Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (12): 25-36.DOI: 10.3778/j.issn.1002-8331.2003-0189

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Survey of Entity Relation Extraction

WANG Chuandong, XU Jiao, ZHANG Yong   

  1. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
  • Online:2020-06-15 Published:2020-06-09



  1. 南京邮电大学 计算机学院,南京 210023


As an important part of information extraction, entity relation extraction can perform semantic analysis on smaller grained information and provide basic data support for more tasks. The development of relation extraction can be divided into two methods based on traditional machine learning and deep learning. In recent years, the research based on traditional machine learning has mainly focused on the combination of statistic-based and rule-based. The framework of deep learning has achieved abundant research results by introducing distant supervision, few-shot learning, attention mechanism, reinforcement learning and multi-instance multilabel. The development of entity relation extraction is reviewed and each model is analyzed. Combing the latest developments in deep learning methods, the development direction and trend of entity relationship extraction are prospected.

Key words: entity relation extraction, machine learning, distant supervision, graph convolutional network, reinforcement learning, neural network



关键词: 实体关系抽取, 机器学习, 远程监督, 图卷积网络, 强化学习, 神经网络