武子轩,王烨,于洪

• 1: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

Abstract:

Keywords:

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)

作者(Author): 武子轩,王烨,于洪

DOI: 10.13705/j.issn.1671-6841.2023120

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