Drilling risk named entity recognition based on RoBERTa-BiLSTM-CRF

Traditional methods for named entity recognition in the construction of a drilling risk knowledge graph face challenges regarding feature extraction accuracy and recognition efficiency. To overcome these issues, a research method based on the RoBERTa-BiLSTM-CRF model is proposed. This method utilizes RoBERTa for word embeddings and applies a BiLSTM network for contextual feature extraction. The Conditional Random Field (CRF) is used for sequence labeling, resulting in a named entity recognition framework for the drilling risk domain. Comparative experiments were conducted on a self-built dataset, comparing the RoBERTa-BiLSTM-CRF model with RoBERTa-BiLSTM and BERT-BiLSTMCRF. The results demonstrate that the RoBERTa-BiLSTM-CRF model achieves superior precision, recall, and F1-score of 89.7%, 89.3%, and 89.1%, outperforming the other models in terms of entity recognition performance.