Chinese and English text classification techniques incorporating CHI feature selection for ELT cloud classroom-Reference-Cited by-同舟云学术

Chinese and English text classification techniques incorporating CHI feature selection for ELT cloud classroom

Published:2024-01-01 Issue:1 Volume:14 Page:
ISSN:2299-1093
Container-title:Open Computer Science
language:en
Short-container-title:

Author:

Wei Yufen¹

Affiliation:

1. School of Foreign Languages, Weinan Normal University , Weinan , 714099 , China

Abstract

Abstract Although online cloud classroom teaching has been popular, the current English teaching cloud classroom has the defects of low information utilization and low information acquisition accuracy. To improve the information utilization and accuracy in teaching, a Chinese and English text classification algorithm is proposed. The algorithm is based on an improved Chi-squared test feature selection (CHI) algorithm. The performance of CHI is optimized by adding parameters such as word frequency, document coverage, and coefficient of variation to the CHI algorithm. According to the experimental results, the proposed algorithm achieved a recall of up to 1.0 under the Chinese dataset. Its accuracy rate was 0.49 higher than traditional CHI. Under the English dataset, the MO of the proposed algorithm reached 0.9. The results indicate that the proposed algorithm has reliable classification ability for both English and Chinese texts and has the potential to be applied to the English language teaching cloud classroom.

Publisher

Walter de Gruyter GmbH

Link

https://www.degruyter.com/document/doi/10.1515/comp-2024-0007/pdf

Reference21 articles.

1. P. Bhuvaneshwari and A. N. Rao, “A comparative study on various pre-processing techniques and deep learning algorithms for text classification,” Int. J. Cloud Comput., vol. 11, no. 1, pp. 61–78, 2022.

2. B. Parlak and A. K. Uysal, “A novel filter feature selection method for text classification: Extensive feature selector,” J. Inf. Sci., vol. 49, no. 1, pp. 59–78, 2023.

3. V. Arya and R. Agrawal, “Sentiment classification using feature selection techniques for text data composed of heterogeneous sources,” Recent. Adv. Comput. Sci. Commun., vol. 15, no. 2, pp. 207–214, 2022.

4. A. Alzamil, “L2 learning of English conditionals: Online versus traditional classroom teaching,” Sino-US Engl. Teach., vol. 19, no. 3, pp. 79–87, 2022.

5. Y. Chen, “Most common types of online English teaching during covid-19 pandemic in China -An introduction to Fanya and Chaoxiang platform,” Engl. Teach., vol. 18, no. 4, pp. 79–85, 2021.