2025 02 v.57 24-30
基于多尺度特征提取的层次多标签文本分类方法
基金项目(Foundation):
国家重点研发计划(2021YFF0704100);
国家自然科学基金项目(62136002,62233018);
重庆市自然科学基金项目(cstc2022 ycjh-bgzxm0004)
邮箱(Email):
yuhong@cqupt.edu.cn;
DOI:
10.13705/j.issn.1671-6841.2023120
中文作者单位:
重庆邮电大学计算智能重庆市重点实验室;
摘要(Abstract):
针对现有的特征提取方法忽略文本局部和全局联系的问题，提出了基于多尺度特征提取的层次多标签文本分类方法。首先，设计了多尺度特征提取模块，对不同尺度特征进行捕捉，更好地表示文本语义。其次，将层次特征嵌入文本表示中，得到具有标签特征的文本语义表示。最后，在标签层次结构的指导下对输入文本构建正负样本，进行对比学习，提高分类效果。在WOS、RCV1-V2、NYT和AAPD数据集上进行对比实验，结果表明，所提模型在评价指标上表现出色，超过了其他主流模型。此外，针对层次分类提出层次Micro-F1和层次Macro-F1指标，并对模型效果进行了评价。
关键词(KeyWords):
层次多标签文本分类;多尺度特征提取;对比学习;层次Micro-F1;层次Macro-F1
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[3] ZHOU J E,MA C P,LONG D K,et al.Hierarchy-aware global model for hierarchical text classification[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Stroudsburg:Association for Computational Linguistics,2020:1106-1117.
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[6] ZHANG X Y,XU J H,SOH C,et al.LA-HCN:label-based attention for hierarchical multi-label text classification neural network[J].Expert systems with applications,2022,187:115922.
[7] WANG Z H,WANG P Y,HUANG L Z,et al.Incorporating hierarchy into text encoder:a contrastive learning approach for hierarchical text classification[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics.Stroudsburg:Association for Computational Linguistics,2022:7109-7119.
[8] 刘燕.基于BERT-BiGRU的中文专利文本自动分类[J].郑州大学学报(理学版),2023,55(2):33-40.LIU Y.Automatic classification method for Chinese patent texts based on BERT-BiGRU[J].Journal of Zhengzhou university (natural science edition),2023,55(2):33-40.
[9] 曾立英，许乾坤，张丽颖，等.面向主题检索的科技政策扩散识别方法[J].郑州大学学报(理学版),2022,54(5):82-89.ZENG L Y,XU Q K,ZHANG L Y,et al.Identification method for subject retrieval of science and technology policy diffusion[J].Journal of Zhengzhou university (natural science edition),2022,54(5):82-89.
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[12] LEWIS D,YANG Y M,ROSE T,et al.RCV1:a new benchmark collection for text categorization research[J].Journal of machine learning research,2004,5:361-397.
[13] YANG P,SUN X,LI W,et al.SGM:sequence generation model for multi-label classification[C]//Proceedings of the 27th International Conference on Computational Linguistics.Stroudsburg:Association for Computational Linguistics,2018:3915-3926.
[14] KIM Y.Convolutional neural networks for sentence classification[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing.Stroudsburg:Association for Computational Linguistics,2014:1746-1751.
[15] LIU X,WU J,YANG Y.Recurrent neural network for text classification with multi-task learning[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.Stroudsburg:Association for Computational Linguistics,2016:10-21.
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基本信息:
DOI：10.13705/j.issn.1671-6841.2023120
中图分类号:TP391.1;TP18
引用信息:
[1]武子轩,王烨,于洪.基于多尺度特征提取的层次多标签文本分类方法[J].郑州大学学报(理学版),2025,57(02):24-30.DOI:10.13705/j.issn.1671-6841.2023120.
基金信息:
国家重点研发计划(2021YFF0704100); 国家自然科学基金项目(62136002,62233018); 重庆市自然科学基金项目(cstc2022 ycjh-bgzxm0004)
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