# A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages

arXiv: https://arxiv.org/abs/2403.18336 · HTML: https://mlsanity.com/paper/2403.18336/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 54 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 21 of 21)

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- [The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews](https://mlsanity.com/paper/2004.03659/) (2020)
- [Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media](https://mlsanity.com/paper/2109.05815/) (2021)
- [Multi-Task Pharmacovigilance Mining from Social Media Posts](https://mlsanity.com/paper/1801.06294/) (2018)
- [Increasing Adverse Drug Events extraction robustness on social media: case study on negation and speculation](https://mlsanity.com/paper/2209.02812/) (2022)
- [AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles](https://mlsanity.com/paper/2209.03452/) (2022)

## Abstract (arXiv)

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.