Chinese Semantic Role Labeling Based on Feature Combination
基于特征组合的中文语义角色标注

This paper proposes a semantic role labeling (SRL) approach for the Chinese, based on feature combination and support vector machine (SVM). The approach takes the constituent as the labeling unit. First, this paper defines the basic feature set by selecting the high-performance features of existing parsing-based SRL systems. Then, a statistics-based method is proposed to construct a combined feature set derived from the basic feature set. According to the distribution of combining features in both positive and negative instances, the ratio of between-class to within-class distance is utilized as the measurement of classifying the performance the feature, and then choosing the combining features with high ratios into the combining feature set. Finally, the experimental results show that the feature combination method-based SRL achieved 91.81% F-score on Chinese PropBank (CPB) corpus, nearly 2% higher than the traditional method.