结合图卷积的深层神经网络用于文本分类

doi:10.3778/j.issn.1002-8331.2010-0172

摘要/Abstract

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，利用双向长短时记忆网络（Bi_LSTM）和卷积神经网络（CNN）混合提取文本的上下文语义信息和局部特征信息去丰富图卷积网络（GCN）的文本表示，从而弥补图卷积网络的不足，同时使用图池化层筛选出重要节点帮助卷积神经网络捕获文本深层局部特征信息，使得模型能更好的表示文本信息。通过在3个英文数据集上的实验结果表明，该模型相比于基线模型有较好的分类效果。

关键词: 文本分类, 神经网络, 图卷积网络（GCN）

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.

Key words: text classification, neural network, graph convolutional network（GCN）

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.

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.

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