现代情报2024，Vol.44Issue(5)：95-106,12.DOI:10.3969/j.issn.1008-0821.2024.05.008
开放同行评审中自动评审分类方法研究
Research on Automatic Review Classification Methods in Open Peer Review
摘要
Abstract
[Purpose/Significance]Scientific papers play a crucial role in the transmission and exchange of knowledge within academia.The evaluation of scientific paper reviews serves as an indicator of the knowledge value contained in these papers.Efficient and accurate prediction of scientific paper review classifications can enable swift assessment of their worth,thereby expediting the dissemination process for valuable knowledge.[Method/Process]This study delved into an automat-ic review classification method within open peer review systems.By harnessing semantic information extracted from scientific papers and expert ratings obtained during open peer reviews,the study constructed text representations and classification models.Traditional machine-learning approaches and deep-learning techniques were employed to generate automatic review classification results.[Result/Conclusion]Experimental findings demonstrate that integrating semantic information with rating data led to more effective review classification models compared to relying solely on mean ratings for judgment purpo-ses.Among the various models tested,the quality review classification model based on SCIBERT with the input of rating+mean achieved the highest accuracy at 90.17%.The proposed automatic review classification method demonstrated usability and high accuracy,offering valuable assistance to journal editors in swiftly screening potential scientific papers and contrib-uting to intelligent advancements in the field of scientific paper reviewing.关键词
文本语义/开放同行评审/自动评审分类/深度学习Key words
text semantics/open peer review/automatic review classification/deep learning分类
社会科学引用本文复制引用
陈红玉,胡文俊,路永和..开放同行评审中自动评审分类方法研究[J].现代情报,2024,44(5):95-106,12.基金项目
广东省重点领域研发计划项目"基于大数据智能的多层次知识检索关键技术研究及应用"(项目编号:2021B0101420004). （项目编号:2021B0101420004）
