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Automatic error calibration system for English semantic translation based on machine learning

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  • Zhenhua Wei

Abstract

The traditional English semantic translation error calibration system can not determine the optimal translation solution, which has the problems of high CPU utilisation, low translation accuracy and high calibration time-consuming. Before English semantic translation, English semantic features are decomposed to realise fuzzy mapping selection of English semantic translation. Then, English semantic translation decision function is obtained by constructing semantic ontology model, while English semantic translation error automatic calibration algorithm is realised by machine learning algorithm. Finally, the overall architecture and network topology of the system is designed, and the design of automatic proofreading system of English semantic translation errors is completed. The experimental results show that the running time of the proposed system is 1.5 s, the CPU occupancy rate of the designed system is only 0.9%, and the calibration accuracy is as high as 99%.

Suggested Citation

  • Zhenhua Wei, 2023. "Automatic error calibration system for English semantic translation based on machine learning," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 43(3), pages 301-316.
    • Handle: RePEc:ids:ijisen:v:43:y:2023:i:3:p:301-316
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