BERT-SVM: A hybrid BERT and SVM method for semantic similarity matching evaluation of paired short texts in English teaching

IF 6.2 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Zunlan Xiao , Xiaohong Ning , Mary Josephine M. Duritan
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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.
BERT-SVM:一种基于BERT和SVM的英语教学短文本语义相似度评价混合方法
在数字化时代的今天,英语教学评估正逐步向智能化转变,但现有的评估方法存在诸多不足。传统的评价方法不仅耗时费力,而且在语义理解的深度和精度上存在明显的局限性,难以准确捕捉学生短文本回答中的语义细微差别。为了解决这些问题,我们提出了一种创新的混合模型BERT-SVM,它结合了BERT的深度语义理解和SVM的高效分类能力。同时,引入多层注意机制,增强对文本复杂关系的理解。该模型通过单词、短语和句子层面的多层关注机制,能够更准确地捕捉文本的语义特征,从而实现对短文本语义相似度的高效评价。实验结果表明,BERT-SVM模型在分类任务中准确率达到90 %,明显优于传统方法,证明了其在英语教学评价中的有效性和可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
alexandria engineering journal
alexandria engineering journal Engineering-General Engineering
CiteScore
11.20
自引率
4.40%
发文量
1015
审稿时长
43 days
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
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