德语、法语和日语的药物警戒数据集：跨语言标注药物不良反应
A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages
AI总结：
本文构建了德语、法语和日语的多语言药物不良反应标注语料库，涵盖多来源文本及12种实体、4种属性和13种关系类型，并通过实验为跨语言实体与关系提取提供了强基线。
摘要：
用户生成的数据源在发现药物不良反应（ADRs）方面日益重要，数字世界中的相关讨论数量不断增加。然而，现有的临床语料库主要围绕英文科学文章。本研究提出了一个多语言文本语料库，收集了来自德语、法语和日语的患者论坛、社交媒体和临床报告等多种来源的关于药物不良反应的文本。我们的语料库包含覆盖12种实体类型、4种属性类型和13种关系类型的标注。它为开发面向医疗保健的真实世界多语言语言模型做出了贡献。我们提供了统计数据以突出与该语料库相关的某些挑战，并进行了初步实验，在语言内部和跨语言的实体及实体间关系提取方面得到了强基线结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
