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

<p>Vocabulary acquisition is pivotal in enhancing English writing proficiency. Effective integration of output vocabulary into written English is essential for improving students’ compositional skills. This study proposes a methodology for extracting vocabulary from English textual materials and subsequently applying it to student writing endeavors. To ensure the integrity and accuracy of the text materials utilized, this research employs a Long Short-Term Memory (LSTM) algorithm to perform a comprehensive spelling check on the English writing corpus prior to vocabulary extraction. Further, this paper adopts the high-frequency word list and Term Frequency-Inverse Document Frequency (TF-IDF) techniques to identify and evaluate the significance of vocabulary within the texts. Key vocabulary that significantly impacts word importance classification is preliminarily identified using the Graph Convolutional Network-K Nearest Neighbor (GCKN) algorithm. These pivotal words, termed ‘key nodes, ’ form the basis for constructing a network within the English texts. Utilizing the message-passing mechanism, information from associated nodes is aggregated at the central node, facilitating the acquisition of output vocabulary. The study findings indicate that students, after learning and applying the acquired vocabulary, demonstrate considerable improvements in their English writing capabilities. They exhibit a broader and more sophisticated use of vocabulary, leading to marked enhancements in their writing performance and overall English proficiency.</p>
</abstract>

Vocabulary acquisition is pivotal in enhancing English writing proficiency. Effective integration of output vocabulary into written English is essential for improving students’ compositional skills. This study proposes a methodology for extracting vocabulary from English textual materials and subsequently applying it to student writing endeavors. To ensure the integrity and accuracy of the text materials utilized, this research employs a Long Short-Term Memory (LSTM) algorithm to perform a comprehensive spelling check on the English writing corpus prior to vocabulary extraction. Further, this paper adopts the high-frequency word list and Term Frequency-Inverse Document Frequency (TF-IDF) techniques to identify and evaluate the significance of vocabulary within the texts. Key vocabulary that significantly impacts word importance classification is preliminarily identified using the Graph Convolutional Network-K Nearest Neighbor (GCKN) algorithm. These pivotal words, termed ‘key nodes, ’ form the basis for constructing a network within the English texts. Utilizing the message-passing mechanism, information from associated nodes is aggregated at the central node, facilitating the acquisition of output vocabulary. The study findings indicate that students, after learning and applying the acquired vocabulary, demonstrate considerable improvements in their English writing capabilities. They exhibit a broader and more sophisticated use of vocabulary, leading to marked enhancements in their writing performance and overall English proficiency.

A Study of Output Vocabulary Knowledge in the English Writing Process

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"A Study of Output Vocabulary Knowledge in the English Writing Process"}

Vocabulary acquisition is pivotal in enhancing English writing proficiency. Effective integration of output vocabulary into written English is essential for...

A Study of Output Vocabulary Knowledge in the English Writing Process

Data publikacji: 09 lip 2024

Zakres stron: -

Otrzymano: 21 mar 2024

Przyjęty: 04 cze 2024

DOI: https://doi.org/10.2478/amns-2024-1802

Słowa kluczowe
Output vocabulary, LSTM, TF-IDF, Spell checking

© 2024 Yan Ning., published by Sciendo

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

A Study of Output Vocabulary Knowledge in the English Writing Process

Data publikacji: 09 lip 2024

Zakres stron: -

Otrzymano: 21 mar 2024

Przyjęty: 04 cze 2024

DOI: https://doi.org/10.2478/amns-2024-1802

Słowa kluczoweOutput vocabulary, LSTM, TF-IDF, Spell checking

© 2024 Yan Ning., published by Sciendo

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

Słowa kluczowe
Output vocabulary, LSTM, TF-IDF, Spell checking