Named Entity Recognition of Hazardous Chemical Risk Information Based on Multihead Self-Attention Mechanism and BERT

An approach based on self-attention mechanism and pretrained model BERT is proposed to solve the problems of entity recognition and relationship recognition of hazardous chemical risk information. The text of hazardous chemical risk information is coded at the character level by adding the pretrained language model BERT, which, when paired with a multihead self-attention mechanism, improves the ability to mine global and local aspects of texts. The experimental results show that the model’s F1 value is 94.57 percent, which is significantly higher than that of other standard models.