@inproceedings{liu-etal-2021-mian,
    title = "面向对话文本的实体关系抽取(Entity Relation Extraction for Dialogue Text)",
    author = "Liu, Liang  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/add-emnlp-2024-awards/2021.ccl-1.31/",
    pages = "327--338",
    language = "zho",
    abstract = "实体关系抽取旨在从文本中抽取出实体之间的语义关系,是自然语言处理的一项基本任务。在新闻报道、维基百科等规范文本上该任务的研究相对丰富,已经取得了一定的效果,但面向对话文本的相关研究还处于起始阶段。相较于规范文本,用于实体关系抽取的对话语料规模较小,对话文本的有效特征难以捕获,这使得面向对话文本的实体关系抽取更具挑战。该文针对这一任务提出了一个基于Star-Transformer的实体关系抽取模型,通过融入高速网络进行信息桥接,并在此基础上融入交互信息和知识,最后使用多任务学习机制进一步提升模型的性能。在DialogRE公开数据集上实验得到F1值为55.7{\%},F1c值为52.3{\%},证明了提出方法的有效性。"
}