对SMOSS模型的语法分析与改进的LSTM模型相结合:以英语写作教学为例
1Department of Public Instruction, Nanyang Medical College, Nanyang, Henan, China.
PloS one
|November 15, 2024
概括
这项研究将顺序匹配在滑动窗序列 (SMOSS) 模型与改进的长短期记忆 (LSTM) 网络相结合,用于英语写作教育. 在英语写作教学中,SMOSS-LSTM模型显著提高了语法分析和错误检测准确度.
科学领域:
- 计算语言学 计算语言学
- 自然语言处理自然语言处理.
- 教育技术的教育技术
背景情况:
- 提高英语写作质量需要增强语法理解.
- 传统模型可能无法完全捕捉教学中复杂的语言结构.
- 在英语写作教学中需要有效的工具.
研究的目的:
- 集成顺序匹配在滑动窗序列 (SMOSS) 模型与优化的长短期内存 (LSTM) 网络.
- 为了提高学习者的语法理解和英语写作能力.
- 开发一个强大的英语文本错误检测模型.
主要方法:
- 分析了SMOSS模型的结构.
- 优化了LSTM模型,使用Connectist时间分类 (CTC) 进行错误检测.
- 将SMOSS和优化的LSTM结合到一个语法分析框架中,在代码中实现.
主要成果:
- 在语法分析 (89.5%) 和错误检测 (94.8%) 中,SMOSS-LSTM 模型实现了高精度,超过了传统方法.
- 词语意义的明确度在"7+7"的上下文范围达到顶峰.
- 虽然在单词意义的明确性方面略低于BERT的准确性,但该模型在语法分析和错误检测方面表现出色.
结论:
- 提出的SMOSS-LSTM模型对于英语写作教学中的语法分析是有效和实用的.
- 这种方法为应用语法分析提供了新的方法,以改善英语语言教育.
- 该模型为提高教学质量和提高学生写作能力提供了宝贵的工具.
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