ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (7): 1424-1448.doi: 10.7544/issn1000-1239.2020.20190358

• 人工智能 • 上一篇    下一篇



  1. (北京林业大学信息学院 北京 100083) (国家林业草原林业智能信息处理工程技术研究中心 北京 100083) (
  • 出版日期: 2020-07-01
  • 基金资助: 

Review of Entity Relation Extraction Methods

Li Dongmei, Zhang Yang, Li Dongyuan, Lin Danqiong   

  1. (School of Information Science and Technology, Beijing Forestry University, Beijing 100083) (Engineering Research Center for Forestry-oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083)
  • Online: 2020-07-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61772078) and the Key Research and Development Program of Beijing (D171100001817003).

摘要: 在自然语言处理领域,信息抽取一直以来受到人们的关注.信息抽取主要包括3项子任务:实体抽取、关系抽取和事件抽取,而关系抽取是信息抽取领域的核心任务和重要环节.实体关系抽取的主要目标是从自然语言文本中识别并判定实体对之间存在的特定关系,这为智能检索、语义分析等提供了基础支持,有助于提高搜索效率,促进知识库的自动构建.综合阐述了实体关系抽取的发展历史,介绍了常用的中文和英文关系抽取工具和评价体系.主要从4个方面展开介绍了实体关系抽取方法,包括:早期的传统关系抽取方法、基于传统机器学习、基于深度学习和基于开放领域的关系抽取方法,总结了在不同历史阶段的主流研究方法以及相应的代表性成果,并对各种实体关系抽取技术进行对比分析.最后,对实体关系抽取的未来重点研究内容和发展趋势进行了总结和展望.

关键词: 自然语言处理, 实体关系抽取, 机器学习, 深度学习, 开放领域

Abstract: There is a phenomenon that information extraction has long been concerned by a lot of research works in the field of natural language processing. Information extraction mainly includes three sub-tasks: entity extraction, relation extraction and event extraction, among which relation extraction is the core mission and a great significant part of information extraction. Furthermore, the main goal of entity relation extraction is to identify and determine the specific relation between entity pairs from plenty of natural language texts, which provides fundamental support for intelligent retrieval, semantic analysis, etc, and improves both search efficiency and the automatic construction of the knowledge base. Then, we briefly expound the development of entity relation extraction and introduce several tools and evaluation systems of relation extraction in both Chinese and English. In addition, four main methods of entity relation extraction are mentioned in this paper, including traditional relation extraction methods, and other three methods respectively based on traditional machine learning, deep learning and open domain. What is more important is that we summarize the mainstream research methods and corresponding representative results in different historical stages, and conduct contrastive analysis concerning different entity relation extraction methods. In the end, we forecast the contents and trend of future research.

Key words: natural language processing, entity relation extraction, machine learning, deep learning, open domain