Chinese clinical named entity recognition via multi-head self-attention based BiLSTM-CRF.
Clinical named entity recognition (CNER) is a fundamental step for many clinical Natural Language Processing (NLP) systems, which aims to recognize and classify clinical entities such as diseases, symptoms, exams, body parts and treatments in clinical free texts. In recent years, with the developmen...
| Publicado en: | Artificial Intelligence in Medicine Vol. 127 |
|---|---|
| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
May2022
|
| 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=156286411&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156286411 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: May2022 vid: 127 pid: 1004 pub: Elsevier B.V. artinfo: ui: 156286411 156286411 NLM35430042 156286411 10.1016/j.artmed.2022.102282 NLM35430042 156286411 ppct: 1 formats: tig: atl: Chinese clinical named entity recognition via multi-head self-attention based BiLSTM-CRF. aug: au: An, Ying Xia, Xianyun Chen, Xianlai Wu, Fang-Xiang Wang, Jianxin affil: Institute of Big Data, Central South University, Changsha 410083, PR China sug: subj: Natural Language Processing China Language Short Portable Mental Status Questionnaire Scales Arthritis Impact Measurement Scales Barthel Index ab: Clinical named entity recognition (CNER) is a fundamental step for many clinical Natural Language Processing (NLP) systems, which aims to recognize and classify clinical entities such as diseases, symptoms, exams, body parts and treatments in clinical free texts. In recent years, with the development of deep learning technology, deep neural networks (DNNs) have been widely used in Chinese clinical named entity recognition and many other clinical NLP tasks. However, these state-of-the-art models failed to make full use of the global information and multi-level semantic features in clinical texts. We design an improved character-level representation approach which integrates the character embedding and the character-label embedding to enhance the specificity and diversity of feature representations. Then, a multi-head self-attention based Bi-directional Long Short-Term Memory Conditional Random Field (MUSA-BiLSTM-CRF) model is proposed. By introducing the multi-head self-attention and combining a medical dictionary, the model can more effectively capture the weight relationships between characters and multi-level semantic feature information, which is expected to greatly improve the performance of Chinese clinical named entity recognition. We evaluate our model on two CCKS challenge (CCKS2017 Task 2 and CCKS2018 Task 1) benchmark datasets and the experimental results show that our proposed model achieves the best performance competing with the state-of-the-art DNN based methods. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|