Chunking with Support Vector Machines
Chunking with Support Vector Machines
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DOI：
10.3115/1073336.1073361
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发表时间：
2001-06
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影响因子：
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通讯作者：
Taku Kudo;Yuji Matsumoto
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文献类型：
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作者：
Taku Kudo;Yuji Matsumoto
我们应用支持向量机 (SVM) 来识别英语基本短语（词块）。众所周知，即使输入数据是高维特征空间，SVM 也能实现较高的泛化性能。此外，根据核原理，SVM 可以以较小的计算开销进行训练，而与维度无关。我们对 8 个基于 SVM 的系统应用加权投票，这些系统经过不同的块表示训练。实验结果表明，我们的方法比以前的方法具有更高的准确性。
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 SVMs-based systems trained with distinct chunk representations. Experimental results show that our approach achieves higher accuracy than previous approaches.