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        <title>SRCB at #SMM4H 2024: Making Full Use of LLM-based Data Augmentation in Adverse Drug Event Extraction and Normalization</title>
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        <namePart type="given">Hongyu</namePart>
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    <name type="personal">
        <namePart type="given">Shanshan</namePart>
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    <name type="personal">
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    <abstract>This paper reports on the performance of SRCB’s system in the Social Media Mining for Health (#SMM4H) 2024 Shared Task 1: extraction and normalization of adverse drug events (ADEs) in English tweets. We develop a system composed of an ADE extraction module and an ADE normalization module which furtherly includes a retrieval module and a filtering module. To alleviate the data imbalance and other issues introduced by the dataset, we employ 4 data augmentation techniques based on Large Language Models (LLMs) across both modules. Our best submission achieves an F1 score of 53.6 (49.4 on the unseen subset) on the ADE normalization task and an F1 score of 52.1 on ADE extraction task.</abstract>
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%0 Conference Proceedings
%T SRCB at #SMM4H 2024: Making Full Use of LLM-based Data Augmentation in Adverse Drug Event Extraction and Normalization
%A Li, Hongyu
%A Zhang, Yuming
%A Zhang, Yongwei
%A Jiang, Shanshan
%A Dong, Bin
%Y Xu, Dongfang
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F li-etal-2024-srcb
%X This paper reports on the performance of SRCB’s system in the Social Media Mining for Health (#SMM4H) 2024 Shared Task 1: extraction and normalization of adverse drug events (ADEs) in English tweets. We develop a system composed of an ADE extraction module and an ADE normalization module which furtherly includes a retrieval module and a filtering module. To alleviate the data imbalance and other issues introduced by the dataset, we employ 4 data augmentation techniques based on Large Language Models (LLMs) across both modules. Our best submission achieves an F1 score of 53.6 (49.4 on the unseen subset) on the ADE normalization task and an F1 score of 52.1 on ADE extraction task.
%U https://aclanthology.org/2024.smm4h-1.8
%P 32-37

Markdown (Informal)

[SRCB at #SMM4H 2024: Making Full Use of LLM-based Data Augmentation in Adverse Drug Event Extraction and Normalization](https://aclanthology.org/2024.smm4h-1.8) (Li et al., SMM4H-WS 2024)

• SRCB at #SMM4H 2024: Making Full Use of LLM-based Data Augmentation in Adverse Drug Event Extraction and Normalization (Li et al., SMM4H-WS 2024)

ACL

• Hongyu Li, Yuming Zhang, Yongwei Zhang, Shanshan Jiang, and Bin Dong. 2024. SRCB at #SMM4H 2024: Making Full Use of LLM-based Data Augmentation in Adverse Drug Event Extraction and Normalization. In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, pages 32–37, Bangkok, Thailand. Association for Computational Linguistics.