Research on Chinese Semantic Role Labeling with Hierarchical Syntactic Clues

Abstract

As a key task in the process of natural language understanding, semantic role labeling has been widely used in the field of natural language processing at a higher level, such as information extraction, text analysis and machine translation. This paper adopts the current mainstream semantic character annotation method based on syntactic tree analysis, proposes a Chinese semantic role labeling method that integrates hierarchical syntactic cues, and implements a hierarchical annotation model based on conditional random field classifier. On the basis of the model, the influence of different syntactic features on system performance is discussed. Different feature sets are formulated for verb predicate and noun predicate respectively, and the contribution of each feature to the system is analyzed. The experimental results show that the introduction of phrase syntax analysis can effectively improve the recognition effect of semantic roles. The results obtained in this study have a good reference value for future research.