Abstract
This paper describes the method we developed for the Task 2 English variation of the Social Media Mining for Health Applications (SMM4H) 2020 shared task. The task was to classify tweets containing adverse effects (AE) after medication intake. Our approach combined transfer learning using a RoBERTa Large Transformer model with a rule-based post-prediction correction to improve model precision. The model’s F1-Score of 0.56 on the test dataset was 10% better than the mean of the F1-Score of the best submissions in the task.- Anthology ID:
- 2020.smm4h-1.18
- Volume:
- Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Graciela Gonzalez-Hernandez, Ari Z. Klein, Ivan Flores, Davy Weissenbacher, Arjun Magge, Karen O'Connor, Abeed Sarker, Anne-Lyse Minard, Elena Tutubalina, Zulfat Miftahutdinov, Ilseyar Alimova
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 113–117
- Language:
- URL:
- https://aclanthology.org/2020.smm4h-1.18
- DOI:
- Cite (ACL):
- Sedigh Khademi, Pari Delirhaghighi, and Frada Burstein. 2020. Adverse Drug Reaction Detection in Twitter Using RoBERTa and Rules. In Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task, pages 113–117, Barcelona, Spain (Online). Association for Computational Linguistics.
- Cite (Informal):
- Adverse Drug Reaction Detection in Twitter Using RoBERTa and Rules (Khademi et al., SMM4H 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.smm4h-1.18.pdf
- Data
- SMM4H