Cross-Lingual Name Entity Recognition from Clinical Text Using Mixed Language Query

Kunli Shi; Gongchi Chen; Jinghang Gu; Longhua Qian; Guodong Zhou

doi:10.1007/978-981-99-9864-7_1

Cross-Lingual Name Entity Recognition from Clinical Text Using Mixed Language Query

Kunli Shi, Gongchi Chen, Jinghang Gu, Longhua Qian (Corresponding Author), Guodong Zhou

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

Cross-lingual Named Entity Recognition (Cross-Lingual NER) addresses the challenge of NER with limited annotated data in low-resource languages by transferring knowledge from high-resource languages. Particularly, in the clinical domain, the lack of annotated corpora for Cross-Lingual NER hinders the development of cross-lingual clinical text named entity recognition. By leveraging the English clinical text corpus I2B2 2010 and the Chinese clinical text corpus CCKS2019, we construct a cross-lingual clinical text named entity recognition corpus (CLC-NER) via label alignment. Further, we propose a machine reading comprehension framework for Cross-Lingual NER using mixed language queries to enhance model transfer capabilities. We conduct comprehensive experiments on the CLC-NER corpus, and the results demonstrate the superiority of our approach over other systems.

Original language	English
Title of host publication	Health Information Processing
Subtitle of host publication	9th China Health Information Processing Conference, CHIP 2023, Proceedings
Editors	Hua Xu, Qingcai Chen, Hongfei Lin, Fei Wu, Lei Liu, Buzhou Tang, Tianyong Hao, Zhengxing Huang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	3-21
Number of pages	19
ISBN (Print)	9789819998630
DOIs	https://doi.org/10.1007/978-981-99-9864-7_1
Publication status	Published - 1 Feb 2024
Event	9th China Health Information Processing Conference, CHIP 2023 - Hangzhou, China Duration: 27 Oct 2023 → 29 Oct 2023

Publication series

Name	Communications in Computer and Information Science
Volume	1993 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	9th China Health Information Processing Conference, CHIP 2023
Country/Territory	China
City	Hangzhou
Period	27/10/23 → 29/10/23

Keywords

Clinical Text
Cross-Lingual NER
Machine Reading Comprehension
Mixed Language Query

ASJC Scopus subject areas

General Computer Science
General Mathematics

More information

10.1007/978-981-99-9864-7_1

http://hdl.handle.net/10397/107981

Cite this

Shi, K., Chen, G., Gu, J., Qian, L., & Zhou, G. (2024). Cross-Lingual Name Entity Recognition from Clinical Text Using Mixed Language Query. In H. Xu, Q. Chen, H. Lin, F. Wu, L. Liu, B. Tang, T. Hao, & Z. Huang (Eds.), Health Information Processing: 9th China Health Information Processing Conference, CHIP 2023, Proceedings (pp. 3-21). (Communications in Computer and Information Science; Vol. 1993 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-9864-7_1

Shi, Kunli ; Chen, Gongchi ; Gu, Jinghang et al. / Cross-Lingual Name Entity Recognition from Clinical Text Using Mixed Language Query. Health Information Processing: 9th China Health Information Processing Conference, CHIP 2023, Proceedings. editor / Hua Xu ; Qingcai Chen ; Hongfei Lin ; Fei Wu ; Lei Liu ; Buzhou Tang ; Tianyong Hao ; Zhengxing Huang. Springer Science and Business Media Deutschland GmbH, 2024. pp. 3-21 (Communications in Computer and Information Science).

@inproceedings{d6e2fc65ed0a47b3bfcd220190af8196,

title = "Cross-Lingual Name Entity Recognition from Clinical Text Using Mixed Language Query",

abstract = "Cross-lingual Named Entity Recognition (Cross-Lingual NER) addresses the challenge of NER with limited annotated data in low-resource languages by transferring knowledge from high-resource languages. Particularly, in the clinical domain, the lack of annotated corpora for Cross-Lingual NER hinders the development of cross-lingual clinical text named entity recognition. By leveraging the English clinical text corpus I2B2 2010 and the Chinese clinical text corpus CCKS2019, we construct a cross-lingual clinical text named entity recognition corpus (CLC-NER) via label alignment. Further, we propose a machine reading comprehension framework for Cross-Lingual NER using mixed language queries to enhance model transfer capabilities. We conduct comprehensive experiments on the CLC-NER corpus, and the results demonstrate the superiority of our approach over other systems.",

keywords = "Clinical Text, Cross-Lingual NER, Machine Reading Comprehension, Mixed Language Query",

author = "Kunli Shi and Gongchi Chen and Jinghang Gu and Longhua Qian and Guodong Zhou",

note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 9th China Health Information Processing Conference, CHIP 2023 ; Conference date: 27-10-2023 Through 29-10-2023",

year = "2024",

month = feb,

day = "1",

doi = "10.1007/978-981-99-9864-7_1",

language = "English",

isbn = "9789819998630",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "3--21",

