Abstract
This paper describes three RoBERTa based systems. The first one recognizes adverse drug events (ADEs) in English tweets and links themwith MedDRA concepts. It scored F1-norm of 40 for the Task 1. The next one extracts pharmacovigilance related named entities inFrench and scored a F1 of 0.4132 for the Task 2a. The third system extracts pharmacovigilance related named entities and their relationsin Japanese. It obtained a F1 of 0.5827 for the Task 2a and 0.0301 for the Task 2b. The French and Japanese systems are the best performing system for the Task 2- Anthology ID:
- 2024.smm4h-1.32
- Volume:
- Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Dongfang Xu, Graciela Gonzalez-Hernandez
- Venues:
- SMM4H | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 136–141
- Language:
- URL:
- https://aclanthology.org/2024.smm4h-1.32
- DOI:
- Cite (ACL):
- Anubhav Gupta. 2024. Team Yseop at #SMM4H 2024: Multilingual Pharmacovigilance Named Entity Recognition and Relation Extraction. In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, pages 136–141, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Team Yseop at #SMM4H 2024: Multilingual Pharmacovigilance Named Entity Recognition and Relation Extraction (Gupta, SMM4H-WS 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.smm4h-1.32.pdf