Chinese clinical named entity recognition based on stacked neural network,Concurrency and Computation: Practice and Experience

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Chinese clinical named entity recognition based on stacked neural network
Concurrency and Computation: Practice and Experience ( IF 2 ) Pub Date : 2020-04-28 , DOI: 10.1002/cpe.5775
Ruoyu Zhang ₁ , Wenpeng Lu ₁ , Shoujin Wang ₂ , Xueping Peng ₃ , Rui Yu ₁ , Yuan Gao ₄

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The 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.

更新日期：2020-04-28

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