Chinese Medical Named Entity Recognition Combined with Multi-Feature Embedding and Multi-Network Fusion

In the medical field, entity recognition can extract valuable information from the text of large-scale electronic medical records. Due to the lack of features for locating entity boundaries and incomplete semantic information extraction, the implementation of Chinese Named Entity Recognition(NER) is more difficult. In this paper, a model combining Multi-Feature Embedding and Multi-Net-work Fusion model (MFE-MNF) is proposed. The model embeds multi-granularity features, i.e. characters, words, radicals and external knowledge, extends the feature representation of characters and defines the entity boundary. The feature vectors are input respectively into the two paths of Bi-directional Long Short-Term Memory (BiLSTM) and adaptive graph convolution network to capture comprehensively and deeply the context semantic information and global semantic information, and alleviate the problem of incomplete semantic information extraction. The experimental results on CCKS2019 and CCKS2020 datasets show that compared with the traditional entity recognition model, the proposed model can extract entities accurately and effectively.