Taku Kudo
ABSTRACT
Kernel-based learning (e.g., Support Vector Machines) has been successfully applied to many hard problems in Natural Language Processing (NLP). In NLP, although feature combinations are crucial to improving performance, they are heuristically selected. Kernel methods change this situation. The merit of the kernel methods is that effective feature combination is implicitly expanded without loss of generality and increasing the computational costs. Kernel-based text analysis shows an excellent performance in terms in accuracy; however, these methods are usually too slow to apply to large-scale text analysis. In this paper, we extend a Basket Mining algorithm to convert a kernel-based classifier into a simple and fast linear classifier. Experimental results on English BaseNP Chunking, Japanese Word Segmentation and Japanese Dependency Parsing show that our new classifiers are about 30 to 300 times faster than the standard kernel-based classifiers.
- Junichi Aoe. 1989. An efficient digital search algorithm by using a double-array structure. IEEE Transactions on Software Engineering, 15(9). Google Scholar
Digital Library
- Michael Collins and Nigel Duffy. 2001. Convolution kernels for natural language. In Advances in Neural Information Processing Systems 14, Vol.1 (NIPS 2001), pages 625--632.Google Scholar
- Hideki Isozaki and Hideto Kazawa. 2002. Efficient support vector classifiers for named entity recognition. In Proceedings of the COLING-2002, pages 390--396. Google ScholarDigital Library
- Hisashi Kashima and Teruo Koyanagi. 2002. Svm kernels for semi-structured data. In Proceedings of the ICML-2002, pages 291--298. Google ScholarDigital Library
- Taku Kudo and Yuji Matsumoto. 2000. Japanese Dependency Structure Analysis based on Support Vector Machines. In Proceedings of the EMNLP/VLC-2000, pages 18--25. Google ScholarDigital Library
- Taku Kudo and Yuji Matsumoto. 2001. Chunking with support vector machines. In Proceedings of the the NAACL, pages 192--199. Google ScholarDigital Library
- Taku Kudo and Yuji Matsumoto. 2002. Japanese dependency analyisis using cascaded chunking. In Proceedings of the CoNLL-2002, pages 63--69. Google ScholarDigital Library
- Sadao Kurohashi and Makoto Nagao. 1997. Kyoto University text corpus project. In Proceedings of the ANLP-1997, pages 115--118.Google Scholar
- Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, and Chris Watkins. 2002. Text classification using string kernels. Journal of Machine Learning Research, 2. Google ScholarDigital Library
- Tetsuji Nakagawa, Taku Kudo, and Yuji Matsumoto. 2002. Revision learning and its application to part-of-speech tagging. In Proceedings of the ACL 2002, pages 497--504. Google ScholarDigital Library
- Jian Pei, Jiawei Han, and et al. 2001. Prefixspan: Mining sequential patterns by prefix-projected growth. In Proc. of International Conference of Data Engineering, pages 215--224. Google ScholarDigital Library
- Lance A. Ramshaw and Mitchell P. Marcus. 1995. Text chunking using transformation-based learning. In Proceedings of the VLC, pages 88--94.Google Scholar
- Vladimir N. Vapnik. 1995. The Nature of Statistical Learning Theory. Springer. Google ScholarDigital Library
- Mohammed Zaki. 2002. Efficiently mining frequent trees in a forest. In Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining KDD, pages 71--80. Google ScholarDigital Library
- Fast methods for kernel-based text analysis
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