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基于神经网络和模糊决策树的英语翻译文本的错误识别

Danqiangyu Zhou¹

¹School of Foreign Languages, Civil Aviation Flight University of China, Guang Han, People's Republic of China.

|August 29, 2025

概括

这项研究引入了改进的英语翻译错误识别决策树算法,显著提高了准确性和回忆率. 该方法通过可靠和高性能错误检测提高了机器翻译的质量.

科学领域:

自然语言处理
机器翻译
人工智能

背景情况:

目前的英语翻译错误识别方法的准确性和回忆力较低.
需要更强大,更可靠的方法来识别翻译英语文本的错误.

研究的目的:

提出和评估一个改进的决策树算法,用于识别英语翻译文本错误.
提高机器翻译错误检测的准确性和回忆力.

主要方法:

使用相互信息进行输入相关性计算和部分语音注释.
在机器翻译中使用编码器解码器神经网络来提取特征.
使用吉尼指数进行错误特征分类的模糊决策树.

主要成果:

拟议的方法的准确率超过94%,回忆率超过86%.
改进的决策树算法在大型数据集 (15,000-50,000句) 中表现一致,准确度超过93%.
这种方法在错误识别方面表现优于其他先进的算法.

结论:

对于英语翻译错误识别的增强方法非常准确和可靠.
这种方法有效地提高了翻译质量,并显示出机器翻译的实际应用潜力.

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