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2 May 2023 A named entity recognition model for fertilizer knowledge areas based on BERT and adversarial training
Hui Chen, Ai-ju Shi, Guang-kuo Xie, Cheng-min Lei, Shao-min Mu
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Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126422N (2023) https://doi.org/10.1117/12.2674724
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China
Abstract
To address the problem of inaccurate identification of entities with fuzzy fertilizer knowledge areas boundaries. In this paper, we propose a named entity recognition model based on BERT with adversarial training. Disturbance factors generation by introducing FreeLB adversarial training method, combined with the vectors of the word embedding layer in BERT to form the adversarial sample. Improving model recognition of boundary ambiguous entities by adversarial training. Experimental results show that, the model proposed in this paper achieves 89.77% accuracy, 93.72% recall and 91.70% F1 on the dataset. compared with other models, the accuracy, recall and F1 values are improved by 1.75%, 0.53% and 1.17% respectively.
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Hui Chen, Ai-ju Shi, Guang-kuo Xie, Cheng-min Lei, and Shao-min Mu "A named entity recognition model for fertilizer knowledge areas based on BERT and adversarial training", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126422N (2 May 2023); https://doi.org/10.1117/12.2674724
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KEYWORDS
Adversarial training
Data modeling
Agriculture
Statistical modeling
Deep learning
Data acquisition
Process modeling
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