@inproceedings{cui-etal-2022-ji,
    title = "基于{S}oft{L}exicon和注意力机制的中文因果关系抽取({C}hinese Causality Extraction Based on {S}oft{L}exicon and Attention Mechanism)",
    author = "Cui, Shilin  and
      Yan, Rong",
    editor = "Sun, Maosong  and
      Liu, Yang  and
      Che, Wanxiang  and
      Feng, Yang  and
      Qiu, Xipeng  and
      Rao, Gaoqi  and
      Chen, Yubo",
    booktitle = "Proceedings of the 21st Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Nanchang, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.ccl-1.18/",
    pages = "190--200",
    language = "zho",
    abstract = "``针对现有中文因果关系抽取方法对因果事件边界难以识别和文本特征表示不充分的问题,提出了一种基于外部词汇信息和注意力机制的中文因果关系抽取模型BiLSTM-TWAM+CRF。该模型首次使用SoftLexicon方法引入外部词汇信息构建词集,解决了因果事件边界难以识别的问题。通过构建的双路关注模块TWAM(Two Way Attention Module),实现了从局部和全局两个角度充分刻画文本特征。实验结果表明,与当前中文因果关系抽取模型相比较,本文方法表现出更优的抽取效果。''"
}