Machine Learning-Based Automatic Syntax and Semantic Error Identification in English Writings

Proficiency in written English is crucial for academic and professional achievement. However, the competency of non-native speakers is an issue in syntax and semantics thus the coherency of their writings may be affected. Current grammar checkers like Grammarly as well as MS Word grammar and spelling checkers are normally able to only scan for simple grammar mishaps, for example, spelling errors and hence fail to flag complicated syntactical and/or semantic inaccuracies. The current research aims at creating a machine learning approach to error identification of syntax and semantic errors in English written work to improve feedback on language concerns. The study proposes a synergistic model that integrates rule-based methods for syntax error detection whereas the semantic error identification is done by large language model (GPT). This integration is designed to overcome the limitations of existing tools, providing a more comprehensive error detection system. The proposed framework has three main stages: (1) rule-based methods for detecting syntax errors, such as tense agreement and subject-verb concord, (2) a deep learning model (GPT) for identifying semantic errors related to word choice and context, and (3) integration of results to produce a detailed, categorized error report. These results indicate high performance with 98% of accuracy which means that the model helped to perform more accurate evaluation and revealed slightly increased clarity and coherence of the evaluated samples. This hybrid model provides a rich solution for identifying syntax and semantic errors and improving the quality of comments particularly to the non-English speaking students. This framework may be useful for learners in academic contexts to help those learning English gain greater abilities.