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

Yingzhuo Xu; Lili Zhang

doi:10.1117/12.3035586

16 August 2024 Drilling risk named entity recognition based on RoBERTa-BiLSTM-CRF

Yingzhuo Xu, Lili Zhang

Proceedings Volume 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024); 132300W (2024) https://doi.org/10.1117/12.3035586
Event: Third International Conference on Machine Vision, Automatic Identification and Detection, 2024, Kunming, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yingzhuo Xu and Lili Zhang "Drilling risk named entity recognition based on RoBERTa-BiLSTM-CRF", Proc. SPIE 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024), 132300W (16 August 2024); https://doi.org/10.1117/12.3035586

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