摘要
在现有文本图基础上引入隐狄利克雷分布,将文档-主题和主题-词信息融入文本图以丰富文本图中节点间关系,之后将该文本图送入一个基于图卷积网络门控机制模型.在多个数据集上进行验证.结果表明,所提出的模型优于现有图卷积网络文本分类模型.
At present,graph convolutional network based text classification model uses word co-occurrence and word frequency-inverse document frequency(TF-IDF)information for graph construction.In this paper,we introduce the Latent Dirichlet Allocation(LDA)to construct text graph based on the existing text graph,and integrate the document-topic and topic-word information into the text graph,thereby enriching the relationship between the nodes in the text graph,and then feed the text graph into a gating mechanism model based on graph convolutional network(G-GCN).Verification on multiple datasets show that the model proposed in this paper is superior to existing text classification models based on graph convolutional networks.
作者
高维奇
黄浩
胡英
吾守尔·斯拉木
GAO Wei-qi;HUANG Hao;HU Ying;WUSHOUR Silamu(School of Information Science and Engineering,Xinjiang University,Urumqi 830046,China;Multi-language Information Technology Laboratory of Xinjiang,Urumqi 830046,China;Multi-language Information Technology Research Center of Xinjiang,Urumqi 830046,China)
出处
《东北师大学报(自然科学版)》
CAS
北大核心
2021年第4期68-76,共9页
Journal of Northeast Normal University(Natural Science Edition)
基金
国家重点研发计划项目(2017YFB1402101)
国家自然科学基金资助项目(61663044,61761041)
新疆重点科技项目(2016A03007-1)
新疆高等教育创新项目(XJEDU2017T002).