English Text Spelling Error Detection and Correction Based on Multi-feature data Fusion Algorithm

• Published in: 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT) pp. 1 - 5
• Main Authors: Wen, Jin, Feng, Xin, Fu, Fengyang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 15.03.2024
• Subjects: Data integration; Data models; detection and correction; English text spelling errors; machine learning; Machine learning algorithms; multi-feature data fusion algorithm; Natural language processing; Semantics; Text recognition; Vocabulary
• DOI: 10.1109/ICDCOT61034.2024.10515339

English text spelling error detection and correction is a traditional research in computer-assisted language and one of the important tasks in the field of natural language processing, which aims to identify and correct spelling errors that exist in the input utterances by analyzing the grammatical dependencies and logics between the components of the statements. In the process of learning a second language, learners need to ensure grammatical correctness in addition to quickly recognizing and correcting word spelling errors. Only under the constraints of grammar, the semantic expression can be more accurate, clear and effective. Based on the above problems, this paper explored English text spelling error detection and correction based on multi-feature data fusion algorithm, constructed an English text spelling error detection and correction model using machine learning algorithm, and finally experimentally proved the model's sophistication (the three major indexes of the system's error-correction performance precision, recall, and F0.5 were more than 0.75, more than 0.7, and more than 0.5).