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

<p>In this paper, after clarifying the common types of errors in English translation teaching, in order to achieve the improvement of English translation teaching, combined with the GLR algorithm for English phrase recognition and analysis, PCFG English syntactic analysis model and HDSM lexicalization model are introduced for the optimization of GLR algorithm, which constitutes the PCFG-HDSM model. Analyze the process of English translation error recognition and correction with an improved GLR algorithm and realize the improvement of the GLR algorithm. Compare the performance of the English translation grammar analysis generator and determine the ratio of pure grammar analysis time to total analysis time. Examine the English translation accuracy of the improved GLR algorithm by combining the training corpus and evaluating it using LP and LR metrics. Analyze practical teaching applications by classifying the structure of English translation topics and using the improved GLR algorithm. The improved GLR algorithm is able to achieve more than 80% of English translation recognition and correction rates, which enhances students’ scores on English translation topics and promotes the development of English translation teaching.</p>
</abstract>

In this paper, after clarifying the common types of errors in English translation teaching, in order to achieve the improvement of English translation teaching, combined with the GLR algorithm for English phrase recognition and analysis, PCFG English syntactic analysis model and HDSM lexicalization model are introduced for the optimization of GLR algorithm, which constitutes the PCFG-HDSM model. Analyze the process of English translation error recognition and correction with an improved GLR algorithm and realize the improvement of the GLR algorithm. Compare the performance of the English translation grammar analysis generator and determine the ratio of pure grammar analysis time to total analysis time. Examine the English translation accuracy of the improved GLR algorithm by combining the training corpus and evaluating it using LP and LR metrics. Analyze practical teaching applications by classifying the structure of English translation topics and using the improved GLR algorithm. The improved GLR algorithm is able to achieve more than 80% of English translation recognition and correction rates, which enhances students’ scores on English translation topics and promotes the development of English translation teaching.

Identification and Correction of Improved GLR Algorithm in English Translation Teaching

Applied Mathematics and Nonlinear Sciences

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

In this paper, after clarifying the common types of errors in English translation teaching, in order to achieve the improvement of English translation...

Identification and Correction of Improved GLR Algorithm in English Translation Teaching

Online veröffentlicht: 10. Juni 2024

Seitenbereich: -

Eingereicht: 10. Feb. 2024

Akzeptiert: 21. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1471

Schlüsselwörter
GLR algorithm, PCFG model, HDSM model, English translation teaching

© 2024 Tao Li et al., published by Sciendo

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

Identification and Correction of Improved GLR Algorithm in English Translation Teaching

Online veröffentlicht: 10. Juni 2024

Seitenbereich: -

Eingereicht: 10. Feb. 2024

Akzeptiert: 21. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1471

SchlüsselwörterGLR algorithm, PCFG model, HDSM model, English translation teaching

© 2024 Tao Li et al., published by Sciendo

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

Schlüsselwörter
GLR algorithm, PCFG model, HDSM model, English translation teaching