Apr 28, 2020 · 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 ...

Apr 28, 2020 · In this article, we introduce a Chinese clinical NER based on stacked neural network model, which performs well on the three real-world datasets.

The neural network stacks two bidirectional LSTM and GRU layers to encode the text twice, followed by a CRF layer to recognize named entities in Chinese.

A deep stacked neural network is proposed for Chinese clinical NER that stacks two bidirectional long‐short term memory and gated recurrent unit layers to ...

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 ...

Chinese clinical named entity recognition based on stacked neural network. Ruoyu Zhang, Wenpeng Lu, Shoujin Wang, Xueping Peng, Rui Yu, Yuan Gao.

In this study, we designed a novel deep neural network model which combines BiLSTM and the multi-head self-attention mechanism in parallel to solve the Chinese ...

May 10, 2023 · We aimed to propose a Chinese CNER method to learn semantics-enriched representations for comprehensively enhancing machines to understand deep semantic ...

This paper proposes a Chinese medical named entity recognition model that combines contextual dependency perception and a new memory unit.

Apr 18, 2023 · The model proposed in this paper improves the accuracy of named entity recognition by 1%, the recall rate by 2%, and the F1 value by 2% on average in the field ...