A Computational Neural Network Model for College English Grammar Correction

Abstract

For the error correction of English grammar, if there are errors in the semantic units (words and sentences), it will inevitably affect the subsequent text analysis and semantic understanding, and ultimately reduce the overall performance of the practical application system. Therefore, intelligent error detection and correction of the word and grammatical errors in English texts is one of the key and difficult points of natural language processing. This exploration innovatively combines a computational neural model with college grammar error correction to improve the accuracy of college grammar error correction. It studies the computational neural model in English grammar error correction based on a neural network named Knowledge and Neural machine translation powered College English Grammar Typo Correction (KNGTC). First, the Recurrent Neural Network is introduced, and the overall structure of the English grammatical error correction neural model is constructed. Moreover, the supervised training of Attention is discussed, and the experimental environment and experimental data are given. The results show that KNGTC has high accuracy in college English grammar correction, and the accuracy of this model in CET-4 and CET-6 writing can reach 82.69%. The English grammar error correction model based on the computational neural network has perfect function and strong error correction ability. The optimization and perfection of the model can improve students’ English grammar level, which has certain practical value. After years of continuous optimization and improvement, English grammar error correction technology has entered a performance bottleneck. This mode’s construction can break the current technology’s limitations and bring a better user experience. Therefore, it is very valuable to study the error correction model of English grammar in practical application.