计算机应用与软件2025，Vol.42Issue(6)：342-349,355,9.DOI:10.3969/j.issn.1000-386x.2025.06.045
基于远程监督的关系抽取数据降噪模型
NOISE REDUCTION MODEL OF RELATION EXTRACTION DATA BASED ON DISTANT SUPERVISION
摘要
Abstract
Aimed at the problem of error labeling in distant supervision,a new relationship extraction model is proposed.The model was divided into two parts:label learner and relationship classifier.The tag learner corresponded the reinforcement learning action to the relationship tag,explored the real tag of the instance through the deep Q network,and the corrected tag and sentence form new data to reduce the impact of noise on the model.At the same time,K-choice strategy was proposed to alleviate the problem of reward sparsity and improve the performance of relationship extraction.In addition,in the training process,the accuracy of label prediction was improved by calculating the contribution value of words in relation classification and mining trigger words.Experiments show that the model can deal with noise well,and has a good effect on sentence level relationship classification.关键词
远程监督/关系抽取/强化学习Key words
Distant supervision/Relationship extraction/Reinforcement learning分类
信息技术与安全科学引用本文复制引用
马建红,李晓珑,陈亚萌..基于远程监督的关系抽取数据降噪模型[J].计算机应用与软件,2025,42(6):342-349,355,9.基金项目
科技部创新方法工作专项项目(2019IM020300). （2019IM020300）
