% to 89.40%. The study was conducted in less than five weeks and as a secondary project of all four authors. Effective modifications include the quality and combination of auto-assigned morphosyntactic features entering machine learning, the internal feature handling as well as the inclusion of global constraints and a combination of different parsing strategies.