Automatic Detection of Grammatical Errors in English Verbs Based on RNN Algorithm: Auxiliary Objectives for Neural...

Area of Science:

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence

Background:

• The increasing use of neural networks necessitates advanced language processing tools.
• Manual detection of English verb grammatical errors is inefficient and outdated.
• Automatic error detection is crucial for improving language translation quality.

Purpose of the Study:

• To propose an automatic detection technology for English verb grammatical errors using Recurrent Neural Network (RNN).
• To compare the accuracy and feedback speed of RNN-based detection with traditional manual methods.
• To design a context-aware detection model for identifying verb grammatical errors.

Main Methods:

• Comparison of accuracy and feedback speed between manual detection and RNN algorithm.
• Development of a detection model integrating grammatical order and contextual information.
• Implementation of an automatic marking system for detected verb grammatical errors.

Main Results:

• The RNN algorithm demonstrates superior accuracy and feedback efficiency compared to manual detection.
• The designed model effectively identifies verb grammatical errors by considering grammatical order and context.
• Experimental results validate the applicability and effectiveness of the proposed RNN-based approach.

Conclusions:

• The proposed RNN-based automatic detection technology significantly improves English verb grammatical error identification.
• This method offers a more applicable and efficient alternative to traditional manual error detection.
• The study highlights the potential of RNNs in advancing automated language error correction.