Affiliations: [a] School of Information Science and Engineering, Xinjiang University, Urumqi, China
| [b] College of Software, Xinjiang University, Urumqi Xinjiang, China
Correspondence:
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Corresponding author. Hui zhao, School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China. E-mail: [email protected].
Abstract: Traditional graph convolutional neural networks (GCN) utilizing linear feature combination methods have limited capacity to capture the interaction between complex features. While current research has extensively investigated various syntactic dependency tree structures, the optimization of GCN algorithms has often been overlooked, leading to suboptimal efficiency in practical applications. To address this issue, this paper proposes a cross-feature method that utilizes feature vector multiplication to construct non-linear combinations of GCN features and enhance the model’s capability to extract complex feature correlations. Experimental results demonstrate the superiority of the proposed method, with our models outperforming state-of-the-art methods and achieving significant improvements on three standard benchmark datasets. These results suggest that the cross-feature method can effectively extract potential connections between features, highlighting its potential for improving the performance of GCN-based models in real-world applications.
