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

<p>Colleges and universities actively carry out Japanese language teaching courses to promote the friendly cooperation and communication between China and Japan. However, improving students’ Japanese intercultural communication ability still faces specific difficulties and practical obstacles due to cultural differences. In this paper, firstly, a Japanese language teaching corpus is collected based on Python technology to establish a Japanese language teaching corpus, based on which a Japanese language flipped classroom teaching model is constructed in colleges and universities. Secondly, to understand the semantic distribution of Japanese vocabulary in the Japanese teaching corpus, the Hidden Markov Model is used to semantically segment Japanese language, and the attention mechanism based on categories is used to extract and classify the textual features of the Japanese corpus. Finally, the model’s teaching effect and sentence features are analyzed using University of N as an example. The results show that after the teaching experiment, the average score of Japanese application proficiency of the experimental class is 86.57, which is 14.495% higher than that of the control class. 22.52% of the sentence length frequency ratios in spoken corpora were distributed in the [10,20] range, and the highest values of sentence length frequency ratios in written corpora, academic corpora, and policy corpora fell in the range of 20-30. Teaching Japanese language in higher education needs to target the lexical meanings of different sentence lengths to help students improve their Japanese language application skills.</p>
</abstract>

Colleges and universities actively carry out Japanese language teaching courses to promote the friendly cooperation and communication between China and Japan. However, improving students’ Japanese intercultural communication ability still faces specific difficulties and practical obstacles due to cultural differences. In this paper, firstly, a Japanese language teaching corpus is collected based on Python technology to establish a Japanese language teaching corpus, based on which a Japanese language flipped classroom teaching model is constructed in colleges and universities. Secondly, to understand the semantic distribution of Japanese vocabulary in the Japanese teaching corpus, the Hidden Markov Model is used to semantically segment Japanese language, and the attention mechanism based on categories is used to extract and classify the textual features of the Japanese corpus. Finally, the model’s teaching effect and sentence features are analyzed using University of N as an example. The results show that after the teaching experiment, the average score of Japanese application proficiency of the experimental class is 86.57, which is 14.495% higher than that of the control class. 22.52% of the sentence length frequency ratios in spoken corpora were distributed in the [10,20] range, and the highest values of sentence length frequency ratios in written corpora, academic corpora, and policy corpora fell in the range of 20-30. Teaching Japanese language in higher education needs to target the lexical meanings of different sentence lengths to help students improve their Japanese language application skills.

A Study of Differences in Sentential Characteristics of Japanese Language Teaching in Colleges and Universities Based on Semantic Contrastive Analysis

Applied Mathematics and Nonlinear Sciences

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

Colleges and universities actively carry out Japanese language teaching courses to promote the friendly cooperation and communication between China and Japan....

A Study of Differences in Sentential Characteristics of Japanese Language Teaching in Colleges and Universities Based on Semantic Contrastive Analysis

Publicado en línea: 26 feb 2024

Páginas: -

Recibido: 04 ene 2024

Aceptado: 13 ene 2024

DOI: https://doi.org/10.2478/amns-2024-0373

Palabras clave
Sentence features, Hidden Markov Model, Semantic segmentation, Attention mechanism, Japanese language teaching corpus

© 2024 Huiqin Tang, published by Sciendo

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

A Study of Differences in Sentential Characteristics of Japanese Language Teaching in Colleges and Universities Based on Semantic Contrastive Analysis

Publicado en línea: 26 feb 2024

Páginas: -

Recibido: 04 ene 2024

Aceptado: 13 ene 2024

DOI: https://doi.org/10.2478/amns-2024-0373

Palabras claveSentence features, Hidden Markov Model, Semantic segmentation, Attention mechanism, Japanese language teaching corpus

© 2024 Huiqin Tang, published by Sciendo

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

Palabras clave
Sentence features, Hidden Markov Model, Semantic segmentation, Attention mechanism, Japanese language teaching corpus