@inproceedings{jiewei-etal-2024-ji,
    title = "基于本体信息增强的人类表型概念识别(Ontology Information-augmented Human Phenotype Concept Recognition)",
    author = "Jiewei, Qi  and
      Ling, Luo  and
      Zhihao, Yang  and
      Jian, Wang  and
      Hongfei, Lin",
    editor = "Sun, Maosong  and
      Liang, Jiye  and
      Han, Xianpei  and
      Liu, Zhiyuan  and
      He, Yulan",
    booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
    month = jul,
    year = "2024",
    address = "Taiyuan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.ccl-1.43/",
    pages = "553--567",
    language = "zho",
    abstract = "{\textquotedblleft}从文本中自动识别人类表型概念对疾病分析具有重大意义。现存本体驱动的表型概念识别方法主要利用本体中概念名和同义词信息,并未充分考虑本体丰富信息。针对此问题,本文提出一种基于本体信息增强的人类表型概念识别方法,利用先进大语言模型进行数据增强,并设计本体向量增强的深度学习模型来提升概念识别性能。在GSC+和ID-68两个数据集上进行实验,结果表明本文提出方法能够利用本体丰富信息有效提升基线模型性能,取得了先进结果。{\textquotedblright}"
}