英语作文的评分方法,在高等职业学院整合深度学习
Shuo Feng1, Lixia Yu1, Fen Liu2
1Department of Culture, Sports and Labor, Gannan Healthcare Vocational College, Ganzhou, 341000, China.
Scientific reports
|March 28, 2024
概括
智能英语作文评分使用新的主题决策 (TD) 模型和深度学习来提高主题相关性和评分. 这种方法提高了准确性,回忆力和F1分数,提供了更可靠的自动化作文评估.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 教育技术的教育技术
背景情况:
- 传统的英语作文评分面临主观性,反缓慢和漫长评分时间的挑战.
- 现有的自动分级方法在主题相关性特征提取方面存在局限性.
- 在NLP和深度学习的进步为自动化作文评分提供了潜在的解决方案.
研究的目的:
- 为了解决传统和当前的自动英语作文评分方法的局限性.
- 提出一个主题决策 (TD) 模型来计算主题相关性和丰富度得分.
- 开发一种智能英语作文评分方法,整合人工特征提取和深度学习.
主要方法:
- 开发一个主题决策 (TD) 模型来评估主题的相关性和丰富性.
- 基于主题丰富度 (TRSR) 的相关性得分计算方法的实施.
- 设计一种智能英语作文评分方法,将传统特征提取与深度学习 (DLIECG) 结合起来.
主要成果:
- 在80次代后,TD模型表现出最佳性能,达到0.97准确度,0.93回忆和0.95F1得分.
- 模型训练损失稳定在0.03,表明有效的趋同.
- DLIECG方法的整体性能优越,特别是在数据集P.上.
结论:
- 拟议的智能英语作文评分方法与传统方法相比,显示出更高的有效性和可靠性.
- 在自动化作文评估中,TD模型对于准确评估主题相关性至关重要.
- 将深度学习与特征提取集成为智能英语作文评分提供了强大的解决方案.
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