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

<p>In this paper, based on the analysis of onomatopoeia in the Japanese language, based on the constructional and grammatical features of the Japanese language, combined with the teaching methods and characteristics of listening, reading and writing in the Japanese language, we utilize the dominant correlation degree method for the characterization of utterance change and its stylistic features. For semantic association, the local dominant association algorithm is employed. The logistic regression algorithm is used to train the two-layer classification model, and logistic regression, plain Bayes and support vector machine are chosen as the analysis and comparison algorithms to judge and analyze the semantics of utterances in Japanese language education. The two-layer classification model was examined to examine the impact of each index, and the word length of written and spoken language was scrutinized for learners and native speakers. The accuracy of each reached more than 99.8%, and the difference was controlled at 0.001. The size order of the word type coverage was Lo>Ns>Lw>Nx>Nr.</p>
</abstract>

In this paper, based on the analysis of onomatopoeia in the Japanese language, based on the constructional and grammatical features of the Japanese language, combined with the teaching methods and characteristics of listening, reading and writing in the Japanese language, we utilize the dominant correlation degree method for the characterization of utterance change and its stylistic features. For semantic association, the local dominant association algorithm is employed. The logistic regression algorithm is used to train the two-layer classification model, and logistic regression, plain Bayes and support vector machine are chosen as the analysis and comparison algorithms to judge and analyze the semantics of utterances in Japanese language education. The two-layer classification model was examined to examine the impact of each index, and the word length of written and spoken language was scrutinized for learners and native speakers. The accuracy of each reached more than 99.8%, and the difference was controlled at 0.001. The size order of the word type coverage was Lo>Ns>Lw>Nx>Nr.

Utterance variation and its stylistic features in Japanese language education using the dominant relevance approach

Applied Mathematics and Nonlinear Sciences

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

In this paper, based on the analysis of onomatopoeia in the Japanese language, based on the constructional and grammatical features of the Japanese language,...

Utterance variation and its stylistic features in Japanese language education using the dominant relevance approach

Pubblicato online: 08 nov 2023

Pagine: -

Ricevuto: 30 gen 2023

Accettato: 19 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.01024

Parole chiave
Dominant correlation method, Logistic regression algorithm, Plain Bayes, Support vector machine, Corpora word length, Japanese language

© 2023 Yan Zhang, published by Sciendo

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

Utterance variation and its stylistic features in Japanese language education using the dominant relevance approach

Pubblicato online: 08 nov 2023

Pagine: -

Ricevuto: 30 gen 2023

Accettato: 19 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.01024

Parole chiaveDominant correlation method, Logistic regression algorithm, Plain Bayes, Support vector machine, Corpora word length, Japanese language

© 2023 Yan Zhang, published by Sciendo

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

Parole chiave
Dominant correlation method, Logistic regression algorithm, Plain Bayes, Support vector machine, Corpora word length, Japanese language