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• W4385654345 abstract "Text classification, a fundamental task in natural language processing, has been extensively studied by researchers worldwide. The primary focus of text classification is on extracting effective features from text, as accurate information extraction is crucial for the task. However, the current utilization of text information in text classification is not optimal, and thus, effective extraction of text information remains an important research topic. Graph attention networks (GATs) have gained popularity among researchers due to their excellent performance in various tasks, including text classification. Additionally, previous graph neural network-derived models only address the differences in importance of edges within nodes during the information aggregation process, but ignore the importance differences between different nodes. But, the correlation between nodes also needs to be exploited for a more comprehensive understanding of the text. In this paper, we propose a novel multi-display graph attention network (MDGAT)-based model to address the challenges of inadequate text information capture and higher-order interactions between words. Our approach involves fusing multiple display graphs to capture diverse features of the text, for comprehensive text information representation. Additionally, we introduce a new information aggregation method called multi-step information aggregation, which considers the importance within nodes and the correlation between nodes, leading to improved text representation learning. We validate the performance of our proposed method through extensive experiments on various benchmark datasets. The results demonstrate the superior performance of our approach in text classification tasks." @default.
• W4385654345 created "2023-08-09" @default.
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• W4385654345 date "2023-01-01" @default.
• W4385654345 modified "2023-10-16" @default.
• W4385654345 title "Multi-display Graph Attention Network for Text Classification" @default.
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• W4385654345 doi "https://doi.org/10.1007/978-3-031-40289-0_7" @default.
• W4385654345 hasPublicationYear "2023" @default.
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