OFU@SMM4H’22: Mining Advent Drug Events Using Pretrained Language Models

Omar Adjali, Fréjus A. A. Laleye, Umang Aggarwal


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:
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/naacl-24-ws-corrections/2022.smm4h-1.46.pdf