@inproceedings{tong-etal-2023-rong,
    title = "融合{S}ynonyms 词库的专利语义相似度计算研究(Patent Semantic Similarity Calculation by Fusing Synonyms Database)",
    author = "Tong, Xinyu  and
      Liao, Jialun  and
      Lu, Yonghe",
    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/Author-page-Marten-During-lu/2023.ccl-1.30/",
    pages = "341--351",
    language = "zho",
    abstract = "{\textquotedblleft}一直以来,专利相似度计算和比较等工作都由专利审查员人工进行并做出准确判断。然而,以人工方式分析和研判专利的原创性、实用性以及是否侵权等工作需要投入大量的人力物力资源且效率较低。基于此,本文将ALBERT预训练模型用于专利的文本表示,并通过引入Synonyms近义词库增强专利文本的语义表达能力,探索一种基于语义知识库和深度学习的专利文本表示模型与相似度计算方法。实验结果表明,加入Synonyms近义词库消歧后的专利文本相似性度量的实验准确率有一定的提升。{\textquotedblright}"
}