一种基于图卷积网络的文本情感分类方法
A text sentiment classification method based on graph convolution network
李波 1许云峰1
作者信息
- 1. 河北科技大学信息科学与工程学院,河北石家庄 050018
- 折叠
摘要
为了更好地分析单词和长距离依赖的作用,解释相关的句法约束,本文提出了一种基于多头注意力机制和图卷积网络模型MHGCN,在句子的依存关系树上建立一个图卷积网络,以利用句法信息和单词依存关系.利用多头注意力机制学习多个表示子空间的相关信息,并使用图卷积网络获得句法信息和长距离依赖.实验表明,MHGCN模型能有效完成情感分类任务,可为人机交互、医疗保健和社交媒体舆情监测等提供参考依据.
Abstract
In order to better analyze the role of words and long-distance dependencies,and explain relevant syntactic constraints,this paper proposes a graph convolutional network model MH-GCN based on multi head attention mechanism and graph convolutional network.A graph conv-olutional network is established on the dependency tree of a sentence to utilize syntactic infor-mation and single word dependencies.Utilize multi head attention mechanism to learn relevant information of multiple representation subspaces,and use graph convolutional networks to ob-tain syntactic information and long-distance dependencies.Experiments have shown that the MHGCN model can effectively complete sentiment classification tasks and provide reference ba-sis for human-computer interaction,healthcare,and social media public opinion monitoring.
关键词
自然语言处理/情感分类/图卷积网络/多头注意力机制/BiLSTMKey words
natural language processing/sentiment classification/graph convolution network/multiple attention mechanism/bilstm引用本文复制引用
基金项目
河北省重点研发计划项目资助项目(21373802D)
出版年
2024
NETL
NSTL
万方数据