@inproceedings{dai-etal-2021-ji,
    title = "基于信息交互增强的时序关系联合识别(Joint Recognition of Temporal Relation Based on Information Interaction Enhancement)",
    author = "Dai, Qianying  and
      Kong, Fang",
    editor = "Li, Sheng  and
      Sun, Maosong  and
      Liu, Yang  and
      Wu, Hua  and
      Liu, Kang  and
      Che, Wanxiang  and
      He, Shizhu  and
      Rao, Gaoqi",
    booktitle = "Proceedings of the 20th Chinese National Conference on Computational Linguistics",
    month = aug,
    year = "2021",
    address = "Huhhot, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.ccl-1.72/",
    pages = "803--812",
    language = "zho",
    abstract = "时序关系识别是信息抽取领域的一个重要分支,对文本理解发挥着关键作用。按照关联对象的不同,时序关系分为三大类:事件对(E-E)间的时序关系,事件与时间表达式间(E-T)的时序关系,事件与文档建立时间(E-D)间的时序关系。不同关系类型孤立识别的方法忽视了其间隐含的关联信息,针对这一问题构建了基于信息交互增强的时序关系联合识别模型。通过在不同神经网络层之间共享参数实现E-E与E-T时序关系的语义交流,利用两者的潜在联系提高识别精度。在Time-Bank Dense语料上的一系列实验表明,该方法优于现有的大多数神经网络方法。"
}