@inproceedings{lv-etal-2025-ji,
    title = "基于大语言模型的中文医学命名实体识别",
    author = "Lv, Tengxiao  and
      Luo, Ling  and
      Lv, Huiyi  and
      Sun, Yuanyuan  and
      Wang, Jian  and
      Lin, Hongfei",
    editor = "Sun, Maosong  and
      Duan, Peiyong  and
      Liu, Zhiyuan  and
      Xu, Ruifeng  and
      Sun, Weiwei",
    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-1.9/",
    pages = "102--116",
    abstract = "``从中文文本中准确识别医学命名实体是实现中文医疗信息结构化的关键。传统机器学习方法在面对中文医学实体边界模糊和嵌套结构复杂等问题时效果有限。本文提出一种基于大语言模型的中文医学命名实体识别方法,首先通过任务重构将识别过程转化为文本生成任务,设计了适配的标注策略以统一处理平面与嵌套实体,然后引入实体筛选器过滤错误候选实体,最后通过大语言模型决策进行冲突消解与多模型集成提升系统整体鲁棒性。在CMeEE-V2与CCKS2019两个数据集上实验结果显示,所提方法在识别准确性与鲁棒性方面均达到当前先进水平,F1值分别为0.7785和0.8821。''"
}