Recognition of Chinese sentence intention combining multi-channel attention convolution and graph neural network

Key Points

• The aim is to enhance sentence intention recognition in Chinese through innovative algorithmic methods.
• Utilized multi-channel attention mechanisms to capture contextual features of sentences.
• Employed convolutional neural networks for feature extraction.
• Integrated graph neural networks to model relationships between words in sentences.
• Achieved improved accuracy in recognizing sentence intentions compared to traditional methods.
• Demonstrated effective integration of attention and graph-based techniques in deep learning applications.

Abstract