Survey of Entity Relationship Extraction Based on Deep Learning

被引：0
|
作者：
E H.-H. [1 ,2 ]
Zhang W.-J. [1 ,2 ]
Xiao S.-Q. [1 ,2 ]
Cheng R. [1 ,2 ]
Hu Y.-X. [1 ,2 ]
Zhou X.-S. [1 ,2 ]
Niu P.-Q. [1 ,2 ]
机构：
[1] Data Science and Service Center, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing
[2] Engineering Research Center of Information Networks of Ministry of Education, Beijing University of Posts and Telecommunications, Beijing
来源：
Ruan Jian Xue Bao/Journal of Software | 2019年 / 30卷 / 06期
基金：
国家重点研发计划;
关键词：
Deep learning; Entity relationship extraction; Generative adversarial network; Joint learning; Remote supervision;
D O I：
10.13328/j.cnki.jos.005817
中图分类号：
学科分类号：
摘要：
Entity relation extraction is a core task and an important part in the fields of information extraction, natural language understanding, and information retrieval. It can extract the semantic relationships between entity pairs from the texts. In recent years, the application of deep learning in the fields of joint learning, remote supervision has resulted in relatively abundant research results in relation extraction tasks. At present, entity relationship extraction technology based on deep learning has gradually exceeded the traditional methods which are based on features and kernel functions in terms of the depth of feature extraction and the accuracy. This paper focuses on the two fields of supervision and remote supervision. It systematically summarizes the research progress of Chinese and overseas scholars' deep relationship-based entity relationship extraction in recent years, and discusses and prospects future possible research directions as well. © Copyright 2019, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：1793 / 1818
页数：25
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