Mathematical plane geometry annotation and problem solving based on transformer-LSTM-BERT model
Zаimin Уапg
Teacher Education School, Nanchong Vocational and Technical College, Nangchong, China
Keywords: transformer, plane geometry problems, artificial intelligence, long short-term memory network, bidirectional encoder
Abstract
With the swift advancement of artificial intelligence technology, automatically solving plane geometry problems has emerged as a popular area of research. To improve the automatic solution effect of mathematical plane geometry problems, an efficient deterministic prediction model is constructed by combining the powerful feature extraction ability of transformer, the time series processing ability of long short-term memory network, and the generation ability based on the bidirectional encoder representation of transformer. The outcomes of the ablation experiment show that the model proposed by the research performs the best in regard to accuracy and score, with 92.5% and 0.91%, respectively. The inference time is 0.45 seconds, significantly better than other models. In addition, the accuracy of this model in text relationship extraction, geometric element detection, and graphic annоtation is 97.88%, 93.68%, and 95.36%, which is significantly better than other comparative models. The application effect analysis results show that the proposed model maintains a low average number of steps and the highest accuracy of 96.21% as the number of theorems increases. The model proposed by the research exhibits higher robustness and stability when dealing with problems with different step numbers, providing a new approach for automatically solving plane geometry problems.
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