Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Fast methods for kernel-based text analysis
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
An approximate approach for training polynomial kernel SVMs in linear time
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Using a maximum entropy-based tagger to improve a very fast vine parser
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Polynomial to linear: efficient classification with conjunctive features
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM
The Journal of Machine Learning Research
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Kernel slicing: scalable online training with conjunctive features
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Finite rank kernels for multi-task learning
Advances in Computational Mathematics
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We present a fast, space efficient and non-heuristic method for calculating the decision function of polynomial kernel classifiers for NLP applications. We apply the method to the MaltParser system, resulting in a Java parser that parses over 50 sentences per second on modest hardware without loss of accuracy (a 30 time speedup over existing methods). The method implementation is available as the open-source splitSVM Java library.