@inproceedings{weiwei-etal-2024-ji,
    title = "基于大小模型结合与半监督自训练方法的古文事件抽取",
    author = "Weiwei, Fu  and
      Shiquan, Wang  and
      Ruiyu, Fang  and
      Mengxiang, Li  and
      Zhongjiang, He  and
      Yongxiang, Li  and
      Shuangyong, Song",
    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/jlcl-multiple-ingestion/2024.ccl-3.19/",
    pages = "172--177",
    language = "zho",
    abstract = "{\textquotedblleft}本文描述了队伍{\textquotedblleft}TeleAI{\textquotedblright}在CCL2024古文历史事件类型抽取评测任务(CHED2024)中提交的参赛系统。该任务旨在自动识别出古代文本中的事件触发词与事件类型,其中事件类型判别被分为粗粒度和细粒度的事件类型判别两部分。为了提高古文历史事件类型抽取的性能,我们结合了大模型和小模型,并采用了半监督自训练的方法。在最终的评估中,我们在触发词识别任务得分0.763,粗粒度事件类型判别任务得分0.842,细粒度事件类型判别任务得分0.779,综合得分0.791,在所有单项任务和综合评分上均排名第一。{\textquotedblright}"
}