[1]马诗语,黄润才.基于ALBERT与BILSTM的糖尿病命名实体识别[J].中国医学物理学杂志,2021,38(11):1438-1443.[doi:DOI:10.3969/j.issn.1005-202X.2021.11.021]
　MA Shiyu,HUANG Runcai.Named entity recognition of diabetes based on ALBERT and BILSTM[J].Chinese Journal of Medical Physics,2021,38(11):1438-1443.[doi:DOI:10.3969/j.issn.1005-202X.2021.11.021]
点击复制

基于ALBERT与BILSTM的糖尿病命名实体识别()
分享到：

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第11期
页码:
1438-1443
栏目:
其他（激光医学等）
出版日期:
2021-11-26

文章信息/Info

Title:
Named entity recognition of diabetes based on ALBERT and BILSTM
文章编号:
1005-202X（2021）11-1438-06
作者:
马诗语; 黄润才
上海工程技术大学电子电气工程学院， 上海 201620
Author(s):
MA Shiyu;  HUANG Runcai
School of Electrical and Electronic Engineering, Shanghai University of Engineering and Technology, Shanghai 201620, China
关键词:
糖尿病; 命名实体识别; 轻量型动态词向量模型; 双向长短记忆网络; 条件随机场
Keywords:
Keywords: diabetes named entity recognition a lite BERT bidirectional long short-term memory network conditional random field
分类号:
TP183；TP391；R319
DOI:
DOI:10.3969/j.issn.1005-202X.2021.11.021
文献标志码:
A
摘要:
糖尿病命名实体识别技术能够从糖尿病文献中识别出关键信息，为糖尿病的诊断和治疗工作提供帮助。为此，本研究提出一种基于轻量型动态词向量模型（ALBERT）与双向长短记忆神经网络的命名实体识别方法，该方法旨在解决BERT语义单一、词汇量有限的问题。除此之外，还针对动态词向量训练耗时长、资源成本高的缺点进行了改进。本实验在糖尿病数据集上展开，并与现有主流模型进行对比。结果表明，融合ALBERT的实体识别效果均高于现有主流模型，且ALBERT较BERT训练速度有所提升。
Abstract:
Named entity recognition of diabetes is able to identify useful key information from the literatures related to diabetes, which is helpful for the diagnosis and treatment of diabetes. A named entity recognition method based on a lite BERT (ALBERT) and bidirectional long short-term memory network is proposed for solving the problems of BERT such as single semantics and limited vocabulary. In addition, the shortcomings of time consuming and high resource cost of dynamic word vector training are also improved. The experiment is carried out on the diabetes data set and then compared with the existing mainstream models. The results show that the entity recognition effect of the model with ALBERT is higher than that of the existing mainstream models, and that the training speed of ALBERT is faster than that of BERT.

相似文献/References:

