Chunking with support vector machines

TLDR

SVMs achieve high generalization performance even in high‑dimensional feature spaces and can train with low computational overhead thanks to the kernel principle. The study applies SVMs to identify English base phrases (chunks). The method uses weighted voting of eight SVM‑based systems trained on distinct chunk representations. Experimental results demonstrate higher accuracy than prior methods.

Abstract

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.