Adversarial training based lattice LSTM for Chinese clinical named entity recognition.
Clinical named entity recognition (CNER), which intends to automatically detect clinical entities in electronic health record (EHR), is a committed step for further clinical text mining. Recently, more and more deep learning models are used to Chinese CNER. However, these models do not make full use...
| Publicado en: | Journal of Biomedical Informatics Vol. 99 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Nov2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139507183&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139507183 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Nov2019 vid: 99 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 139507183 139507183 NLM31557528 139507183 10.1016/j.jbi.2019.103290 NLM31557528 139507183 ppct: 1 formats: tig: atl: Adversarial training based lattice LSTM for Chinese clinical named entity recognition. aug: au: Zhao, Shan Cai, Zhiping Chen, Haiwen Wang, Ye Liu, Fang Liu, Anfeng affil: College of Computer, National University of Defense Technology, Changsha, China sug: subj: Data Mining Methods Information Science China Language Cluster Analysis Medical Informatics Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales ab: Clinical named entity recognition (CNER), which intends to automatically detect clinical entities in electronic health record (EHR), is a committed step for further clinical text mining. Recently, more and more deep learning models are used to Chinese CNER. However, these models do not make full use of the information in EHR, for these models are either word-based or character-based. In addition, neural models tend to be locally unstable and even tiny perturbation may mislead them. In this paper, we firstly propose a novel adversarial training based lattice LSTM with a conditional random field layer (AT-lattice LSTM-CRF) for Chinese CNER. Lattice LSTM is used to capture richer information in EHR. As a powerful regularization method, AT can be used to improve the robustness of neural models by adding perturbations to the training data. Then, we conduct experiments on the proposed neural model with dataset of CCKS-2017 Task 2. The results show that the proposed model achieves a highly competitive performance (with an F1 score of 89.64%) compared to other prevalent neural models, which can be a reinforced baseline for further research in this field. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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