A Computational Neural Network Model for College English Grammar Correction

Area of Science:

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence

Background:

• Grammatical errors in English text impede semantic understanding and system performance.
• Intelligent detection and correction of English grammar errors are crucial in Natural Language Processing.
• Existing English grammar error correction technologies face performance limitations.

Purpose of the Study:

• To enhance the accuracy of college English grammar error correction.
• To introduce an innovative computational neural model for grammar correction.
• To address the performance bottleneck in current grammar correction technologies.

Main Methods:

• Development of the Knowledge and Neural machine translation powered College English Grammar Typo Correction (KNGTC) model.
• Integration of Recurrent Neural Networks for model structure.
• Application of supervised training with Attention mechanisms.

Main Results:

• The KNGTC model demonstrates high accuracy in college English grammar correction.
• Achieved an accuracy of 82.69% in correcting errors in CET-4 and CET-6 writing.
• The model exhibits robust error correction capabilities.

Conclusions:

• The KNGTC model offers a valuable solution for improving English grammar levels among students.
• This computational neural network-based approach can overcome current technological limitations.
• The study highlights the practical value and potential for enhanced user experience in English grammar correction.