A semi-supervised approach for extracting TCM clinical terms based on feature words

被引：15

作者：

Liu, Liangliang ^{[1
]}

Wu, Xiaojing ^{[1
]}

Liu, Hui ^{[1
]}

Cao, Xinyu ^{[2
]}

Wang, Haitao ^{[2
]}

Zhou, Hongwei ^{[3
]}

Xie, Qi ^{[4
]}

机构：

[1] Shanghai Univ Int Business & Econ, Sch Stat & Informat, Shanghai 201620, Peoples R China

[2] China Natl Inst Standardizat, Beijing, Peoples R China

[3] China Acad Chinese Med Sci, Inst Basic Res Clin Med, Beijing 100700, Peoples R China

[4] China Acad Chinese Med Sci, Dept Acad Management, Beijing 100700, Peoples R China

来源：

BMC MEDICAL INFORMATICS AND DECISION MAKING | 2020年 / 20卷 / Suppl 3期

关键词：

TCM; NER; Clinical terms; Deep learning; Semi-supervised; NAMED ENTITY RECOGNITION;

D O I：

10.1186/s12911-020-1108-1

中图分类号：

R-058 [];

学科分类号：

摘要：

BackgroundA semi-supervised model is proposed for extracting clinical terms of Traditional Chinese Medicine using feature words.MethodsThe extraction model is based on BiLSTM-CRF and combined with semi-supervised learning and feature word set, which reduces the cost of manual annotation and leverage extraction results.ResultsExperiment results show that the proposed model improves the extraction of five types of TCM clinical terms, including traditional Chinese medicine, symptoms, patterns, diseases and formulas. The best F1-value of the experiment reaches 78.70% on the test dataset.ConclusionsThis method can reduce the cost of manual labeling and improve the result in the NER research of TCM clinical terms.

引用

页数：7

共 32 条

[1]

Al-Hegami AS, 2017, INT J COMPUT SCI NET, V17, P170

[2]

[Anonymous], 2016, Character-aware neural language models

[3]

[Anonymous], 2012, SUPERVISED SEQUENCE

[4]

Chalapathy R., 2016, CLIN NAT LANG PROC W

[5]

A study of active learning methods for named entity recognition in clinical text [J].

Chen, Yukun ;

Lasko, Thomas A. ;

Mei, Qiaozhu ;

Denny, Joshua C. ;

Xu, Hua .

JOURNAL OF BIOMEDICAL INFORMATICS, 2015, 58 :11-18

[6]

Collobert R, 2011, J MACH LEARN RES, V12, P2493

[7]

Finkel J.R., 2005, INCORPORATING NONLOC

[8]

Frantzi KT, 1998, C VALUE NC VALUE MET

[9]

Gridach M, 2015, J BIOMED INFORM, V33, pi37

[10]

Deep learning with word embeddings improves biomedical named entity recognition [J].

Habibi, Maryam ;

Weber, Leon ;

Neves, Mariana ;

Wiegandt, David Luis ;

Leser, Ulf .

BIOINFORMATICS, 2017, 33 (14) :I37-I48

← 1 2 3 4 →