摘要
多标签学习广泛应用于文本分类、标签推荐、主题标注等.最近,基于深度学习技术的多标签学习受到广泛关注,针对如何在多标签学习中有效挖掘并利用高阶标签关系的问题,提出一种基于图卷积网络探究标签高阶关系的模型TMLLGCN.该模型采用GCN的映射函数从数据驱动的标签表示中生成对象分类器挖掘标签高阶关系.首先,采用深度学习方法提取文本特征,然后以数据驱动方式获得基础标签关联表示矩阵,为更好地建模高阶关系及提高模型效果,在基础标签关联表示矩阵上考虑未标记标签集对已知标签集的影响进行标签补全,并以此相关性矩阵指导GCN中标签节点之间的信息传播,最后将提取的文本特征应用到学习高阶标签关系的图卷积网络分类器进行端到端训练,综合标签关联和特征信息作为最终的预测结果.在实际多标签数据集上的实验结果表明,提出的模型能够有效建模标签高阶关系且提升了多标签学习的效果.
Multi-label learning is widely used in text categorization,label recommendation and topic labeling.Recently,multi-label learning based on deep learning techniques has received widespread attention.In order to explore and exploit high-order label correlations in multi-label learning,a model TMLLGCN which is based on graph convolutional networks(GCN) is proposed.The GCN mapping function generates object classifiers from data-driven label representation to mine label high-order correlations.Firstly,text features are extracted based on deep learning method,the basic label correlation matrix is obtained by data-driven method.Secondly,the unmarked label set is used to perform label completion to improve the effect.After that,the new label correlation matrix is designed to guide information propagation between label nodes in GCN.Finally features extracted from the text are applied to the graph convolutional network classifier for end-to-end training,and the final prediction is made by integrating the label correlation and feature information.The experimental results on the multi-label dataset illustrate the effectiveness of the proposed model.
作者
刘晓玲
刘柏嵩
王洋洋
LIU Xiao-ling;LIU Bai-song;WANG Yang-yang(Institute of Information Science and Technology,Ningbo University,Ningbo 315211,China)
出处
《小型微型计算机系统》
CSCD
北大核心
2021年第3期531-535,共5页
Journal of Chinese Computer Systems
基金
国家社会科学基金项目(15FTQ002)资助
浙江省部级实验室/开放基金项目(B2014)资助。
关键词
多标签学习
标签关联
标签补全
深度学习
图卷积网络
multi-label learning
label correlations
label completion
deep learning
graph convolutional networks
