英语文本语义特征分析和翻译的智能算法

Q3 Decision Sciences
Sha Chen
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引用次数: 0

摘要

准确、快速的翻译有利于不同语言的文化交流。本文简要介绍了长短时记忆(LSTM)算法。为了提高 LSTM 算法的性能,本文引入了语义特征,并利用语义相似性筛选出更符合源文本语义的译文。然后,进行了模拟实验。实验首先考察了 LSTM 的隐层节点数量和激活函数类型对翻译性能的影响。然后,将 LSTM 算法与递归神经网络(RNN)和传统 LSTM 算法进行了比较。当隐层节点数为 512 个且激活函数为 sigmoid 时,所提出的翻译算法表现最佳,其性能优于其他两种翻译算法,且得到的结果与源文本的语义一致、流畅。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Intelligent Algorithm for Semantic Feature Analysis and Translation of English Texts
Accurate and rapid translation is conducive to the cultural communication of different languages. This paper briefly introduces the long short-term memory (LSTM) algorithm. To enhance the performance of the LSTM algorithm, semantic features were introduced, and semantic similarity was used to screen the translations that are more in line with the semantics of the source text. Then, simulation experiments were conducted. The experiments first examined the effects of the quantity of hidden layer nodes and the type of activation function in LSTM on the translation performance. Then, the LSTM algorithm was compared with the recurrent neural network (RNN) and traditional LSTM algorithms. The proposed translation algorithm showed the best performance when there were 512 hidden layer nodes and the activation function was sigmoid, it performed better than the other two translation algorithms, and the obtained result was consistent with the semantic meaning of the source text and smooth.
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来源期刊
Journal of ICT Standardization
Journal of ICT Standardization Computer Science-Information Systems
CiteScore
2.20
自引率
0.00%
发文量
18
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