Named entity recognition of chemical experiment operations based on BERT

Chuanning He; Han Zhang; Jiasheng Liu; Yue Shi; Haoyuan Li; Jianhua Zhang

doi:10.1117/12.3011770

7 December 2023 Named entity recognition of chemical experiment operations based on BERT

Chuanning He, Han Zhang, Jiasheng Liu, Yue Shi, Haoyuan Li, Jianhua Zhang

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 1294139 (2023) https://doi.org/10.1117/12.3011770
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Named Entity Recognition (NER) for chemical experiment operations can not only extract key information and automatically generate operation instructions in the field of automated synthesis but also facilitate chemical experiment personnel in analyzing literature data more efficiently. In this paper, we propose a NER model that combines multiple layers of BiLSTM and IDCNN in parallel, based on the Bert pre-trained model. By adjusting the number of BiLSTM and IDCNN modules at each layer, we can extract more contextual information and local feature for different datasets, and subsequently generate entity labels using a Conditional Random Field (CRF) layer. The experimental results indicate that the model achieves an F1 score of 0.9174 in the constructed dataset, surpassing existing algorithms.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chuanning He, Han Zhang, Jiasheng Liu, Yue Shi, Haoyuan Li, and Jianhua Zhang "Named entity recognition of chemical experiment operations based on BERT", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 1294139 (7 December 2023); https://doi.org/10.1117/12.3011770

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