电子科技大学学报2024，Vol.53Issue(2)：243-251,9.DOI:10.12178/1001-0548.2023107
融合强化学习的实体关系联合抽取模型
Entity-Relationship Joint Extraction Model Infused with Reinforcement Learning
摘要
Abstract
Existing joint extraction tasks of entities and relationships introduce distant supervision strategies to automatically generate large-scale training data,leading to severe problems of noisy data during data processing.To address the issue of noisy data,this paper proposes an entity relation joint extraction model with reinforcement learning integration.The model consists of two components:reinforcement learning and joint extraction model.The joint extraction model is composed of a graph convolutional network and a multi-head self-attention mechanism.Firstly,reinforcement learning is utilized to eliminate noisy sentences from the original dataset,and the denoised high-quality sentences are input into the joint extraction model.Secondly,the joint extraction model is employed to predict and extract entities and relationships from the input sentences,and provide feedback rewards to the reinforcement learning component to guide it in selecting high-quality sentences.Finally,the reinforcement learning and joint extraction models are jointly trained and iteratively optimized.The experiments demonstrating that the proposed model can effectively address the issue of data noise and outperform baseline methods in entity relationship extraction.关键词
实体关系联合抽取/噪声数据/强化学习/多头自注意力机制/图卷积网络Key words
joint extraction of entities and relationships/noisy data/reinforcement learning/multi-head self-attention mechanism/graph convolutional network分类
信息技术与安全科学引用本文复制引用
翟社平,李航,亢鑫年,杨锐..融合强化学习的实体关系联合抽取模型[J].电子科技大学学报,2024,53(2):243-251,9.基金项目
国家自然科学基金(61373116) （61373116）
工业和信息化部通信软科学项目(2018-R-26) （2018-R-26）
陕西省教育厅科学研究计划(18JK0697) （18JK0697）
陕西省重点研发计划(2022GY-038) （2022GY-038）
西安邮电大学研究生创新基金(CXJJYL2021045) （CXJJYL2021045）
陕西省大学生创新创业训练计划(202211664053) （202211664053）
陕西省大学生创新创业训练计划(202211664086) （202211664086）
