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作 者:裘江南[1] 徐雪冬 鲁艳霞 杨智龙 Qiu Jiangnan;Xu Xuedong;Lu Yanxia;Yang Zhilong(School of Economics and Management,Dalian University of Technology,Dalian 116024,China;School of Management,Liaoning Normal University,Dalian 116024,China)
机构地区:[1]大连理工大学经济管理学院,大连116024 [2]辽宁师范大学管理学院,大连116024
出 处:《数据分析与知识发现》2025年第2期106-119,共14页Data Analysis and Knowledge Discovery
基 金:国家重点研发计划课题(项目编号:2021YFC3300201)的研究成果之一。
摘 要:【目的】对公众诉求中反映的矛盾进行识别与多标签分类,探究不同地区不同矛盾类型与回应率差异。【方法】以养老保险纠纷为例,通过领域词库构建、关键诉求内容提取与简单数据增强两种方式对ERNIE模型进行知识与数据增强;构建ERNIE-BiLSTM矛盾识别分类模型,实现对低数据资源场景下的公众诉求中矛盾的深度挖掘,解决现有研究中缺少定量方法进行社会矛盾分析的问题。最后,基于识别分类结果对矛盾进行差异性分析。【结果】数据收集区间内,河南省与辽宁省的养老保险缴纳类矛盾较多,而广东省与北京市更容易发生养老保险服务类矛盾,不同矛盾类型的回应率具有较大差异。【局限】未考虑不同矛盾类型之间的相关性。【结论】研究揭示了养老保险纠纷矛盾的省际差异,可以帮助决策者把握矛盾热点与态势,辅助政府决策。[Objective]This paper identifies and classifies issues from public appeals.It also explores regional differences in issue types and response rates.[Methods]Taking pension insurance disputes as an example,the ERNIE model was enhanced with knowledge and data through domain-specific vocabulary construction,key appeal content extraction,and simple data augmentation.An ERNIE-BiLSTM contradiction identification and classification model was developed to deeply analyze contradictions in public appeals in low-data-resource scenarios,addressing existing studies'lack of quantitative methods for social contradiction analysis.Finally,a differentiation analysis of contradictions was conducted based on the classification results.[Results]During the data collection period,pension insurance payment-related conflicts were more frequent in Henan and Liaoning provinces,while pension insurance service-related conflicts were more prevalent in Guangdong Province and Beijing.Significant differences in response rates were observed across different types of contradictions.[Limitations]This paper does not consider the correlation between different types of conflicts.[Conclusions]This paper reveals the inter-provincial differences in pension insurance disputes,providing governments with insights into hotspots and trends to assist in decision-making.
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