Yingying Xiao∗
Error detection and correction, fuzzy logic (FL), language learning, English writing
English is one of the prominent languages spoken by most people worldwide, and it is an essential component. English language learners encounter several difficulties with listening, writing, and speaking. The most significant and challenging one is writing, and the most prevalent errors in English are grammatical. Errors in English grammar, especially in semantic portions such as sentences and their associated words, can significantly affect language learning. Therefore, addressing these errors is crucial to enhance learners’ accuracy and adequate understanding of the language. The research’s novel idea resides in developing a fuzzy logic-based English validation model (FL-EVM) for detecting and rectifying language writing and learning errors. The model aims to enhance the effectiveness of error detection and correction techniques, ultimately improving the learner’s English learning experience. For this, Levenshtein edit distance in fuzzy matching is integrated with the fuzzy logic (FL) to accurately measure errors by performing insertion, substitution, and deletion operations. The validations used the triangular fuzzy membership function with Mamdani inference model-based FL for English writing and language learners. The observed results from the comparison study significantly improved the accuracy of the error detection and correction model in the English language.
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