Abstract
In this paper, we apply Support Vector Machines (SVMs) to identify English base phrases (chunks).It is well-known that SVMs achieve high generalization performance even using input data with a high dimensional feature space.Furthermore, by introducing the Kernel principle, SVMs can carry out training with smaller computational cost independent of the dimensionality of the feature space.In order to improve accuracy, we also apply majority voting with 8 SVMs which are trained using distinct chunk representations.Experimental results show that our approach achieves better accuracy than other conventional frameworks.
