A multi-mechanism fusion research method for entity relationship extraction modeling

NLP relies heavily on entity-relationship extraction, but complicated structures provide challenges for conventional approaches. This paper proposes an advanced model integrating multi-head attention, BiLSTM, CRF, and gated attention mechanisms, leveraging the pre-trained Chinese-RoBERTa-wwm-ext model for text encoding. The Attention-Enhanced BiLSTM captures dependencies and focuses on key information, while CRF optimizes globally. Experiments on the DuIE dataset demonstrate improved efficiency, accuracy, and resilience, offering a robust solution for entity-relationship extraction across various applications.