Abstract
We describe in this paper our proposed systems for the Social Media Mining for Health 2022 shared task 1. In particular, we participated in the three sub-tasks, tasks that aim at extracting and processing Adverse Drug Events. We investigate different transformer-based pretrained models we fine-tuned on each task and proposed some improvement on the task of entity normalization.- Anthology ID:
- 2022.smm4h-1.46
- Volume:
- Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Graciela Gonzalez-Hernandez, Davy Weissenbacher
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 171–175
- Language:
- URL:
- https://aclanthology.org/2022.smm4h-1.46
- DOI:
- Cite (ACL):
- Omar Adjali, Fréjus A. A. Laleye, and Umang Aggarwal. 2022. OFU@SMM4H’22: Mining Advent Drug Events Using Pretrained Language Models. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 171–175, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- OFU@SMM4H’22: Mining Advent Drug Events Using Pretrained Language Models (Adjali et al., SMM4H 2022)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2022.smm4h-1.46.pdf