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

<p>With the popularization of big data technology, intelligent translation technology gradually rises and is applied in daily life, and this paper explores its utility in English writing. English writing can benefit from the auxiliary utility of Big Data intelligent translation The grammatical error correction model for intelligent English translation is constructed based on statistical classification and deep classification to optimize the English translation system. Through the performance testing of the intelligent translation system, the performance of the algorithm model of this paper is improved by 3.03, 2.64, and 8.55 compared with CYK, CYK-PU, and the shift-and-return algorithm, based on which the intelligent translation system is applied to English writing for the evaluation and analysis of the effect, and the reliability coefficients of the three scales are greater than 0.9 through the reliability and validity test and correlation analysis. Finally, a practical analysis of the auxiliary utility of intelligent translation in English writing is conducted. Finally, the auxiliary utility of intelligent translation in English writing is practically analyzed, and in the final achievement test, its various ability test scores and comprehensive achievement scores are all greater than 0.9. An intelligent translation system can help cultivate and improve English writing ability, which enhances the realistic reference significance of intelligent translation to aid English writing.</p>
</abstract>

With the popularization of big data technology, intelligent translation technology gradually rises and is applied in daily life, and this paper explores its utility in English writing. English writing can benefit from the auxiliary utility of Big Data intelligent translation The grammatical error correction model for intelligent English translation is constructed based on statistical classification and deep classification to optimize the English translation system. Through the performance testing of the intelligent translation system, the performance of the algorithm model of this paper is improved by 3.03, 2.64, and 8.55 compared with CYK, CYK-PU, and the shift-and-return algorithm, based on which the intelligent translation system is applied to English writing for the evaluation and analysis of the effect, and the reliability coefficients of the three scales are greater than 0.9 through the reliability and validity test and correlation analysis. Finally, a practical analysis of the auxiliary utility of intelligent translation in English writing is conducted. Finally, the auxiliary utility of intelligent translation in English writing is practically analyzed, and in the final achievement test, its various ability test scores and comprehensive achievement scores are all greater than 0.9. An intelligent translation system can help cultivate and improve English writing ability, which enhances the realistic reference significance of intelligent translation to aid English writing.

The Auxiliary Utility of Big Data Intelligent Translation in English Writing

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"The Auxiliary Utility of Big Data Intelligent Translation in English Writing"}

With the popularization of big data technology, intelligent translation technology gradually rises and is applied in daily life, and this paper explores its...

The Auxiliary Utility of Big Data Intelligent Translation in English Writing

Pubblicato online: 31 gen 2024

Pagine: -

Ricevuto: 20 dic 2023

Accettato: 28 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0220

Parole chiave
Phrase model, Extraction algorithm, Error correction model, Intelligent translation system, Utility analysis

© 2024 Lihua Zhang, published by Sciendo

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

The Auxiliary Utility of Big Data Intelligent Translation in English Writing

Pubblicato online: 31 gen 2024

Pagine: -

Ricevuto: 20 dic 2023

Accettato: 28 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0220

Parole chiavePhrase model, Extraction algorithm, Error correction model, Intelligent translation system, Utility analysis

© 2024 Lihua Zhang, published by Sciendo

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

Parole chiave
Phrase model, Extraction algorithm, Error correction model, Intelligent translation system, Utility analysis