English Grammar Error Correction Algorithm Based on Classification Model

Abstract

English grammar error correction algorithm refers to the use of computer programming technology to automatically recognize and correct the grammar errors contained in English text written by nonnative language learners. Classification model is the core of machine learning and data mining, which can be applied to extracting information from English text data and constructing a reliable grammar correction method. On the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of English grammar error correction algorithm, elaborated the development background, current status, and future challenges of the classification model, introduced the methods and principles of feature extraction method and dynamic residual structure, constructed a basic model for English grammar error correction based on the classification model, analyzed the classification model and translation model of English grammar error correction, proposed the English grammar error correction algorithm based on the classification model, performed the analyses of the model architecture and model optimizer of the grammar error correction algorithm, and finally conducted a simulation experiment and its result analysis. The study results show that, with the continuous increase of training samples and the continuous progress of learning process, the proposed English grammar error correction algorithm based on the classification model will continue to increase its classification accuracy, further refine its recognition rules, and gradually improve correction efficiency, thereby reducing processing time, saving storage space, and streamlining processing flow. The study results of this paper provide a certain reference for the further research on English grammar error correction algorithm based on the classification model.

Improving fake news classification using dependency grammar

“…Their detector can detect more than 94% of grammatical errors. Very similar research for English grammar correction based on dependency grammar was proposed by Zhou and Liu [ 18 ]. Comparative analysis of related work presented in this paper is shown in Table 1 .…”

“…These methods can be used for other applications, such as feature extraction, sentiment analysis, or other classification tasks [13][14][15][16]. Dependency grammar is widely used in grammar error detection and correction [17,18]. Researchers use both statistical and machine learning methods for classification tasks [3,5,19].…”

Identification of Grammatical Errors of English Language Based on Intelligent Translational Model

“…Compared with words, there are many more correct words in the sentences than the words containing errors, so the Bi-LSTM neural network structure always tends to mark 0, which means that if there is no balance, the sentence is wrong. erefore, weights are assigned to the loss function [7], in order to rebalance the correct and incorrect labels.…”

Automatic Grammar Error Correction Model Based on Encoder-decoder Structure for English Texts

“…Cheng et al [15] designed a character-level deep learning model based on transformer and Seq2Seq, and used a model ensemble approach combined with an N-gram language model to obtain the highest-scoring output. Zhou et al [16] used the idea of classification model to design a grammar error correction model, and continuously optimized the model through the grammatical relationship and hierarchical structure between words. Tarnavskyi et al [17] made an in-depth study on the sequence tagging method of pretrained large-scale models, ensemble models by spanlevel edits voting algorithm, and achieved new SOTA results on the BEA-2019 test set.…”