S3AAL: Support Set Selection based on Adversarial Active Learning for Medical Few-Shot Relation Extraction

Qingyao Li; Hui Xu; Hui Wang; Buzhou Tang

doi:10.1109/BIBM55620.2022.9995320

S³AAL: Support Set Selection based on Adversarial Active Learning for Medical Few-Shot Relation Extraction

Qingyao Li
, Hui Xu
, Hui Wang
, Buzhou Tang^*

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen
Gennlife (Beijing) Technology Co Ltd
Peng Cheng Laboratory

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Support set is one of the most important components of Few-Shot Learning (FSL) methods that greatly affects the performance of these methods. Most existing studies mainly focus on how to effectively utilize the support set sampled randomly, but ignoring the representative of the support set, leading to that the performance of the few-shot learning methods using different support sets randomly sampled varies greatly. In this paper, we focus on how to select a representative support set for FSL methods for medical few-shot relation extraction (FSRE), and propose a novel approach for Support Set Selection based on Adversarial Active Learning (text{S}-{3} AAL). The adversarial active learning does not only keeps the features shared by source and target, but also guarantees the diversity of the support set. We create three benchmark datasets for medical FSRE based on four public medical RE datasets. The experimental results on the three benchmark datasets demonstrate the effectiveness of our approach when it is plugged into state-of-the-art (SOTA) few-shot learning methods.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Editors	Donald Adjeroh, Qi Long, Xinghua Shi, Fei Guo, Xiaohua Hu, Srinivas Aluru, Giri Narasimhan, Jianxin Wang, Mingon Kang, Ananda M. Mondal, Jin Liu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	777-780
Number of pages	4
ISBN (Electronic)	9781665468190
DOIs	https://doi.org/10.1109/BIBM55620.2022.9995320
State	Published - 2022
Externally published	Yes
Event	2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 - Las Vegas, United States Duration: 6 Dec 2022 → 8 Dec 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

Conference

Conference	2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Country/Territory	United States
City	Las Vegas
Period	6/12/22 → 8/12/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

active learning
adversarial learning
few-shot learning
medical relation extraction

Access to Document

10.1109/BIBM55620.2022.9995320

Cite this

Li, Q., Xu, H., Wang, H., & Tang, B. (2022). S³AAL: Support Set Selection based on Adversarial Active Learning for Medical Few-Shot Relation Extraction. In D. Adjeroh, Q. Long, X. Shi, F. Guo, X. Hu, S. Aluru, G. Narasimhan, J. Wang, M. Kang, A. M. Mondal, & J. Liu (Eds.), Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 (pp. 777-780). (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM55620.2022.9995320

Li, Qingyao ; Xu, Hui ; Wang, Hui et al. / S³AAL : Support Set Selection based on Adversarial Active Learning for Medical Few-Shot Relation Extraction. Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. editor / Donald Adjeroh ; Qi Long ; Xinghua Shi ; Fei Guo ; Xiaohua Hu ; Srinivas Aluru ; Giri Narasimhan ; Jianxin Wang ; Mingon Kang ; Ananda M. Mondal ; Jin Liu. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 777-780 (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022).

@inproceedings{2cf007d8a2264835804750305117a021,

title = "S3AAL: Support Set Selection based on Adversarial Active Learning for Medical Few-Shot Relation Extraction",

abstract = "Support set is one of the most important components of Few-Shot Learning (FSL) methods that greatly affects the performance of these methods. Most existing studies mainly focus on how to effectively utilize the support set sampled randomly, but ignoring the representative of the support set, leading to that the performance of the few-shot learning methods using different support sets randomly sampled varies greatly. In this paper, we focus on how to select a representative support set for FSL methods for medical few-shot relation extraction (FSRE), and propose a novel approach for Support Set Selection based on Adversarial Active Learning (text\{S\}-\{3\} AAL). The adversarial active learning does not only keeps the features shared by source and target, but also guarantees the diversity of the support set. We create three benchmark datasets for medical FSRE based on four public medical RE datasets. The experimental results on the three benchmark datasets demonstrate the effectiveness of our approach when it is plugged into state-of-the-art (SOTA) few-shot learning methods.",

keywords = "active learning, adversarial learning, few-shot learning, medical relation extraction",

author = "Qingyao Li and Hui Xu and Hui Wang and Buzhou Tang",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 ; Conference date: 06-12-2022 Through 08-12-2022",

