山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (6): 16-25.doi: 10.6040/j.issn.1672-3961.0.2023.127
• 机器学习与数据挖掘 • 上一篇
郑泾飞,廖永新,王华珍,何霆*
ZHENG Jingfei, LIAO Yongxin, WANG Huazhen, HE Ting*
摘要： 为有效汇聚实体间信息,利用实体间路径信息判断语义关系,提出一种基于提及图和显式路径的文档级关系抽取方法。利用基于提及对类型的图构建方法,通过融合提及间天然存在的结构化信息,结合图注意力网络,实现实体间信息的汇聚;利用基于显式路径的关系推理方法,包括显式路径构建方法和启发式的路径特征融合方法两个子方法,通过显式方式构建关系推理路径,将推理路径分为句内推理路径、句间推理路径、直接推理路径,实现区分句内推理和句间推理,差异化融合路径特征,提高关系路径推理能力,增强关系抽取的准确度。在3个公开数据集上的对比试验表明,本方法在F1和Ign F1指标上较目前主流方法存在优越性,验证了基于提及图和显式路径的文档级关系抽取方法能够更有效地支持文档级关系抽取任务。
中图分类号:
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