A Grammatical Error Correction Model for English Essay Words in Colleges Using Natural Language Processing

• Published in: Mobile information systems Vol. 2022; pp. 1 - 9
• Main Author: Long, Juan
• Format: Journal Article
• Language: English
• Published: Amsterdam Hindawi 13.07.2022; John Wiley & Sons, Inc
• Subjects: Abbreviations; Accuracy; Algorithms; Artificial intelligence; Computers; Context; English language; Error correction; Error correction & detection; Error detection; Essays; Grammar; Information retrieval; Language; Mathematical models; Microbalances; Natural language processing; Search engines; Semantics; Sentences; Software; Speech; Spelling; Statistical methods; Statistical models; Words (language); Writing
• ISSN: 1574-017X; 1875-905X; 1875-905X
• DOI: 10.1155/2022/1881369

Natural language processing technology is a theory and approach for exploring and developing successful human-computer communication. With the rapid growth of computer science and technology, statistical learning methods have become an important research area in artificial intelligence and semantic search. If there are errors in the semantic units (words and sentences), it will affect future text analysis and semantic understanding, eventually affecting the whole application system performance. As a result, intelligent word and grammatical error detection and correction in English text are a significant and difficult aspect of natural language processing. Therefore, this paper examines the phenomena of word spelling and grammatical errors in undergraduate English essays and balances the mathematical-statistical models and technology solutions involved in intelligent error correction. The research findings of this study are represented in two aspects. (1) In nonword mistakes, four sorts of errors are studied: insertion, loss, replacement, and exchange between letters. It focuses on nonword mistakes and varied word forms (such as English abbreviations, hyphenated compound terms, and proper nouns) produced by word pronunciation difficulties. This paper utilizes the nonword check information to recommend an optimum combination prediction method based on the suggested candidate list for actual word errors, and the genuine word repair model is trained. This approach is 83.78% accurate when used with actual words with spelling errors in the context. (2) It verifies and corrects sentence grammar using context information from the text training set, as well as grammatical rules and statistical models. In addition, it has investigated singular and plural inconsistency, word confusion, subject, and predicate inconsistency, and modal (auxiliary) verb errors. It includes sentence boundary disambiguation, word part-of-speech tagging, named entity identification, and context information extraction. The software for checking and fixing sentence grammatical mistakes presented in this article works on English texts with difficulty levels 4 and 6. Furthermore, this work obtains a clause correctness rate of 99.70%, and the system’s average corrective accuracy rate for four-level and six-level essays is more than 80%.