MBG-C: Optimizing Chinese Named Entity Recognition with MBG-C NER

In the current era characterized by exponential data growth, industries across the spectrum have amassed enormous textual data repositories brimming with untapped value. The challenge lies in efficiently and precisely mining this information, posing a universal dilemma for academia and industry alike. Introducing the MBG-C Ner model, this study innovatively fuses MacBERT, BiGRU, and CRF methodologies to adeptly grasp contextual nuances, tackle long-range dependencies, and refine label sequence decoding, thereby enhancing named entity recognition capabilities. To validate its efficacy, rigorous experiments spanning diverse datasets—MSRA, OntoNotes-4, Weibo NER, and Resume—were undertaken. The outcomes underscore MBG-C Ner's remarkable superiority over cutting-edge techniques in Chinese named entity recognition, evidenced by peak F1 scores of 95.70%, 80.17%, 69.76%, and 97.46%, respectively. This milestone not only validates MBG-C Ner's exceptional performance but also opens new avenues and strategies for advancing Chinese named entity recognition research.