Virus Named Entity Recognition based on Pre-training Model

Virus plays an important role in the earth's ecosystem. It maintains the balance of the ecosystem. It infects host cells, causing damage or death to the host. Understanding the relationship between virus and host is the key to preventing viral diseases. There are a large number of proven relationships between viruses and hosts in the literature. Extracting the relationships between viruses and hosts in these literature and organizing them into a virus-host knowledge base is of great significance to medical and biological research. Virus named entity recognition (VNER) is the key prerequisite step of relationship extraction. The complexity of virus naming and classification makes the identification of virus named entities challenging. In this paper, we provide a labeled corpus for the task of VNER. Furthermore, we use different pre-training models to compare its performance on downstream virus entity recognition tasks. Finally, BioBERT_ BiLSTM_ CRF got best result on the task of VNER. The precision value, recall value and F1 value are 92.18%, 91.28% and 91.85%, respectively.