Research on Retrieval Method of Online English Grammar Questions Based on Natural Language Processing

Abstract

The rapid development of information technology is driving the advancement of natural language processing. The retrieval of grammatical problems in natural language processing is one of its specific tasks, particularly in the context of online learning. Therefore, a retrieval method based on fuzzy tree matching is proposed to tackle the issue of grammatical multiple-choice questions (MCQs) in online English, and its effectiveness is validated through experiments. The experimental results indicate that for incomplete queries, the MRR value STPK of the grammatical MCQ questions STP is increased by 7.9% compared to the proposed method. Compared to the traditional POS sorting algorithm, this algorithm demonstrates a 2.1% improvement. When the recall rate is 0.1, the accuracy rate of other methods is below 0.4, while the method proposed in the study surpasses 0.4. In the case of a comprehensive query, STPK the MRR value for t is STP increases by 29.6%. The proposed method in the research generally maintains an accuracy rate between 0.2 and 1.0. However, when other methods achieve an accuracy rate of 0.2, the proposed method’s accuracy rate drops below 0.2. Overall, the proposed method effectively enhances the retrieval accuracy of online English grammar MCQs compared to existing statistical and grammatical analysis methods. This improvement holds great significance for the actual retrieval of online English grammar multiple-choice questions.