Biomedical Causal Relation Extraction Incorporated with External Knowledge

Dongmei Li; Dongling Li; Jinghang Gu; Longhua Qian; Guodong Zhou

doi:10.1007/978-981-99-9864-7_8

Biomedical Causal Relation Extraction Incorporated with External Knowledge

Dongmei Li, Dongling Li, 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

Biomedical causal relation extraction is an important task. It aims to analyze biomedical texts and extract structured information such as named entities, semantic relations and function type. In recent years, some related works have largely improved the performance of biomedical causal relation extraction. However, they only focus on contextual information and ignore external knowledge. In view of this, we introduce entity information from external knowledge base as a prompt to enrich the input text, and propose a causal relation extraction framework JNT_KB incorporating entity information to support the underlying understanding for causal relation extraction. Experimental results show that JNT_KB consistently outperforms state-of-the-art extraction models, and the final extraction performance F1 score in Stage 2 is as high as 61.0%.

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	112-128
Number of pages	17
ISBN (Print)	9789819998630
DOIs	https://doi.org/10.1007/978-981-99-9864-7_8
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

BEL Statement
Causal Relation Extraction
Entity Information
External Knowledge

ASJC Scopus subject areas

General Computer Science
General Mathematics

More information

10.1007/978-981-99-9864-7_8

Cite this

Li, D., Li, D., Gu, J., Qian, L., & Zhou, G. (2024). Biomedical Causal Relation Extraction Incorporated with External Knowledge. 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. 112-128). (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_8

Li, Dongmei ; Li, Dongling ; Gu, Jinghang et al. / Biomedical Causal Relation Extraction Incorporated with External Knowledge. 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. 112-128 (Communications in Computer and Information Science).

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title = "Biomedical Causal Relation Extraction Incorporated with External Knowledge",

abstract = "Biomedical causal relation extraction is an important task. It aims to analyze biomedical texts and extract structured information such as named entities, semantic relations and function type. In recent years, some related works have largely improved the performance of biomedical causal relation extraction. However, they only focus on contextual information and ignore external knowledge. In view of this, we introduce entity information from external knowledge base as a prompt to enrich the input text, and propose a causal relation extraction framework JNT\_KB incorporating entity information to support the underlying understanding for causal relation extraction. Experimental results show that JNT\_KB consistently outperforms state-of-the-art extraction models, and the final extraction performance F1 score in Stage 2 is as high as 61.0\%.",

keywords = "BEL Statement, Causal Relation Extraction, Entity Information, External Knowledge",

author = "Dongmei Li and Dongling Li 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",

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doi = "10.1007/978-981-99-9864-7\_8",

language = "English",

isbn = "9789819998630",

series = "Communications in Computer and Information Science",

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

pages = "112--128",

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

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Li, D, Li, D, Gu, J, Qian, L & Zhou, G 2024, Biomedical Causal Relation Extraction Incorporated with External Knowledge. 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. 112-128, 9th China Health Information Processing Conference, CHIP 2023, Hangzhou, China, 27/10/23. https://doi.org/10.1007/978-981-99-9864-7_8

Biomedical Causal Relation Extraction Incorporated with External Knowledge. / Li, Dongmei; Li, Dongling; 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. 112-128 (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 - Biomedical Causal Relation Extraction Incorporated with External Knowledge

AU - Li, Dongmei

AU - Li, Dongling

AU - Gu, Jinghang

AU - Qian, Longhua

AU - Zhou, Guodong

PY - 2024/2/1

Y1 - 2024/2/1

N2 - Biomedical causal relation extraction is an important task. It aims to analyze biomedical texts and extract structured information such as named entities, semantic relations and function type. In recent years, some related works have largely improved the performance of biomedical causal relation extraction. However, they only focus on contextual information and ignore external knowledge. In view of this, we introduce entity information from external knowledge base as a prompt to enrich the input text, and propose a causal relation extraction framework JNT_KB incorporating entity information to support the underlying understanding for causal relation extraction. Experimental results show that JNT_KB consistently outperforms state-of-the-art extraction models, and the final extraction performance F1 score in Stage 2 is as high as 61.0%.

AB - Biomedical causal relation extraction is an important task. It aims to analyze biomedical texts and extract structured information such as named entities, semantic relations and function type. In recent years, some related works have largely improved the performance of biomedical causal relation extraction. However, they only focus on contextual information and ignore external knowledge. In view of this, we introduce entity information from external knowledge base as a prompt to enrich the input text, and propose a causal relation extraction framework JNT_KB incorporating entity information to support the underlying understanding for causal relation extraction. Experimental results show that JNT_KB consistently outperforms state-of-the-art extraction models, and the final extraction performance F1 score in Stage 2 is as high as 61.0%.

KW - BEL Statement

KW - Causal Relation Extraction

KW - Entity Information

KW - External Knowledge

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U2 - 10.1007/978-981-99-9864-7_8

DO - 10.1007/978-981-99-9864-7_8

M3 - Conference article published in proceeding or book

AN - SCOPUS:85186646480

SN - 9789819998630

T3 - Communications in Computer and Information Science

SP - 112

EP - 128

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 -

Li D, Li D, Gu J, Qian L, Zhou G. Biomedical Causal Relation Extraction Incorporated with External Knowledge. 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. 112-128. (Communications in Computer and Information Science). doi: 10.1007/978-981-99-9864-7_8

Biomedical Causal Relation Extraction Incorporated with External Knowledge

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