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An Improved Method for Named Entity Recognition and Its Application to CEMR

Department of Intelligent Information Processing, Shanghai University, Shanghai 200444, China
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This paper is an extended version of our paper published in This paper is an extended version of our paper: Ming Gao, Qifeng Xiao, Shaochun Wu, Kun Deng, An attention-based ID-CNNs-CRF model for named entity recognition on clinical electronic medical records. In the Proceedings of the 28th International Conference on Artificial Neural Networks, 17th–19th September, 2019, Munich, Germany; No.195.
Future Internet 2019, 11(9), 185; https://doi.org/10.3390/fi11090185
Received: 8 July 2019 / Revised: 11 August 2019 / Accepted: 15 August 2019 / Published: 26 August 2019
(This article belongs to the Section Big Data and Augmented Intelligence)
Named Entity Recognition (NER) on Clinical Electronic Medical Records (CEMR) is a fundamental step in extracting disease knowledge by identifying specific entity terms such as diseases, symptoms, etc. However, the state-of-the-art NER methods based on Long Short-Term Memory (LSTM) fail to exploit GPU parallelism fully under the massive medical records. Although a novel NER method based on Iterated Dilated CNNs (ID-CNNs) can accelerate network computing, it tends to ignore the word-order feature and semantic information of the current word. In order to enhance the performance of ID-CNNs-based models on NER tasks, an attention-based ID-CNNs-CRF model, which combines the word-order feature and local context, is proposed. Firstly, position embedding is utilized to fuse word-order information. Secondly, the ID-CNNs architecture is used to extract global semantic information rapidly. Simultaneously, the attention mechanism is employed to pay attention to the local context. Finally, we apply the CRF to obtain the optimal tag sequence. Experiments conducted on two CEMR datasets show that our model outperforms traditional ones. The F1-scores of 94.55% and 91.17% are obtained respectively on these two datasets, and both are better than LSTM-based models. View Full-Text
Keywords: clinical electronic records; named entity recognition; convolutional neural network clinical electronic records; named entity recognition; convolutional neural network
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Gao, M.; Xiao, Q.; Wu, S.; Deng, K. An Improved Method for Named Entity Recognition and Its Application to CEMR. Future Internet 2019, 11, 185.

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