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This study addresses the rapid development of artificial intelligence in improving machine translation, focusing on translation talent training. Future translation teaching requires evaluating traditional skills and emphasizing machine translation review ability. The study introduces an intelligent analysis-supported English translation teaching system, detecting errors through the lens of semantic attributes, neural network algorithms, synonymic assemblies, and an augmented proficiency in spell-check mechanisms, errors are discerned and rectified within the system. The system performs a comprehensive error analysis, establishing association rules and clustering students to summarize typical errors. It constructs an article community, using collaborative filtering to recommend assignments based on student groups.

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Intelligent Translation Recognition and Error Correction System Based on Machine Intelligence and Data Mining Technology

  • 会议论文

  • 发表日期：

    2025年

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  • Hong Liu

摘要

This study addresses the rapid development of artificial intelligence in improving machine translation, focusing on translation talent training. Future translation teaching requires evaluating traditional skills and emphasizing machine translation review ability. The study introduces an intelligent analysis-supported English translation teaching system, detecting errors through the lens of semantic attributes, neural network algorithms, synonymic assemblies, and an augmented proficiency in spell-check mechanisms, errors are discerned and rectified within the system. The system performs a comprehensive error analysis, establishing association rules and clustering students to summarize typical errors. It constructs an article community, using collaborative filtering to recommend assignments based on student groups.