[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]
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基于ALBERT与BILSTM的糖尿病命名实体识别()
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《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.

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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