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

<p>Vocabulary serves as the cornerstone of Japanese translation instruction, with the identification of high-frequency terms within educational texts yielding significant pedagogical insights. This study employs the KH-Coder software to extract feature words frequently used in Japanese translation teaching materials. Through a combination of covariate network analysis, sentiment analysis, and hierarchical clustering, the paper delves into the intrinsic textual value of these educational resources. Furthermore, it integrates both generative and discriminative approaches to conduct dependency parsing of Japanese translation. By incorporating probabilistic functions, the methodology enhances contextual understanding, ensuring that each annotation unit within a sentence is optimally tagged. Analysis of the CGTT-35 corpus through KH-Coder reveals a list of the top 50 high-frequency words in Japanese translation teaching, each appearing over 100 times. This enables students to use these frequent words and sentence structures as cues to master common phrases and familiarize themselves with core content. The approach has proven effective in enhancing students’ receptivity to Japanese translation instruction and their comprehension capabilities, offering valuable implications for teaching strategies.</p>
</abstract>

Vocabulary serves as the cornerstone of Japanese translation instruction, with the identification of high-frequency terms within educational texts yielding significant pedagogical insights. This study employs the KH-Coder software to extract feature words frequently used in Japanese translation teaching materials. Through a combination of covariate network analysis, sentiment analysis, and hierarchical clustering, the paper delves into the intrinsic textual value of these educational resources. Furthermore, it integrates both generative and discriminative approaches to conduct dependency parsing of Japanese translation. By incorporating probabilistic functions, the methodology enhances contextual understanding, ensuring that each annotation unit within a sentence is optimally tagged. Analysis of the CGTT-35 corpus through KH-Coder reveals a list of the top 50 high-frequency words in Japanese translation teaching, each appearing over 100 times. This enables students to use these frequent words and sentence structures as cues to master common phrases and familiarize themselves with core content. The approach has proven effective in enhancing students’ receptivity to Japanese translation instruction and their comprehension capabilities, offering valuable implications for teaching strategies.

Mining Text Value in Japanese Translation Teaching Based on KH Coder Text Data

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Mining Text Value in Japanese Translation Teaching Based on KH Coder Text Data"}

Vocabulary serves as the cornerstone of Japanese translation instruction, with the identification of high-frequency terms within educational texts yielding...

Mining Text Value in Japanese Translation Teaching Based on KH Coder Text Data

Publié en ligne: 07 juin 2024

Pages: -

Reçu: 26 févr. 2024

Accepté: 06 mai 2024

DOI: https://doi.org/10.2478/amns-2024-1422

Mots clés
Text mining, KH coder, High-frequency feature words, Dependency relationship, Probability function

© 2024 Xiaoke Zhang, published by Sciendo

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

Mining Text Value in Japanese Translation Teaching Based on KH Coder Text Data

Publié en ligne: 07 juin 2024

Pages: -

Reçu: 26 févr. 2024

Accepté: 06 mai 2024

DOI: https://doi.org/10.2478/amns-2024-1422

Mots clésText mining, KH coder, High-frequency feature words, Dependency relationship, Probability function

© 2024 Xiaoke Zhang, published by Sciendo

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

Mots clés
Text mining, KH coder, High-frequency feature words, Dependency relationship, Probability function