@inproceedings{zong-etal-2025-ccl25,
    title = "{CCL}25-Eval 任务6系统报告:基于数据增强及大小模型协同的中小学作文修辞识别",
    author = "Zong, Xuquan  and
      An, Jiyuan  and
      Fu, Xiang  and
      Lu, Luming  and
      Zhu, Haonan  and
      Yang, Liner  and
      Yang, Erhong",
    editor = "Lin, Hongfei  and
      Li, Bin  and
      Tan, Hongye",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-2.28/",
    pages = "233--243",
    abstract = "``CCL25-Eval任务6提出了一个段落级、多层次,细粒度中小学修辞识别与理解任务。针对修辞分类任务的特点,本文构建了一种以数据增强为核心、结合高效监督微调的多策略融合框架,并融合语句层面修辞识别与段落句间关系建模及识别,以全面提升模型的修辞理解能力。针对修辞成分抽取任务的特点,本文采用先进行修辞类别判定,后在该基础上进行修辞相关实体识别的两阶段处理策略,有效提升了整体识别精度。结果表明,本文所提出的方法能够有效对修辞进行识别和抽取,三个赛道上的分数分别达到了43.47、51.71、38.27,总成绩位列第二。''"
}