VGCN: An Enhanced Graph Convolutional Network Model for Text Classification
DOI:
https://doi.org/10.5281/zenodo.13118430ARK:
https://n2t.net/ark:/40704/JIEAS.v2n4a16Keywords:
Graph Convolutional Neural Networks, Text Classification, Variational StructureAbstract
The rapid growth of textual data has made text classification a critical research area in the field of Natural Language Processing. Traditional Graph Convolutional Networks (GCN) face challenges in text classification due to the limitations of fixed edge weights and the inability to explore high-dimensional graph structural features, which hampers effective semantic information capture. To address these issues, we propose an improved graph convolutional network model, Variational Graph Convolutional Network (VGCN). In VGCN, we design a variational structure within the GCN to learn latent representations of textual data, forming a continuous, high-dimensional graph structure distribution. This feature representation captures complex graph structural features, thereby enhancing semantic information extraction and improving classification performance. Experimental results on datasets such as THUCNews and Toutiao demonstrate that VGCN significantly outperforms traditional models like CNN, RNN, and GCN in text classification tasks. These findings underscore the effectiveness and superiority of VGCN in capturing semantic information in texts.
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