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NAACL
2001

Chunking with Support Vector Machines

14 years 1 months ago
Chunking with Support Vector Machines
We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead independent of their dimensionality. We apply weighted voting of 8 SVMsbased systems trained with distinct chunk representations. Experimental results show that our approach achieves higher accuracy than previous approaches.
Taku Kudo, Yuji Matsumoto
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2001
Where NAACL
Authors Taku Kudo, Yuji Matsumoto
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