@inproceedings{yu-etal-2023-ji,
    title = "基于句法特征的事件要素抽取方法(Syntax-aware Event Argument Extraction )",
    author = "Yu, Zijian  and
      Zhu, Tong  and
      Chen, Wenliang",
    editor = "Sun, Maosong  and
      Qin, Bing  and
      Qiu, Xipeng  and
      Jiang, Jing  and
      Han, Xianpei",
    booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics",
    month = aug,
    year = "2023",
    address = "Harbin, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.ccl-1.18/",
    pages = "196--207",
    language = "zho",
    abstract = "``事件要素抽取(Event Argument Extraction, EAE)旨在从非结构化文本中提取事件参与要素。编码器{---}解码器(Encoder-Decoder)框架是处理该任务的一种常见策略,此前的研究大多只向编码器端输入文本的字词信息,导致模型泛化和远程依赖处理能力较弱。为此,本文提出一种融入句法信息的事件要素抽取模型。首先对文本分析得到成分句法解析树,将词性标签和各节点的句法成分标签编码,增强模型的文本表征能力。然后,本文提出了一种基于树结构的注意力机制(Tree-Attention)辅助模型更好地感知结构化语义信息,提高模型处理远距离依赖的能力。实验结果表明,本文所提方法相较于基线系统F1值提升2.02{\%},证明该方法的有效性。''"
}