| アイテムタイプ |
会議発表論文 / Conference Paper(1) |
| 公開日 |
2025-05-30 |
| タイトル |
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|
タイトル |
A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages |
| 言語 |
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言語 |
eng |
| キーワード |
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|
主題Scheme |
Other |
|
主題 |
biomedical NLP |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
information extraction |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
adverse drug reactions |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
multilingual |
| 資源タイプ |
|
|
資源タイプ |
conference paper |
| アクセス権 |
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|
アクセス権 |
open access |
| 著者 |
Raithel, Lisa
Yeh, Hui-Syuan
矢田, 竣太郎
Grouin, Cyril
Lavergne, Thomas
N$00E9v$00E9ol, Aur$00E9lie
Paroubek, Patrick
Thomas, Philippe
Nishiyama, Tomohiro
M$00F6ller, Sebastian
荒牧, 英治
Matsumoto, Yuji
Roller, Roland
Zweigenbaum, Pierre
|
| 抄録 |
|
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内容記述タイプ |
Abstract |
|
内容記述 |
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. |
| 書誌情報 |
en : Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
p. 395-414,
発行日 2024-05-23
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| 会議情報 |
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会議名 |
In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) |
|
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開始年 |
2024 |
|
|
開始月 |
05 |
|
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開始日 |
20 |
|
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終了年 |
2024 |
|
|
終了月 |
05 |
|
|
終了日 |
25 |
|
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開催地 |
Torino |
|
開催国 |
ITA |
| 出版者 |
|
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出版者 |
ELRA and ICCL |
| 出版者版URI |
|
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
|
|
関連識別子 |
https://aclanthology.org/2024.lrec-main.36/ |
| 権利 |
|
|
権利情報Resource |
https://creativecommons.org/licenses/by-nc/4.0/ |
|
権利情報 |
$00A9 2024 ELRA Language Resource Association: CC BY-NC 4.0 |
| 著者版フラグ |
|
|
出版タイプ |
NA |
| 助成情報 |
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|
助成機関名 |
Japan Science and Technology Agency(JST) |
|
|
研究課題番号 |
JPMJCR20G9 |
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|
研究課題名 |
医薬品安全性監視のための言語を超えた知識強化情報抽出(KEEPHA) |
| 助成情報 |
|
|
|
助成機関名 |
Agence Nationale de la Recherche(ANR) |
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|
研究課題番号 |
ANR-20-IADJ-0005-01 |
| 助成情報 |
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|
助成機関名 |
Deutsche Forschungsgemeinschaft (DFG) |
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研究課題番号 |
DFG442445488 |
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研究課題名 |
Mehrsprachige wissensverbesserte Informationsextraktion f$00FCr die Pharmakovigilanz |
| 助成情報 |
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|
助成機関名 |
German Federal Ministry of Education and Research |
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研究課題番号 |
BIFOLD24B |
