Multi-level semantic fusion network for Chinese medical named entity recognition.

Medical named entity recognition (MNER) is a fundamental component of understanding the unstructured medical texts in electronic health records, and it has received widespread attention in both academia and industry. However, the previous approaches of MNER do not make full use of hierarchical semantics from morphology to syntactic relationships like word dependency. Furthermore, extracting entities from Chinese medical texts is a more complex task because it usually contains for example homophones or pictophonetic characters. In this paper, we propose a multi-level semantic fusion network for Chinese medical named entity recognition, which fuses semantic information on morphology, character, word and syntactic level. We take radical as morphology semantic, pinyin and character dictionary as character semantic, word dictionary as word semantic, and these semantic features are fused by BiLSTM to get the contextualized representation. Then we use a graph neural network to model word dependency as syntactic semantic to enhance the contextualized representation. The experimental results show the effectiveness of the proposed model on two public datasets and robustness in real-world scenarios.