Semantic analysis and construction of English discourse based on neural network

• Published in: Applied mathematics and nonlinear sciences 2024-01, Vol.9 (1)
• Main Author: Liu, Junjie
• Format: Article
• Language: English
• Subjects: 68M12; Bi-LSTM+Attention model; Neural network; RNN model; Semantic analysis
• ISSN: 2444-8656; 2444-8656
• DOI: 10.2478/amns-2024-2546

Accurate recognition and analysis of English semantics is of great significance in the application field of computer and artificial intelligence technology. Traditional text analysis technology has poor real-time analysis accuracy and efficiency. This paper constructs an English semantic analysis model based on neural networks. By designing an English semantic framework that integrates LSTM and RNN and optimizing it on the basis of LSTM and RNN models, an English semantic analysis model with improved attention mechanisms and Bi-LSTM is constructed. By comparing the Bi-LSTM+Attention model proposed in this paper with other semantic analysis models, the classification accuracy of the model constructed in this paper reaches 98%. Through the practical application of the model to analyze the semantic coherence of English, the average score of the semantic coherence quality measured by the model is 15.76, and the average score of the manual judgment is 16.03, which indicates that the application of the semantic analysis model constructed in this paper is close to the effect of the manual judgment.