ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (7): 1424-1448.doi: 10.7544/issn1000-1239.2020.20190358

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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).

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

CLC Number: