UKYNLP@SMM4H2024: Language Model Methods for Health Entity Tagging and Classification on Social Media (Tasks 4 & 5)
Motasem Obeidat, Vinu Ekanayake, Md Sultan Al Nahian, Ramakanth Kavuluru
Abstract
We describe the methods and results of our submission to the 9th Social Media Mining for Health Research and Applications (SMM4H) 2024 shared tasks 4 and 5. Task 4 involved extracting the clinical and social impacts of non-medical substance use and task 5 focused on the binary classification of tweets reporting children’s medical disorders. We employed encoder language models and their ensembles, achieving the top score on task 4 and a high score for task 5.- Anthology ID:
- 2024.smm4h-1.29
- 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:
- 124–129
- Language:
- URL:
- https://aclanthology.org/2024.smm4h-1.29
- DOI:
- Cite (ACL):
- Motasem Obeidat, Vinu Ekanayake, Md Sultan Al Nahian, and Ramakanth Kavuluru. 2024. UKYNLP@SMM4H2024: Language Model Methods for Health Entity Tagging and Classification on Social Media (Tasks 4 & 5). In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, pages 124–129, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- UKYNLP@SMM4H2024: Language Model Methods for Health Entity Tagging and Classification on Social Media (Tasks 4 & 5) (Obeidat et al., SMM4H-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.smm4h-1.29.pdf