A Classification Algorithm for Chinese Verb Phrases Using Support Vector Machine

ABSTRACT

Chinese verb phrases classification is to determine boundaries of verb phrases and divide them exactly, using brackets, by automatically analyzing and processing by computer after the sentences have been decollated and marked the characteristic or property of a certain word. SVM classification model is a common and powerful for classification tasks. In this paper, the SVM classification model is built by extracting static features and dynamic features of Chinese verb phrases, and an algorithm to perform Chinese verb phrases classification using support vector machine is proposed. Using 3500 sentences to train and test, experiment results show that the SVM model dramatically reduces the training time and steps. Compared with the method proposed in literature 15, classification precision rate is increased by approximately 8.0% using the algorithm in this paper, which fully illustrates that the performance of the proposed algorithm is superior classification algorithm.

Keywords:

Feature extraction, machine learning, support vector machine, vector space model, verb phrase classification.