{"id":10612,"date":"2026-08-22T16:58:00","date_gmt":"2026-08-22T08:58:00","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=10612"},"modified":"2026-08-22T18:03:05","modified_gmt":"2026-08-22T10:03:05","slug":"jase-202611-34-065","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-065","title":{"rendered":"Interpretable Artificial Intelligence Used to Diagnose Common Grammatical Errors in English Writing by Non-Native Engineering Researcher"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

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Yafeng Liu<\/i><\/a> <\/p>\n\n\n\n

School of English Language and Culture, Xi\u2019an Fanyi University, Xi\u2019an 710105, China<\/p>\n<\/div>\n\n\n\n

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Received: May 15, 2026
Accepted: July 24, 2026
Publication Date: August 22, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Architecture of TF\u2013IDF and Logistic Regression-Based Grammatical Error Detection Framework<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.065<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Grammatical error detection is an essential task in natural language processing that supports automated writing assistance and educational language assessment systems. This study proposes an explainable framework for identifying multiple categories of grammatical errors in English text. The model was evaluated using a dataset of 2,990 annotated English sentences containing both grammatically correct and erroneous structures across several error categories. The proposed framework integrates text pre-processing, Term Frequency\u2013Inverse Document Frequency (TF\u2013IDF) based word-level n-gram feature extraction, and a multi-class Logistic Regression (LR) classifier. The framework generates efficient numerical representations of textual data while capturing lexical, syntactic, and pragmatic characteristics for accurate grammatical error identification. To enhance transparency and interpretability, SHAP (SHapley Additive exPlanations) was employed to analyze feature importance and provide word-level explanations for classification decisions, thereby incorporating explainable artificial intelligence into grammar evaluation tasks. Experimental results demonstrate strong classification performance, achieving an accuracy of 0.9766, precision of 0.9376, recall of 0.9889, macro-average F1-score of 0.9603, and weighted F1-score of 0.9772. Furthermore, the Cohen\u2019s Kappa coefficient of 0.9667 indicates a high level of agreement between predicted and actual labels, confirming the reliability of the proposed model. The combination of TF\u2013IDF statistical text representation and interpretable logistic regression provides an effective, computationally efficient, and explainable solution for automated grammatical error identification.<\/p>\n\n\n\n

Keywords: Grammatical Error Detection; Natural Language Processing; TF\u2013IDF Feature Extraction; Logistic Regression; Explainable Artificial Intelligence; SHAP Interpretability<\/em><\/p>\n\n\n\n

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