@inproceedings{wenjun-etal-2024-ji,
    title = "基于增量预训练与外部知识的古文历史事件检测",
    author = "Wenjun, Kang  and
      Jiali, Zuo  and
      Yiyu, Hu  and
      Mingwen, Wang",
    editor = "Lin, Hongfei  and
      Tan, Hongye  and
      Li, Bin",
    booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
    month = jul,
    year = "2024",
    address = "Taiyuan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.ccl-3.21/",
    pages = "191--200",
    language = "zho",
    abstract = "``古文历史事件检测任务旨在识别文本中的事件触发词和类型。为了解决传统pipeline方法容易产生级联错误传播,以及大多数事件检测方法仅依赖句子层面信息的问题,本文提出了一种结合外部信息和全局对应矩阵的联合抽取模型EIGC,以实现触发词和事件类型的精确抽取。此外,本文还整理了一个包含{``}二十四史{''}等古汉语文献的数据集,共计约97万条古汉语文本,并利用该文本对BERT-Ancient-Chinese进行增量预训练。最终,本文所提出的模型在三个任务上的总F1值达到了76.2{\%},验证了该方法的有效性。''"
}