SVM-based chunk recognition and transformation-based error-driver learning

The paper presents a method of Chinese chunk recognition based on Support Vector Machines(SVM) and transformation-based error-driven learning. It is well known that SVM is good at achieving high generalization of very high dimensional feature space. SVM can be trained in a high dimensional space with smaller computational cost independent of their dimensionalities. Transformation-based learning method can combine many kinds features and express much knowledge of linguistic which is very important to other research. Using SVM method and Transformation-based learning method can combine the advantages and get a satisfaction of identification.