Drug Specification Named Entity Recognition base on BiLSTM-CRF Model

被引：9

作者：

Li, Wei-Yan ^{[1
]}

Song, Wen-Ai ^{[1
]}

Jia, Xin-Hong ^{[1
]}

Yang, Ji-Jiang ^{[2
]}

Wang, Qing ^{[2
]}

Lei, Yi ^{[3
]}

Huang, Ke ^{[4
,5
,6
]}

Li, Jun ^{[4
,5
,6
]}

Yang, Ting ^{[4
,5
,6
]}

机构：

[1] North Univ China, Software Sch, Taiyuan, Shanxi, Peoples R China

[2] Tsinghua Univ, Res Inst Informat Technol, Beijing, Peoples R China

[3] Beijing Dfus Co Ltd, Beijing, Peoples R China

[4] China Japan Friendship Hosp, Dept Pulm & Crit Care Med, Beijing, Peoples R China

[5] Natl Clin Res Ctr Resp Dis, Beijing, Peoples R China

[6] Chinese Acad Med Sci, Inst Resp Med, Beijing, Peoples R China

来源：

2019 IEEE 43RD ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC), VOL 2 | 2019年

关键词：

BiLSTM; CRF; Named Entity Recognition; Deep Learning; Drug Specification;

D O I：

10.1109/COMPSAC.2019.10244

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In order to realize automatic recognition and extraction of entities in unstructured medical texts, a model combining language model conditional random field algorithm (CRF) and Bi-directional Long Short-term Memory networks (BiLSTM) is proposed. We crawled 804 drug specifications for treating asthma from the Internet, and then quantized the normalized field of drug specification word by a vector as the input of the neural network. Compared with the traditional machine learning algorithm CRF model, the system accuracy, recall and F1 value are improved by 6.18%, 5.2% and 4.87%. This model is applicable to extract named entity information from drug specification.

引用

页码：429 / 433

页数：5

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