RIBERT Leveraging Rule Information for Chemical Production Named Entity Recognition via Pretrained BERT

Chemical Named Entity Recognition (NER) faces critical challenges in accurately identifying domainspecific entities such as complex compound nomenclature and safety protocols.To address the underutilization of structural rules in chemical literature, this study proposes RiBERT, a novel framework integrating rule-informed boundary definitions with a hybrid BERT-KNN architecture.We systematically define 15 entity types across 4 categories based on regulatory documents.The model synergizes BERT's contextual embeddings with KNN's non-parametric retrieval to dynamically adapt to sparse entities and lexical variations.Evaluations on specialized chemical and public maintenance corpora demonstrate F1 scores of 69.77% and 88.12%, outperforming state-of-the-art baselines by up to 3.91%.The results indicate that incorporating rule-based information significantly enhances NER performance for chemical data.