Chinese clinical named entity recognition based on stacked neural network

Abstract

SummaryThe precise named entity recognition (NER) is a key component in Chinese clinical natural language processing. Although clinical NER systems have attracted widespread attention and been studied for decades, the latest NER research usually relies on a shallow text representation with one‐layer neural encoding, which fails to capture deep features and limits its performance improvement. To capture more features and encode the clinical text efficiently, we propose a deep stacked neural network for Chinese clinical NER. The neural network stacks two bidirectional long‐short term memory and gated recurrent unit layers to encode the text twice, followed by a conditional random fields (CRF) layer to recognize named entities in Chinese clinical text. Extensive empirical results on three real‐world datasets demonstrate that the proposed method significantly outperforms six state‐of‐the‐art NER methods. Especially compared with the conventional CRF model, our method has at least 3.75% F1‐score improvement on these public datasets.