Applying Support Vector Machines to Chinese Shallow Parsing

Abstract

To be able to represent the whole hierarchical phrase structure, 10 types of Chinese chunks are defined. The paper presents a method of Chinese shallow Paring based on Support Vector machines (SVMs). Conventional recognition techniques based on Machine Learning have difficulty in selecting useful features as well as finding appropriate combination of selected features. SVMs can automatically focus on useful features and robustly handle a large feature set to develop models that maximize their generalizability. On the other hand, it is well known that SVMs achieve high generalization of very high dimensional feature space. Furthermore, by introducing the Kernel principle, SVMs can carry out the training in high-dimensional space with smaller computational cost independent of their dimensionality. The experiments produced promising results.