基于注意力机制和CNN的多标签文本分类模型  

MULTI-LABEL TEXT CLASSIFICATION MODEL BASED ON ATTENTION MECHANISM AND CNN

作　　者：杨春霞[1,2] 吴佳君 瞿涛 姚思诚 Yang Chunxia;Wu Jiajun;Qu Tao;Yao Sicheng(Nanjing University of Information Science&Technology,Nanjing 210044,Jiangsu,China;Jiangsu Key Laboratory of Big Data Analysis Technology,Nanjing 210044,Jiangsu,China)

机构地区：[1]南京信息工程大学,江苏南京210044 [2]江苏省大数据分析技术重点实验室,江苏南京210044

出　　处：《计算机应用与软件》2024年第3期156-162,共7页Computer Applications and Software

基　　金：国家自然科学基金项目(61273229);江苏省青蓝工程项目。

摘　　要：针对目前多标签文本分类模型存在无法充分提取文本语义与标签的相互关系,提出一种基于注意力机制和卷积神经网络(CNN)的多标签文本分类模型。通过多头注意力机制和CNN对文本进行建模表示,充分挖掘文本全局和局部的语义特征;结合标签与文本信息进行交互注意力计算,捕捉结合文本内容后标签间的相互关系;使用一种自适应融合策略进一步提取两者语义信息。实验结果表明,该模型相比于其他主流模型能有效提升多标签文本分类效果。To address the problem of being unable to fully extract the relationship between text semantics and label in current multi-label text classification,a multi-label text classification model based on attention mechanism and convolutional neural network is proposed.The multi attention mechanism and CNN were used to represent the text,and the global and local semantic features of the text were fully mined.It combined tags and text information to calculate the interactive attention,and captured the relationship between tags after combining the text content.It used an adaptive fusion strategy to further extract the semantic information of the two.Experimental results show that this model can effectively improve the effect of multi label text classification compared with other mainstream models.

关 键 词：多标签文本分类 注意力机制 卷积神经网络 文本表示 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]