@inproceedings{yaozong-etal-2025-ji,
    title = "基于大模型增强的两阶段高效事件共指消解方法",
    author = "Yaozong, Wu  and
      Qi, Shuai  and
      Wang, Fangyuan  and
      Bao, Chenlong  and
      Tang, Jin-Tao",
    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-1.7/",
    pages = "77--88",
    abstract = "``本文针对两阶段事件共指消解方法存在的触发词词目启发机制缺乏同义词聚类能力和小模型理解触发词指代事件能力有限等问题,提出了一种基于大模型增强的两阶段高效的事件共指消解方法,一阶段引入大模型进行同义词聚类,二阶段大模型提供触发词解释文本增强小模型。此外,设计了引导小模型侧重触发词特征向量的损失函数。本文方法在保持近似线性时间复杂度的同时,在ECB+和GVC数据集上的CoNLLF1得分分别提升了2.9和8.0。''"
}