Study of Named Entity Recognition methods in biomedical field

被引：6

作者：

Sniegula, Anna ^{[1
,2
]}

Poniszewska-Maranda, Aneta ^{[1
]}

Chomatek, Lukasz ^{[1
]}

机构：

[1] Lodz Univ Technol, Inst Informat Technol, Lodz, Poland

[2] Univ Lodz, Fac Econ & Sociol, Inst Appl Econ & Infromat, Dept Comp Sci Econ, Lodz, Poland

来源：

10TH INT CONF ON EMERGING UBIQUITOUS SYST AND PERVAS NETWORKS (EUSPN-2019) / THE 9TH INT CONF ON CURRENT AND FUTURE TRENDS OF INFORMAT AND COMMUN TECHNOLOGIES IN HEALTHCARE (ICTH-2019) / AFFILIATED WORKOPS | 2019年 / 160卷

关键词：

Machine learning; Natural Language Processing; recurrent neural networks; Named Entity Recognition; Conditional Random Fields; UMLS; Long-Short Term Memory;

D O I：

10.1016/j.procs.2019.09.466

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Natural Language Processing (NLP) is very important in modern data processing taking into consideration different sources, forms and purpose of data as well as information in different areas our industry, administration, public and private life. Our studies concern Natural Language Processing techniques in biomedical field. The increasing volume of information stored in medical health record databases both in natural language and in structured forms is creating increasing challenges for information retrieval (IR) technologies. The paper presents the comparison study of chosen Named Entity Recognition techniques for biomedical field. (C) 2019 The Authors. Published by Elsevier B.V.

引用

页码：260 / 265

页数：6

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