# A method for English paragraph grammar correction based on differential fusion of syntactic features

## TL;DR

This paper introduces a new method for correcting grammar errors in English paragraphs by combining syntactic features with deep learning techniques.

## Contribution

The paper proposes a differential fusion method that leverages syntactic features to enhance grammar correction accuracy.

## Key findings

- The method achieves 0.88 accuracy on the CoLA dataset, outperforming BERT-GC by 3 percentage points.
- It shows 0.86 accuracy on the LCoLE dataset and 0.89 on the FCE dataset, surpassing baseline models.
- The approach improves grammar error recognition and correction, benefiting English learners and writing quality.

## Abstract

The new progress of deep learning and natural language processing technology has strongly promoted the development of English grammar error correction. However, the existing methods mostly rely on large-scale corpus, and often ignore the fine syntactic correlation in paragraphs, which limits the efficiency in complex grammar error correction scenarios. In order to break through this bottleneck, this study proposes an innovative method to effectively use syntactic features to improve the quality and accuracy of paragraph-level grammar correction. Firstly, the sentence vector representation is constructed by BERT, and then the syntactic structure is extracted by dependency parsing. Then carry out difference fusion analysis, measure the syntactic differences of adjacent sentences by cosine similarity, identify the significant differences caused by grammatical errors according to the preset threshold, lock the position and type of errors, and input the original sentence vector into the Seq2Seq model based on Transformer. The model focuses on the wrong area by attention mechanism to generate correction suggestions. The preliminary results show that this method is significantly better than the existing grammar error correction system. In CoLA dataset, the accuracy is 0.88, which is three percentage points higher than that of BERT-GC. The accuracy of LCoLE dataset is 0.86, which is ahead of the baseline model. The accuracy of FCE data set is 0.89, which has obvious advantages. The accuracy is improved by 3% to a higher level. It shows the excellent effect of this method in grammar error recognition and correction, and has far-reaching significance in providing accurate error correction suggestions, helping English learners improve their writing ability and ensuring the quality of English writing. This study not only presents a powerful approach to English grammar error correction, but also highlights the key value of syntactic features in optimizing natural language processing applications.