Hybrid text classification model based on graph convolution network and neural network

Zhaohe Dong; Zhengli Zhai; Jitao Yang

doi:10.1117/12.2681612

1 June 2023 Hybrid text classification model based on graph convolution network and neural network

Zhaohe Dong, Zhengli Zhai, Jitao Yang

Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 127181O (2023) https://doi.org/10.1117/12.2681612
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China

Abstract

With the rapid development of graph neural network technology, its application in the field of natural language processing is more and more extensive, text classification is one of the important applications, everyday life will produce a large number of non-Euclidean text data, while the traditional classification methods in the graphic structure of text data has been a great challenge. Graph convolutional neural network(GCN) is considered to be able to model the structural attributes and node feature information of graphs well, and is gradually becoming a good choice for text classification of graph data. This paper proposes a text classification model based on graph convolution network and neural network local enhancement. On the basis of using GCN to extract features, Bi-LSTM method is used to balance the experimental results, enrich the feature information by capturing local information, integrate the attention mechanism, and fuse the evaluation values to improve the accuracy of classification. It is verified that this method has achieved better results than the existing classification methods in many classical data sets such as 20NG and OHSUMED.

Citation Download Citation

Zhaohe Dong, Zhengli Zhai, and Jitao Yang "Hybrid text classification model based on graph convolution network and neural network", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 127181O (1 June 2023); https://doi.org/10.1117/12.2681612

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