editor = "Hua Xu and Qingcai Chen and Hongfei Lin and Fei Wu and Lei Liu and Buzhou Tang and Tianyong Hao and Zhengxing Huang",

booktitle = "Health Information Processing",

address = "Germany",

}

Shi, K, Chen, G, Gu, J, Qian, L & Zhou, G 2024, Cross-Lingual Name Entity Recognition from Clinical Text Using Mixed Language Query. in H Xu, Q Chen, H Lin, F Wu, L Liu, B Tang, T Hao & Z Huang (eds), Health Information Processing: 9th China Health Information Processing Conference, CHIP 2023, Proceedings. Communications in Computer and Information Science, vol. 1993 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 3-21, 9th China Health Information Processing Conference, CHIP 2023, Hangzhou, China, 27/10/23. https://doi.org/10.1007/978-981-99-9864-7_1

Cross-Lingual Name Entity Recognition from Clinical Text Using Mixed Language Query. / Shi, Kunli; Chen, Gongchi; Gu, Jinghang et al.
Health Information Processing: 9th China Health Information Processing Conference, CHIP 2023, Proceedings. ed. / Hua Xu; Qingcai Chen; Hongfei Lin; Fei Wu; Lei Liu; Buzhou Tang; Tianyong Hao; Zhengxing Huang. Springer Science and Business Media Deutschland GmbH, 2024. p. 3-21 (Communications in Computer and Information Science; Vol. 1993 CCIS).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Cross-Lingual Name Entity Recognition from Clinical Text Using Mixed Language Query

AU - Shi, Kunli

AU - Chen, Gongchi

AU - Gu, Jinghang

AU - Qian, Longhua

AU - Zhou, Guodong

PY - 2024/2/1

Y1 - 2024/2/1

N2 - Cross-lingual Named Entity Recognition (Cross-Lingual NER) addresses the challenge of NER with limited annotated data in low-resource languages by transferring knowledge from high-resource languages. Particularly, in the clinical domain, the lack of annotated corpora for Cross-Lingual NER hinders the development of cross-lingual clinical text named entity recognition. By leveraging the English clinical text corpus I2B2 2010 and the Chinese clinical text corpus CCKS2019, we construct a cross-lingual clinical text named entity recognition corpus (CLC-NER) via label alignment. Further, we propose a machine reading comprehension framework for Cross-Lingual NER using mixed language queries to enhance model transfer capabilities. We conduct comprehensive experiments on the CLC-NER corpus, and the results demonstrate the superiority of our approach over other systems.

AB - Cross-lingual Named Entity Recognition (Cross-Lingual NER) addresses the challenge of NER with limited annotated data in low-resource languages by transferring knowledge from high-resource languages. Particularly, in the clinical domain, the lack of annotated corpora for Cross-Lingual NER hinders the development of cross-lingual clinical text named entity recognition. By leveraging the English clinical text corpus I2B2 2010 and the Chinese clinical text corpus CCKS2019, we construct a cross-lingual clinical text named entity recognition corpus (CLC-NER) via label alignment. Further, we propose a machine reading comprehension framework for Cross-Lingual NER using mixed language queries to enhance model transfer capabilities. We conduct comprehensive experiments on the CLC-NER corpus, and the results demonstrate the superiority of our approach over other systems.

KW - Clinical Text

KW - Cross-Lingual NER

KW - Machine Reading Comprehension

KW - Mixed Language Query

UR - http://www.scopus.com/inward/record.url?scp=85186660504&partnerID=8YFLogxK

U2 - 10.1007/978-981-99-9864-7_1

DO - 10.1007/978-981-99-9864-7_1

M3 - Conference article published in proceeding or book

AN - SCOPUS:85186660504

SN - 9789819998630

T3 - Communications in Computer and Information Science

SP - 3

EP - 21

BT - Health Information Processing

A2 - Xu, Hua

A2 - Chen, Qingcai

A2 - Lin, Hongfei

A2 - Wu, Fei

A2 - Liu, Lei

A2 - Tang, Buzhou

A2 - Hao, Tianyong

A2 - Huang, Zhengxing

PB - Springer Science and Business Media Deutschland GmbH

T2 - 9th China Health Information Processing Conference, CHIP 2023

Y2 - 27 October 2023 through 29 October 2023

ER -

Shi K, Chen G, Gu J, Qian L, Zhou G. Cross-Lingual Name Entity Recognition from Clinical Text Using Mixed Language Query. In Xu H, Chen Q, Lin H, Wu F, Liu L, Tang B, Hao T, Huang Z, editors, Health Information Processing: 9th China Health Information Processing Conference, CHIP 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 3-21. (Communications in Computer and Information Science). doi: 10.1007/978-981-99-9864-7_1

Cross-Lingual Name Entity Recognition from Clinical Text Using Mixed Language Query

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

More information

Other files and links

Fingerprint

Cite this