Chinese clinical named entity recognition with radical-level feature and self-attention mechanism.
Named entity recognition is a fundamental and crucial task in medical natural language processing problems. In medical fields, Chinese clinical named entity recognition identifies boundaries and types of medical entities from unstructured text such as electronic medical records. Recently, a composit...
| Publicado en: | Journal of Biomedical Informatics Vol. 98 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Oct2019
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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=138988230&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138988230 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Oct2019 vid: 98 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 138988230 138988230 NLM31541715 138988230 10.1016/j.jbi.2019.103289 NLM31541715 138988230 ppct: 1 formats: tig: atl: Chinese clinical named entity recognition with radical-level feature and self-attention mechanism. aug: au: Yin, Mingwang Mou, Chengjie Xiong, Kaineng Ren, Jiangtao affil: School of Data and Computer Science, Guangdong Province Key Lab of Computational Science, Sun Yat-Sen University, Guangzhou, Guangdong 510006, PR China sug: subj: Natural Language Processing China Text Messaging Algorithms Medical Informatics Methods Language Semantics Attention Human Information Science Validation Studies Comparative Studies Evaluation Research Multicenter Studies Arthritis Impact Measurement Scales Scales Short Portable Mental Status Questionnaire ab: Named entity recognition is a fundamental and crucial task in medical natural language processing problems. In medical fields, Chinese clinical named entity recognition identifies boundaries and types of medical entities from unstructured text such as electronic medical records. Recently, a composition model of bidirectional Long Short-term Memory Networks (BiLSTMs) and conditional random field (BiLSTM-CRF) based character-level semantics has achieved great success in Chinese clinical named entity recognition tasks. But this method can only capture contextual semantics between characters in sentences. However, Chinese characters are hieroglyphics, and deeper semantic information is hidden inside, the BiLSTM-CRF model failed to get this information. In addition, some of the entities in the sentence are dependent, but the Long Short-term Memory (LSTM) does not capture long-term dependencies perfectly between characters. So we propose a BiLSTM-CRF model based on the radical-level feature and self-attention mechanism to solve these problems. We use the convolutional neural network (CNN) to extract radical-level features, aims to capture the intrinsic and internal relevances of characters. In addition, we use self-attention mechanism to capture the dependency between characters regardless of their distance. Experiments show that our model achieves F1-score 93.00% and 86.34% on CCKS-2017 and TP_CNER dataset respectively. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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