首页 | 本学科首页   官方微博 | 高级检索  
     

结合图卷积的深层神经网络用于文本分类
引用本文:郑诚,陈杰,董春阳. 结合图卷积的深层神经网络用于文本分类[J]. 计算机工程与应用, 2022, 58(7): 206-212. DOI: 10.3778/j.issn.1002-8331.2010-0172
作者姓名:郑诚  陈杰  董春阳
作者单位:1.安徽大学 计算机科学与技术学院,合肥 2306012.计算智能与信号处理教育部重点实验室,合肥 230601
摘    要:随着图卷积网络的发展,图卷积网络已经应用到很多任务中,其中就包含文本分类任务.通过将文本数据表示成图数据,进而在图上应用图卷积,从而捕获文本的结构信息和单词间的长距离依赖关系获得了良好的分类效果.但将文本建模成图模型后,图卷积网络面临着文本上下文语义信息和局部特征信息表示不充分的问题.提出一种新的模型,利用双向长短时记...

关 键 词:文本分类  神经网络  图卷积网络(GCN)

Deep Neural Network Combined with Graph Convolution for Text Classification
ZHENG Cheng,CHEN Jie,DONG Chunyang. Deep Neural Network Combined with Graph Convolution for Text Classification[J]. Computer Engineering and Applications, 2022, 58(7): 206-212. DOI: 10.3778/j.issn.1002-8331.2010-0172
Authors:ZHENG Cheng  CHEN Jie  DONG Chunyang
Affiliation:1.School of Computer Science and Technology, Anhui University, Hefei 230601, China2.Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Hefei 230601, China
Abstract:With the development of graph convolutional network, graph convolutional network has been applied to many tasks, including text classification. By representing the text data as graph data, and then applying graph convolution on the graph, the structural information of the text and the long-distance dependence between words are captured, and good classification results are obtained. However, after the text is modeled as a graph model, the graph convolutional network faces the problem that the semantic information and local information of the text context are not fully expressed. A new model is proposed, which uses bi-directional long-short-term memory network(Bi_LSTM) and convolutional neural network(CNN) to extract the context semantic information and local feature information of text to enrich the text representation of graph convolutional network(GCN), thus making up for the deficiency of graph convolutional network. At the same time, the graph pooling layer is used to filter out important nodes to help convolutional neural network capture the deep local feature information of text, which makes the model better represent text information. The experimental results on three English datasets show that the model has better classification effect than the baseline model.
Keywords:text classification   neural network   graph convolutional network(GCN)  
本文献已被 万方数据 等数据库收录!
点击此处可从《计算机工程与应用》浏览原始摘要信息
点击此处可从《计算机工程与应用》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号