[1]雍军光,阮 萍,沈洪涛,等.新型显微成像与分析系统在糖尿病患者红细胞定量研究中的应用[J].中国医学物理学杂志,2014,31(04):5081.[doi:10.3969/j.issn.1005-202X.2014.04.023]
[2]赵承奇,雷涛,罗二平,等.脉冲电磁场对糖尿病大鼠外周神经病变的影响[J].中国医学物理学杂志,2013,30(05):4431.[doi:10.3969/j.issn.1005-202X.2013.05.021]
[3]苑旺,梁媛媛,崔黎丽,等.正极性聚丙烯驻极体对糖尿病大鼠皮肤结构的影响[J].中国医学物理学杂志,2015,32(06):835.[doi:doi:10.3969/j.issn.1005-202X.2015.06.016]
　[J].Chinese Journal of Medical Physics,2015,32(11):835.[doi:doi:10.3969/j.issn.1005-202X.2015.06.016]
[4]余丽玲,陈婷,金浩宇,等.基于支持向量机和自回归积分滑动平均模型组合的血糖值预测[J].中国医学物理学杂志,2016,33(4):381.[doi:10.3969/j.issn.1005-202X.2016.04.012]
　[J].Chinese Journal of Medical Physics,2016,33(11):381.[doi:10.3969/j.issn.1005-202X.2016.04.012]
[5]花琦琦,刘新凤,焦青,等.基于SQL Server的糖尿病信息管理与分析系统[J].中国医学物理学杂志,2016,33(11):1183.[doi:10.3969/j.issn.1005-202X.2016.11.020]
　[J].Chinese Journal of Medical Physics,2016,33(11):1183.[doi:10.3969/j.issn.1005-202X.2016.11.020]
[6]楚轶,冯品,张薇,等. 稳恒磁场刺激对糖尿病动脉粥样硬化大鼠血清和主动脉中VEGF、TGF-β1、TNF-α和IL-6表达的影响[J].中国医学物理学杂志,2017,34(10):1045.[doi:DOI:10.3969/j.issn.1005-202X.2017.10.016]
　[J].Chinese Journal of Medical Physics,2017,34(11):1045.[doi:DOI:10.3969/j.issn.1005-202X.2017.10.016]
[7]郭一冰,崔栋,薛雅卓,等. 糖尿病行为量表计算工具箱的研制[J].中国医学物理学杂志,2018,35(2):195.[doi:DOI:10.3969/j.issn.1005-202X.2018.02.015]
　GUO Yibing,CUI Dong,XUE Yazhuo,et al. Development of diabetes behavioral scale calculation toolbox[J].Chinese Journal of Medical Physics,2018,35(11):195.[doi:DOI:10.3969/j.issn.1005-202X.2018.02.015]
[8]张嘉阳,黄河,刘子怡,等. 基于Gabor滤波器的糖尿病视网膜新生血管检测[J].中国医学物理学杂志,2018,35(8):968.[doi:DOI:10.3969/j.issn.1005-202X.2018.08.019]
　ZHANG Jiayang,HUANG He,LIU Ziyi,et al. Gabor filter-based detection of neovascularization due to diabetic retinopathy[J].Chinese Journal of Medical Physics,2018,35(11):968.[doi:DOI:10.3969/j.issn.1005-202X.2018.08.019]
[9]陈真诚,杜莹,邹春林,等. 基于K-Nearest Neighbor和神经网络的糖尿病分类研究[J].中国医学物理学杂志,2018,35(10):1220.[doi:DOI:10.3969/j.issn.1005-202X.2018.010.022]
　CHEN Zhencheng,DU Ying,ZOU Chunlin,et al. Classification of diabetes based on K-Nearest Neighbor and neural network[J].Chinese Journal of Medical Physics,2018,35(11):1220.[doi:DOI:10.3969/j.issn.1005-202X.2018.010.022]
[10]张迪,陈真诚,梁永波,等. 协同训练算法在无创血糖检测中的应用[J].中国医学物理学杂志,2018,35(11):1295.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.011]
　ZHANG Di,CHEN Zhencheng,LIANG Yongbo,et al. Application of co-training algorithm in noninvasive blood glucose detection[J].Chinese Journal of Medical Physics,2018,35(11):1295.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.011]
[11]任建华,赵若涵.基于特征融合的糖尿病命名实体识别[J].中国医学物理学杂志,2024,41(7):890.[doi:DOI:10.3969/j.issn.1005-202X.2024.07.016]
　REN Jianhua,ZHAO Ruohan.Diabetes named entity recognition based on feature fusion[J].Chinese Journal of Medical Physics,2024,41(11):890.[doi:DOI:10.3969/j.issn.1005-202X.2024.07.016]

备注/Memo

备注/Memo:
【收稿日期】2021-07-05 【作者简介】马诗语，硕士，主要研究方向为自然语言处理，E-mail: 781052830@qq.com 【通信作者】黄润才，博士，副教授，主要研究方向为计算机网络与信息系统、人工智能与大数据，E-mail: hrc@sues.edu.com
更新日期/Last Update: 2021-11-27