@inproceedings{raithel-etal-2024-dataset-pharmacovigilance,
title = "A Dataset for Pharmacovigilance in {G}erman, {F}rench, and {J}apanese: Annotating Adverse Drug Reactions across Languages",
author = {Raithel, Lisa and
Yeh, Hui-Syuan and
Yada, Shuntaro and
Grouin, Cyril and
Lavergne, Thomas and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Paroubek, Patrick and
Thomas, Philippe and
Nishiyama, Tomohiro and
M{\"o}ller, Sebastian and
Aramaki, Eiji and
Matsumoto, Yuji and
Roller, Roland and
Zweigenbaum, Pierre},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.36",
pages = "395--414",
abstract = "User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world. However, the existing clinical corpora predominantly revolve around scientific articles in English. This work presents a multilingual corpus of texts concerning ADRs gathered from diverse sources, including patient fora, social media, and clinical reports in German, French, and Japanese. Our corpus contains annotations covering 12 entity types, four attribute types, and 13 relation types. It contributes to the development of real-world multilingual language models for healthcare. We provide statistics to highlight certain challenges associated with the corpus and conduct preliminary experiments resulting in strong baselines for extracting entities and relations between these entities, both within and across languages.",
}
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%0 Conference Proceedings
%T A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages
%A Raithel, Lisa
%A Yeh, Hui-Syuan
%A Yada, Shuntaro
%A Grouin, Cyril
%A Lavergne, Thomas
%A Névéol, Aurélie
%A Paroubek, Patrick
%A Thomas, Philippe
%A Nishiyama, Tomohiro
%A Möller, Sebastian
%A Aramaki, Eiji
%A Matsumoto, Yuji
%A Roller, Roland
%A Zweigenbaum, Pierre
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F raithel-etal-2024-dataset-pharmacovigilance
%X User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world. However, the existing clinical corpora predominantly revolve around scientific articles in English. This work presents a multilingual corpus of texts concerning ADRs gathered from diverse sources, including patient fora, social media, and clinical reports in German, French, and Japanese. Our corpus contains annotations covering 12 entity types, four attribute types, and 13 relation types. It contributes to the development of real-world multilingual language models for healthcare. We provide statistics to highlight certain challenges associated with the corpus and conduct preliminary experiments resulting in strong baselines for extracting entities and relations between these entities, both within and across languages.
%U https://aclanthology.org/2024.lrec-main.36
%P 395-414
Markdown (Informal)
[A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages](https://aclanthology.org/2024.lrec-main.36) (Raithel et al., LREC-COLING 2024)
ACL
- Lisa Raithel, Hui-Syuan Yeh, Shuntaro Yada, Cyril Grouin, Thomas Lavergne, Aurélie Névéol, Patrick Paroubek, Philippe Thomas, Tomohiro Nishiyama, Sebastian Möller, Eiji Aramaki, Yuji Matsumoto, Roland Roller, and Pierre Zweigenbaum. 2024. A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 395–414, Torino, Italia. ELRA and ICCL.