电子科技2025，Vol.38Issue(7)：40-49,10.DOI:10.16180/j.cnki.issn1007-7820.2025.07.006
基于深度学习的关系抽取研究进展
Research Progress of Relation Extraction Based on Deep Learning
摘要
Abstract
In natural language processing,as the core task,the research direction of entity relation extraction task has gradually shifted from rule-based learning and traditional machine learning to deep learning.At present,deep learning relationship extraction models widely use convolutional neural networks,recurrent neural networks and graph neural networks.This study summarizes the excellent relationship extraction models in each neural network,shows the evolution direction of each model by tracing the development history and trend of the model,and makes a comparative analysis of each method and model.Due to the continuous improvement of attention mechanism and other methods,the semantic analysis ability of relational extraction model has been significantly enhanced.In this study,the relevant improvement methods are reviewed,and the characteristics and experimental results of each method are described.This study introduces the common data sets in the field of relational extraction,and summarizes and com-pares the models with the best performance on each data set.The challenges in relation extraction are summarized and the solutions are proposed.关键词
关系抽取/深度学习/神经网络/信息抽取/知识图谱/卷积/Transformer/注意力Key words
relation extraction/deep learning/neural network/information extraction/knowledge graph/convolu-tion/Transformer/attention分类
信息技术与安全科学引用本文复制引用
沈依宁,王一然,吴聪..基于深度学习的关系抽取研究进展[J].电子科技,2025,38(7):40-49,10.基金项目
海军军医大学"三航"军事医学人才项目(2019-YH-09) （2019-YH-09）
基础加强计划技术领域基金(2019-JCJQ-JJ-066)Naval Medical Universit"Three Hangs"Military Medical Talent Project(2019-YH-09) （2019-JCJQ-JJ-066）
Basic Strengthening Plan Technology Field Fund Project(2019-JCJQ-JJ-066) （2019-JCJQ-JJ-066）
