计算机科学 ›› 2023, Vol. 50 ›› Issue (3): 42-48.doi: 10.11896/jsjkx.220600239
• 知识图谱赋能的知识工程:理论、技术与系统专题 • 上一篇 下一篇
邓亮1,2,3, 齐攀虎4, 刘振龙4, 李敬鑫4, 唐积强5
DENG Liang1,2,3, QI Panhu4, LIU Zhenlong4, LI Jingxin4, TANG Jiqiang5
摘要： 实体-关系联合抽取指从非结构化文本中联合抽取出实体-关系三元组,是信息抽取和知识图谱构建的一项关键任务。文中提出了一种新的基于全局指针网络实体关系联合抽取方法BGPNRE(BERT-based Global Pointer Network for Named Entity-Relation Joint Extraction),首先通过潜在关系预测模块预测文本中蕴含的关系,过滤掉不可能存在的关系,将实体抽取限制在预测的关系子集中;其次通过使用基于关系的全局指针网络,获取所有主客体实体的位置;最后通过全局指针网络通信模块,将主客体位置高效率地解码对齐成一个实体关系三元组。该方法避免了传统管道式方法存在的错误传播问题,同时也解决了关系冗余、实体重叠、Span提取泛化不足等问题。实验结果表明,所提方法在多关系和重叠实体抽取上表现卓越,并且在NYT和WebNLG公共数据集上达到了最先进的水平。
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