• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Measuring Technology and Mechatronics Automation >Elimination of Machine Translation Errors in English Language Transformation
【24h】

Elimination of Machine Translation Errors in English Language Transformation

机译：消除英语翻译中的机器翻译错误

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

To improve the level of automation and intelligence of English language transformation in machine translation, a method of machine translation error elimination based on deep learning and feature extraction of language transformation error is proposed. The semantic correlation detection model of error exclusion in English language conversion translation is constructed by using the differentiated semantic modification method, and the semantic tree of error exclusion in English language transformation translation is built by means of grammar analysis. The semantic similarity feature of English language transformation is extracted. According to the different combinations of semantic similarity, the semantic allocation and machine translation error feature analysis in English language transformation are carried out. The tree topic word list of English language conversion is established by means of deep learning method, and the sentence structure of English language transformation is adjusted according to the semantic modification target in the tree topic word list. In order to eliminate the errors in translation of English language conversion and the registration of topic words, the optimal semantic correlation feature of each clause is calculated, and the deep learning algorithm is used to automatically optimize the errors in translation of English language conversion. The simulation results show that the accuracy of the proposed approach is high and the relevance of translation calibration is strong.
机译：为了提高机器翻译中英语翻译的自动化和智能水平，提出了一种基于深度学习的语言翻译错误特征提取和机器翻译错误消除方法。利用差异化语义修饰方法构建了英语转换翻译中错误排除的语义相关检测模型，并通过语法分析构建了英语转换翻译中错误排除的语义树。提取了英语转换中的语义相似度特征。根据语义相似度的不同组合，对英语转换中的语义分配和机器翻译错误特征进行了分析。通过深度学习的方法建立了英语转换的树主题词表，并根据树主题词表中的语义修饰目标对英语转换的句结构进行了调整。为了消除英语转换中的翻译错误和主题词的注册错误，计算每个子句的最佳语义相关性，并使用深度学习算法自动优化英语转换中的错误。仿真结果表明，该方法准确度高，翻译标定的相关性强。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号