ArticlePloS one2024
Syntactic analysis of SMOSS model combined with improved LSTM model: Taking English writing teaching as an example.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Abstract
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.
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