计算机科学 ›› 2023, Vol. 50 ›› Issue (10): 223-229.doi: 10.11896/jsjkx.220900108
文坤建, 陈艳平, 黄瑞章, 秦永彬
WEN Kunjian, CHEN Yanping, HUANG Ruizhang, QIN Yongbin
摘要： 在非结构化生物医学文本数据中提取出实体之间的关系,对生物医学的信息化发展有着重大意义,同时也是自然语言处理领域的研究热点。目前,在生物医学数据中正确地提取出实体间的关系面临着两个难点:1)由于在生物医学数据中实体单词大多由复合词、未知词组成,模型难以学习到实体内部的语义特征;2)由于生物医学带标注数据较少,而神经网络的参数量较大,使得神经网络容易过拟合。因此,文中提出了基于提示学习的生物医学关系抽取方法,增加了一种针对实体的注解标签,来对实体进行提示以达到实体语义增强以及联系上下文信息的目的。此外,在传统提示调优方法的基础上,文中使用连续性模板来缓解人工设计模板所带来的性能偏差,同时结合深度前缀控制attention的深度提示能力,使模型在处理较少数据的情况时仍能取得良好的效果。
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