2003 FastMethodsForKernelBasedTextAnalysis

• (Kudo and Matsumoto, 2003) ⇒ Taku Kudo, Y. Matsumoto. (2003). “Fast methods for kernel-based text analysis.” In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL 2003).

Subject Headings: YamCha System.

Cited by

~117 http://scholar.google.com/scholar?cites=3419142536471625906

Quotes

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.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2003 FastMethodsForKernelBasedTextAnalysis	Taku Kudo Y. Matsumoto			Fast methods for kernel-based text analysis		Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics	http://acl.ldc.upenn.edu/acl2003/main/pdfs/Kudo.pdf			2003