Design And Implementation Of An Automatic Grammar Error Correction Model For English Text

As a global language,English plays an important role in the political,economic,cultural and other fields of communication between countries.The importance of learning English is self-evident.In China,the learning of English runs through the entire learning career of students.The study of English grammar is the foundation of learning English well.Only by mastering English grammar skillfully can one better understand the meaning of English sentences and write texts that conform to the norms of English expression.The traditional process of English grammar learning involves teaching grammar knowledge to teachers and consolidating students’ writing after class.While teachers have a heavy workload in correcting compositions,students have a long period of receiving correction feedback,which affects the efficiency of students’ English grammar learning.With the development of natural language processing technology,it has become possible to automatically correct grammatical errors in English text.Design a grammar automatic correction model suitable for Chinese students to automatically correct grammar in their English compositions,and develop a specific application system based on this model,which can greatly assist teachers in completing the tasks of English grammar teaching and improve students’ grammar learning efficiency.Based on the English corpus of English learners both at home and abroad,this study designed an automatic grammar correction model for English texts,and developed an intelligent grammar correction system for English compositions based on this model to achieve automatic grammar correction for Chinese students’ English compositions.The main contents of this study are as follows:1.An automatic grammar error correction model with reference to both syntactic and semantic information is studied and designed.Based on the encoder-decoder framework,the model uses a combination of dual encoders and hybrid attention mechanisms to improve error correction performance.The dual encoder includes a syntactic encoder and a semantic encoder.The syntactic encoder uses Bi-GRU(Bidirectional Gated Recurrent Unit)to automatically extract syntactic information from the part-of-speech tagging results,and the semantic encoder uses BERT(Bidirectional Encoder Representations from Transformers)and Bi-GRU to extract semantic information from the original sentences.The decoder uses a hybrid attention mechanism to refer to the syntactic and semantic information extracted by the encoder to improve the accuracy of decoding.2.Based on the probability distribution of the types of grammatical errors frequently made by Chinese students,a data expansion method combining rules and probability is designed to expand the learner corpus data.The expanded data is used to jointly train the grammatical error correction model with the learner corpus data,so that the grammatical error correction model has better performance when applied to correcting grammatical errors in Chinese students’ English compositions.The experiment shows that the accuracy rate of grammatical error correction of the model on Chinese students’ English writing corpus reaches 83.25%,which has good application value.3.Based on the grammar error correction model studied,a specific application,namely an intelligent grammar error correction system for English writing,has been developed.The system can automatically correct grammar errors in input English compositions,visually display grammar correction results,and provide corresponding suggestions.It can meet the daily grammar correction needs of teachers and students in English compositions.