Research on Optimizing English Writing Automatic Feedback System from the Perspective of Computational Linguistics

Abstract

Current deficiencies of English writing automatic feedback systems in grammatical error correction, semantic understanding, and pragmatic appropriateness may keep learners from more personalized and effective feedback in the computer-assisted language learning (CALL). The paper integrates key natural language processing skills with the English writing evaluation system to reveal the existing system issues, and provides targeted optimization strategies from the perspective of computational linguistics, including corpus construction, model training, and experimental analysis. The following experimental results show that the optimized system significantly improves grammar correction accuracy, textual logic recognition rate and other core indicators. It also offers learners outstanding practicality in personalized feedback compared to mainstream systems, which effectively assists learners in enhancing their English writing skills and provides reliable technical support and practical reference for CALL.