[1]陈　燕,韦紫君,廖宇翔,等.基于RoBERTa和指针网络的中文实体与关系联合抽取方法[J].郑州大学学报(工学版),2026,47(02):41-50.[doi:10.13705/j.issn.1671-6833.2025.05.007]
　CHEN Yan,WEI Zijun,LIAO Yuxiang,et al.Joint Extraction Method of Chinese Entities and Relations Based on RoBERTa and Pointer Network[J].Journal of Zhengzhou University (Engineering Science),2026,47(02):41-50.[doi:10.13705/j.issn.1671-6833.2025.05.007]
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基于RoBERTa和指针网络的中文实体与关系联合抽取方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年02期
页码:
41-50
栏目:
出版日期:
2026-02-13

文章信息/Info

Title:
Joint Extraction Method of Chinese Entities and Relations Based on RoBERTa and Pointer Network
文章编号:
1671-6833(2026)02-0041-10
作者:
陈　燕1; 2;  韦紫君2;  廖宇翔2;  谭志湘2;  胡小春3; 4;  宋　玲2
1.广西壮族自治区信息中心 广西数字基础设施重点实验室,广西 南宁 530201;2.广西大学 计算机与电子信息学院,广西 南宁 530004;3.广西财经学院 广西财经大数据重点实验室,广西 南宁 530003;4.广西财经学院 大数据与人工智能学院,广西 南宁 530003
Author(s):
CHEN Yan1; 2;  WEI Zijun2;  LIAO Yuxiang2;  TAN Zhixiang2;  HU Xiaochun3; 4;  SONG Ling2
1. Guangxi Key Laboratory of Digital Infrastructure, Guangxi Zhuang Autonomous Region Information Center, Nanning 530201, China; 2. School of Computer and Electronic Information, Guangxi University, Nanning 530004, China; 3. Guangxi Key Laboratory of Finance and Economics Big Data, Guangxi University of Finance and Economics, Nanning 530003, China; 4. School of Big Data and Artificial Intelligence, Guangxi University of Finance and Economics, Nanning 530003, China
关键词:
实体与关系联合抽取;  RoBERTa;  指针网络;  自然语言处理;  深度学习
Keywords:
entity and relation joint extraction;  RoBERTa;  pointer network;  natural language processing;  deep learning
分类号:
TP391 TP312
DOI:
10.13705/j.issn.1671-6833.2025.05.007
文献标志码:
A
摘要:
为了有效解决非结构化文本中实体与关系联合抽取时的三元组重叠问题,提出了一种基于RoBERTa和指针网络的中文实体与关系联合抽取方法。首先,针对实体重叠问题,基于指针网络设计了实体识别模块,将实体识别任务构建为token-pair识别问题,通过识别实体的开始和结束位置来提取所有可能的实体;其次,针对三元组重叠问题,设计基于多头注意力机制和Ptr-Net的关系抽取模块,将三元组(s, r, o)抽取任务构建为五元组(sh, st, r, oh, ot)识别任务;最后,在中文信息抽取数据集DuIE上进行大量实验。实验结果表明:所提模型综合性能优于所有基线模型,其精确率、召回率和F1值分别为81.04%、85.82%和83.36%。
Abstract:
To effectively solve the problem of triple overlap in the joint extraction of entities and relations in unstructured text. A Chinese entity and relation joint extraction method was proposed based on RoBERTa and pointer network. Firstly, for the entity overlap problem, an entity recognition module was based on the pointer network, and the entity recognition task was constructed as a token-pair recognition problem, which extracted designed all possible entities by recognizing the start and end positions of the entities. Secondly, for the triplet overlap problem, a relation extraction module was designed based on the multi-head attention mechanism and Ptr-Net to construct the triple (s, r, o) extraction task as a quintuple (sh, st, r, oh, ot) identification problem. Finally, extensive experiments on the Chinese information extraction dataset DuIE showed that the comprehensive performance of the proposed model was better than all baseline models, with the precision, recall and F1 values of 81.04%, 85.82% and 83.36% respectively.

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更新日期/Last Update: 2026-03-04