Research on Named Entity Recognition Method Based on BERT Model

Named Entity Recognition (NER), as a fundamental task in the field of natural language processing, directly determines the efficiency and accuracy of semantic processing and inference in subsequent tasks. In previous studies, the Bi-LSTM model has demonstrated relatively mature and stable performance in entity extraction tasks. However, with the continuous evolution of technology, pre-trained models have now become the preferred choice for mainstream NLP tasks. In this study, based on five publicly annotated datasets, we conducted an in-depth comparison between the Bi-LSTM and BERT models in the task of named entity recognition. We found that in four out of the five datasets, the performance of the BERT model was superior to that of Bi-LSTM. This result fully demonstrates the excellent performance of pre-trained models in the field of named entity recognition, especially when dealing with large-scale and complex textual data, where their performance is even more outstanding. Thanks to pre-trained models, which are trained on large-scale corpora, they can learn rich language rules and knowledge, thereby more accurately capturing deep semantic information in text and improving the accuracy and efficiency of recognition.