Research on automatic parsing and error correction of complex English grammatical mechanisms based on deep neural networks and transfer learning

With English as the dominant global language, demand for precise grammar processing grows rapidly. Traditional methods struggle with complex grammatical structures, especially for non-native speakers. To address this, we propose a deep neural network (DNN) and transfer learning-based system for automated English grammar parsing and correction. Our approach: (1) Uses Transformer-based DNNs to capture intricate grammar patterns from large corpora; (2) Applies transfer learning to adapt generic linguistic knowledge to specialized error correction; (3) Achieves 10% higher accuracy and 8% better recall than traditional tools. Critically, it handles complex errors like subject-verb disagreement and tense misuse with high robustness.