A named entity recognition model for fertilizer knowledge areas based on BERT and adversarial training
Hui Chen, Aiju Shi, Guang-kuo XIE, Cheng-min LEI, Shaomin Mu · 2023
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.