面向投稿选刊的学术论文多标签分类研究OACHSSCDCSTPCD
Research on Multi-label Classification of Academic Papers for Periodical Selection
[目的/意义]学术论文投稿中面临期刊选择多样性和拒稿重投问题,研究利用深度学习和多标签分类技术,基于论文题录信息给出多标签的投稿选刊建议.[方法/过程]选取情报学领域 8 种CSSCI期刊近 20年的论文作为样本,采用TextCNN、TextRNN等深度学习模型和预训练语言模型BERT构建多标签分类方法进行实验,并对比不同特征组合和多标签设置策略下的实验效果.[结果/结论]多标签分类能够反映学术论文对不同期刊的适合度,预训练语言模型BERT表现最佳,F1 达到 68.99%.
[Purpose/Significance]The academic paper submission is faced with the problems of journal selection di-versity and re-submission,this paper studies the use of machine learning technology to give multi-label recommendations for periodical submission based on the content of the academic paper.[Method/Process]Papers from 8 CSSCI journals in the field of information science in recent 20 years were selected as samples,TextCNN,TextRNN,and pre-trained lan-guage model BERT were used for experiments,and the experimental effects under different feature combinations and multi-label setting strategies were compared.[Result/Conclusion]Multi-label classification can reflect the suitability of articles for different periodical,and the pre-trained language model BERT performs best,with F1 reaching 68.99%.
江天明;郑国杰;王晴;曹高辉
华中师范大学信息管理学院, 湖北 武汉 430079
投稿选刊多标签分类深度学习自然语言处理
periodical selectionmulti-label classificationdeep learningnatural language learning
《现代情报》 2024 (001)
48-56,108 / 10
中国博士后科学基金面上项目"基于深度语义挖掘的引文推荐可解释性研究"(项目编号:2021M701367);中央高校基本科研业务费项目"基于机器学习的引文推荐可解释性研究"(项目编号:CCNU21XJ020)、"开源跨模态科技情报知识组织与智能分析"(项目编号:CCNU22QN016).
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