计算机科学 ›› 2024, Vol. 51 ›› Issue (4): 353-358.doi: 10.11896/jsjkx.240300109
屠鑫1, 张伟2, 李继东1, 李美姣1, 龙相波1
TU Xin1, ZHANG Wei2, LI Jidong1, LI Meijiao1 , LONG Xiangbo1
摘要: 随着在线教学逐渐成为常态化的教学方式之一,人们对其提出了更高质量的教学需求。各种在线教学平台及互联网上海量的教育资源大大便利了众多学习者,但同时也存在着教育资源丰富但质量参差不齐、缺乏有效的分类整合以及主要依靠人工整理等问题,这就导致人们在获取在线教育资源时往往需要花费大量的时间和精力来进行检索、甄别和整理。针对在线教育资源现存的不足,文中提出了基于自然语言处理技术的在线教育资源自动分类方法,并以中学英语语法重点内容八大英语时态的习题自动分类为例,收集了线上及线下共9万余条时态类习题,通过数据清洗,最终选择3万余条语句构建数据集,并构建BERT微调文本分类模型,通过训练模型实现了对八大时态的自动分类,整体分类准确率达到86.15%,其中对一般现在时的识别准确率最高,达到93.88%。可以一定程度上满足中学英语时态类教育资源自动分类整理、习题智能批改及个性化推送、智能问答等现实需要,为提高在线教学质量,整合在线教育资源提供可行的思路和解决方案。
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