year = "2022",

doi = "10.1109/BIBM55620.2022.9995320",

language = "英语",

series = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "777--780",

editor = "Donald Adjeroh and Qi Long and Xinghua Shi and Fei Guo and Xiaohua Hu and Srinivas Aluru and Giri Narasimhan and Jianxin Wang and Mingon Kang and Mondal, \{Ananda M.\} and Jin Liu",

booktitle = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",

address = "美国",

}

Li, Q, Xu, H, Wang, H & Tang, B 2022, S³AAL: Support Set Selection based on Adversarial Active Learning for Medical Few-Shot Relation Extraction. in D Adjeroh, Q Long, X Shi, F Guo, X Hu, S Aluru, G Narasimhan, J Wang, M Kang, AM Mondal & J Liu (eds), Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, Institute of Electrical and Electronics Engineers Inc., pp. 777-780, 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, Las Vegas, United States, 6/12/22. https://doi.org/10.1109/BIBM55620.2022.9995320

S³AAL: Support Set Selection based on Adversarial Active Learning for Medical Few-Shot Relation Extraction. / Li, Qingyao; Xu, Hui; Wang, Hui et al.
Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. ed. / Donald Adjeroh; Qi Long; Xinghua Shi; Fei Guo; Xiaohua Hu; Srinivas Aluru; Giri Narasimhan; Jianxin Wang; Mingon Kang; Ananda M. Mondal; Jin Liu. Institute of Electrical and Electronics Engineers Inc., 2022. p. 777-780 (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - S3AAL

T2 - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

AU - Li, Qingyao

AU - Xu, Hui

AU - Wang, Hui

AU - Tang, Buzhou

PY - 2022

Y1 - 2022

N2 - Support set is one of the most important components of Few-Shot Learning (FSL) methods that greatly affects the performance of these methods. Most existing studies mainly focus on how to effectively utilize the support set sampled randomly, but ignoring the representative of the support set, leading to that the performance of the few-shot learning methods using different support sets randomly sampled varies greatly. In this paper, we focus on how to select a representative support set for FSL methods for medical few-shot relation extraction (FSRE), and propose a novel approach for Support Set Selection based on Adversarial Active Learning (text{S}-{3} AAL). The adversarial active learning does not only keeps the features shared by source and target, but also guarantees the diversity of the support set. We create three benchmark datasets for medical FSRE based on four public medical RE datasets. The experimental results on the three benchmark datasets demonstrate the effectiveness of our approach when it is plugged into state-of-the-art (SOTA) few-shot learning methods.

AB - Support set is one of the most important components of Few-Shot Learning (FSL) methods that greatly affects the performance of these methods. Most existing studies mainly focus on how to effectively utilize the support set sampled randomly, but ignoring the representative of the support set, leading to that the performance of the few-shot learning methods using different support sets randomly sampled varies greatly. In this paper, we focus on how to select a representative support set for FSL methods for medical few-shot relation extraction (FSRE), and propose a novel approach for Support Set Selection based on Adversarial Active Learning (text{S}-{3} AAL). The adversarial active learning does not only keeps the features shared by source and target, but also guarantees the diversity of the support set. We create three benchmark datasets for medical FSRE based on four public medical RE datasets. The experimental results on the three benchmark datasets demonstrate the effectiveness of our approach when it is plugged into state-of-the-art (SOTA) few-shot learning methods.

KW - active learning

KW - adversarial learning

KW - few-shot learning

KW - medical relation extraction

UR - https://www.scopus.com/pages/publications/85146667348

U2 - 10.1109/BIBM55620.2022.9995320

DO - 10.1109/BIBM55620.2022.9995320

M3 - 会议稿件

AN - SCOPUS:85146667348

T3 - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

SP - 777

EP - 780

BT - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

A2 - Adjeroh, Donald

A2 - Long, Qi

A2 - Shi, Xinghua

A2 - Guo, Fei

A2 - Hu, Xiaohua

A2 - Aluru, Srinivas

A2 - Narasimhan, Giri

A2 - Wang, Jianxin

A2 - Kang, Mingon

A2 - Mondal, Ananda M.

A2 - Liu, Jin

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 6 December 2022 through 8 December 2022

ER -

Li Q, Xu H, Wang H, Tang B. S³AAL: Support Set Selection based on Adversarial Active Learning for Medical Few-Shot Relation Extraction. In Adjeroh D, Long Q, Shi X, Guo F, Hu X, Aluru S, Narasimhan G, Wang J, Kang M, Mondal AM, Liu J, editors, Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 777-780. (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022). doi: 10.1109/BIBM55620.2022.9995320