Named entity recognition of chemical experiment operations based on BERT

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.