Pseudo-projective dependency parsing
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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
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
An improved oracle for dependency parsing with online reordering
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Automatic discovery of feature sets for dependency parsing
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Data-driven systems for natural language processing have the advantage that they can easily be ported to any language or domain for which appropriate training data can be found. However, many data-driven systems require careful tuning in order to achieve optimal performance, which may require specialized knowledge of the system. We present MaltOptimizer, a tool developed to facilitate optimization of parsers developed using MaltParser, a data-driven dependency parser generator. MaltOptimizer performs an analysis of the training data and guides the user through a three-phase optimization process, but it can also be used to perform completely automatic optimization. Experiments show that MaltOptimizer can improve parsing accuracy by up to 9 percent absolute (labeled attachment score) compared to default settings. During the demo session, we will run MaltOptimizer on different data sets (user-supplied if possible) and show how the user can interact with the system and track the improvement in parsing accuracy.