@inproceedings{raithel-etal-2024-dataset,
    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.",
}

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.
Raithel, Lisa Yeh, Hui-Syuan Yada, Shuntaro Grouin, Cyril Lavergne, Thomas N{\'e}v{\'e}ol, Aur{\'e}lie Paroubek, Patrick Thomas, Philippe Nishiyama, Tomohiro M{\"o}ller, Sebastian Aramaki, Eiji Matsumoto, Yuji Roller, Rol Zweigenbaum, Pierre
A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages