Zunlan Xiao , Xiaohong Ning , Mary Josephine M. Duritan
{"title":"BERT-SVM: A hybrid BERT and SVM method for semantic similarity matching evaluation of paired short texts in English teaching","authors":"Zunlan Xiao , Xiaohong Ning , Mary Josephine M. Duritan","doi":"10.1016/j.aej.2025.04.061","DOIUrl":null,"url":null,"abstract":"<div><div>In today's digital era, English teaching assessment is gradually transforming to intelligence, but there are many shortcomings in the existing assessment methods. Traditional assessment methods are not only time-consuming and laborious, but also have obvious limitations in the depth and precision of semantic understanding, making it difficult to accurately capture the semantic nuances in students' short-text responses. To address these issues, we propose an innovative hybrid model, BERT-SVM, which combines the deep semantic understanding of BERT and the efficient classification ability of SVM. At the same time, a multi-layer attention mechanism is introduced to enhance the understanding of complex relationships in text. Through the multi-layer attention mechanism at the word, phrase and sentence levels, the model is able to capture the semantic features of the text more accurately, thus realizing the efficient evaluation of the semantic similarity of short texts. The experimental results show that the BERT-SVM model achieves 90 % accuracy in the classification task, which is significantly better than the traditional methods, proving its effectiveness and reliability in the assessment of English teaching.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 231-246"},"PeriodicalIF":6.2000,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"alexandria engineering journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1110016825005575","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
In today's digital era, English teaching assessment is gradually transforming to intelligence, but there are many shortcomings in the existing assessment methods. Traditional assessment methods are not only time-consuming and laborious, but also have obvious limitations in the depth and precision of semantic understanding, making it difficult to accurately capture the semantic nuances in students' short-text responses. To address these issues, we propose an innovative hybrid model, BERT-SVM, which combines the deep semantic understanding of BERT and the efficient classification ability of SVM. At the same time, a multi-layer attention mechanism is introduced to enhance the understanding of complex relationships in text. Through the multi-layer attention mechanism at the word, phrase and sentence levels, the model is able to capture the semantic features of the text more accurately, thus realizing the efficient evaluation of the semantic similarity of short texts. The experimental results show that the BERT-SVM model achieves 90 % accuracy in the classification task, which is significantly better than the traditional methods, proving its effectiveness and reliability in the assessment of English teaching.
期刊介绍:
Alexandria Engineering Journal is an international journal devoted to publishing high quality papers in the field of engineering and applied science. Alexandria Engineering Journal is cited in the Engineering Information Services (EIS) and the Chemical Abstracts (CA). The papers published in Alexandria Engineering Journal are grouped into five sections, according to the following classification:
• Mechanical, Production, Marine and Textile Engineering
• Electrical Engineering, Computer Science and Nuclear Engineering
• Civil and Architecture Engineering
• Chemical Engineering and Applied Sciences
• Environmental Engineering