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Chinese Drug Information Named Entity Recognition Based on MSCNN And BERT-BiLSTM-CRF

In the study of drug-drug interactions, knowledge graph on drugs plays an important role to extract drug features. Named entity recognition is a key to construct knowledge graph. The traditional method BERT-BiLSTM-CRF is usually effective in extracting entities from the corpus. However, in the field of Chinese drug information, the statement format of the corpus is not uniform, and the entities are long and semantically complex, such as in the extraction of key entities such as adverse reactions, indications and contraindications in drug instructions, the traditional methods cannot effectively deal with the above difficulties. In this work, we propose a CDINER model to solve the Chinese drug information named entity recognition. The model performs a multi-scale convolution after the character vector are generated by the BERT model, and then to be input to the Bi-LSTM. Then the input and output of the BiLSTM module are concatenated by skip connect and fed to the CRF module. In the experiments, we compare our proposed method with other classical models in the Chinese drug information corpus. The results show that our model outperforms others. It can effectively identify three types of drug information entities that consist of adverse reactions, indications and contraindications.