<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>English translation is not a simple task, and it needs to be translated on the basis of different languages and cultures in order to get the ideal translation effect and promote cross-cultural communication between different languages. In this paper, with the help of web crawler technology to crawl the Chinese-English translation corpus data, and use the inverse document frequency and dynamic planning ideas to align the corpus data, the Chinese-English translation corpus is established. The LDA model is used to mine subject information in the Chinese-English translation corpus, and edge distribution estimation is used to construct the English translation model. For the application of the translation model and the Chinese-English translation corpus in improving the quality and effect of English translation, the word and sentence features, target word blocks, and the comparison of language features of human-computer translation from the Chinese-English corpus are verified in various aspects. The results show that the class/formant ratio of the source language in the corpus is between 56.73% and 75.84%, its average word length fluctuates in the range of [3.91,5.62], and the target word chunks of the English text are mainly based on nominal and verbal structures. The English translation model has a less significant discrepancy in other language features than human translation, except for the difference of 4.526 between sentence lengths. English translation needs to adopt a reasonable annotation method, semantic conversion, and technical tools to improve the quality of English translation.</p>
</abstract>

English translation is not a simple task, and it needs to be translated on the basis of different languages and cultures in order to get the ideal translation effect and promote cross-cultural communication between different languages. In this paper, with the help of web crawler technology to crawl the Chinese-English translation corpus data, and use the inverse document frequency and dynamic planning ideas to align the corpus data, the Chinese-English translation corpus is established. The LDA model is used to mine subject information in the Chinese-English translation corpus, and edge distribution estimation is used to construct the English translation model. For the application of the translation model and the Chinese-English translation corpus in improving the quality and effect of English translation, the word and sentence features, target word blocks, and the comparison of language features of human-computer translation from the Chinese-English corpus are verified in various aspects. The results show that the class/formant ratio of the source language in the corpus is between 56.73% and 75.84%, its average word length fluctuates in the range of [3.91,5.62], and the target word chunks of the English text are mainly based on nominal and verbal structures. The English translation model has a less significant discrepancy in other language features than human translation, except for the difference of 4.526 between sentence lengths. English translation needs to adopt a reasonable annotation method, semantic conversion, and technical tools to improve the quality of English translation.

Exploring English Translation Strategies Oriented by Big Data Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Exploring English Translation Strategies Oriented by Big Data Technology"}

English translation is not a simple task, and it needs to be translated on the basis of different languages and cultures in order to get the ideal translation...

Exploring English Translation Strategies Oriented by Big Data Technology

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 20. Dez. 2023

Akzeptiert: 25. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0189

Schlüsselwörter
Inverse document frequency, LDA model, Edge distribution estimation, Corpus, Dynamic programming, English translation

© 2024 Jinping Fan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploring English Translation Strategies Oriented by Big Data Technology

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 20. Dez. 2023

Akzeptiert: 25. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0189

SchlüsselwörterInverse document frequency, LDA model, Edge distribution estimation, Corpus, Dynamic programming, English translation

© 2024 Jinping Fan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Inverse document frequency, LDA model, Edge distribution estimation, Corpus, Dynamic programming, English translation