Department of Public Instruction, Nanyang Medical College, Nanyang, Henan, China.
PLoS One. 2024 Nov 15;19(11):e0312049. doi: 10.1371/journal.pone.0312049. eCollection 2024.
This paper explores the method of combining Sequential Matching on Sliding Window Sequences (SMOSS) model with improved Long Short-Term Memory (LSTM) model in English writing teaching to improve learners' syntactic understanding and writing ability, thus effectively improving the quality of English writing teaching. Firstly, this paper analyzes the structure of SMOSS model. Secondly, this paper optimizes the traditional LSTM model by using Connectist Temporal Classification (CTC), and proposes an English text error detection model. Meanwhile, this paper combines the SMOSS model with the optimized LSTM model to form a comprehensive syntactic analysis framework, and designs and implements the structure and code of the framework. Finally, on the one hand, the semantic disambiguation performance of the model is tested by using SemCor data set. On the other hand, taking English writing teaching as an example, the proposed method is further verified by designing a comparative experiment in groups. The results show that: (1) From the experimental data of word sense disambiguation, the accuracy of the SMOSS-LSTM model proposed in this paper is the lowest when the context range is "3+3", then it rises in turn at "5+5" and "7+7", reaches the highest at "7+7", and then begins to decrease at "10+10"; (2) Compared with the control group, the accuracy of syntactic analysis in the experimental group reached 89.5%, while that in the control group was only 73.2%. (3) In the aspect of English text error detection, the detection accuracy of the proposed model in the experimental group is as high as 94.8%, which is significantly better than the traditional SMOSS-based text error detection method, and its accuracy is only 68.3%. (4) Compared with other existing researches, although it is slightly inferior to Bidirectional Encoder Representations from Transformers (BERT) in word sense disambiguation, this proposed model performs well in syntactic analysis and English text error detection, and its comprehensive performance is excellent. This paper verifies the effectiveness and practicability of applying SMOSS model and improved LSTM model to the syntactic analysis task in English writing teaching, and provides new ideas and methods for the application of syntactic analysis in English teaching.
本文探讨了将序列匹配滑动窗口序列（SMOSS）模型与改进的长短期记忆（LSTM）模型相结合在英语写作教学中提高学习者句法理解和写作能力的方法，从而有效提高英语写作教学质量。首先，本文分析了 SMOSS 模型的结构。其次，本文通过使用连接时间分类（CTC）对传统 LSTM 模型进行优化，并提出了一种英语文本错误检测模型。同时，本文将 SMOSS 模型与优化后的 LSTM 模型相结合，形成一个全面的句法分析框架，并设计和实现了框架的结构和代码。最后，一方面，使用 SemCor 数据集测试模型的语义消歧性能。另一方面，以英语写作教学为例，通过分组设计对比实验进一步验证了所提出的方法。结果表明：（1）从词义消歧的实验数据来看，本文提出的 SMOSS-LSTM 模型在上下文范围为“3+3”时准确率最低，然后依次在“5+5”和“7+7”上升，在“7+7”时达到最高，然后开始在“10+10”时下降；（2）与对照组相比，实验组的句法分析准确率达到 89.5%，而对照组仅为 73.2%；（3）在英语文本错误检测方面，实验组提出的模型检测准确率高达 94.8%，明显优于基于传统 SMOSS 的文本错误检测方法，其准确率仅为 68.3%；（4）与其他现有研究相比，虽然在词义消歧方面略逊于基于双向编码器表示的转换器（BERT），但该模型在句法分析和英语文本错误检测方面表现良好，综合性能优异。本文验证了将 SMOSS 模型和改进的 LSTM 模型应用于英语写作教学中的句法分析任务的有效性和实用性，为句法分析在英语教学中的应用提供了新的思路和方法。
