Abstract

"Chinese Frame Semantic Parsing (CFSP) aims to extract fine-grained frame semantic structures from text, providing rich semantic information to enhance the capabilities of natural language understanding models in semantic representation and downstream applications. Building on the CCL-2024 CFSP evaluation task and motivated by the prevalent phenomenon of semantic roles nesting in sentences, we update the nested role annotation data by simultaneously labeling all nested semantic roles. Based on this enhancement, we publish a more challenging CFSP evaluation task for CCL-2025. The evaluation dataset consists of 22,000 annotated examples involving 703 frames, including nested annotations covering 101 semantic roles. The evaluation task, divided into three subtasks: frame identification, argument identification, and role identification, has attracted wide attention from both industry and academia, with a total of 156 teams participating. As for the evaluation results, Yongqing Huang from Guangdong Province won first place with a final score of 70.76.In this paper, we report key information about the evaluation task, including key concepts, evaluation dataset, top-3 results and corresponding methods. More information about this task can be found on the website for the CCL-2025 CFSP evaluation task."