Fine-Grained Chinese Named Entity Recognition Based on RoBERTa-WWM-BiLSTM-CRF Model

Named entity recognition (NER) is a basic technology of Natural Language Processing (NLP). It is mainly used to identify entities and entity types. Compared with traditional entity recognition, fine-grained entity recognition can provide more precise semantics. In order to improve the effect of fine-grained Chinese N ER, w e propose a model based on RoBERTa-WWM-BiLSTM-CRF and compare it with other high-quality models. The experimental results show that this model has better effect on the CLUENER2020 dataset of fine-grained Chinese NER.