Abstract
In this paper, we describe our approaches for task six of Social Media Mining for Health Applications (SMM4H) shared task in 2021. The task is to classify twitter tweets containing COVID-19 symptoms in three classes (self-reports, non-personal reports & literature/news mentions). We implemented BERT and XLNet for this text classification task. Best result was achieved by XLNet approach, which is F1 score 0.94, precision 0.9448 and recall 0.94448. This is slightly better than the average score, i.e. F1 score 0.93, precision 0.93235 and recall 0.93235.- Anthology ID:
- 2021.smm4h-1.19
- Volume:
- Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
- Month:
- June
- Year:
- 2021
- Address:
- Mexico City, Mexico
- Editors:
- Arjun Magge, Ari Klein, Antonio Miranda-Escalada, Mohammed Ali Al-garadi, Ilseyar Alimova, Zulfat Miftahutdinov, Eulalia Farre-Maduell, Salvador Lima Lopez, Ivan Flores, Karen O'Connor, Davy Weissenbacher, Elena Tutubalina, Abeed Sarker, Juan M Banda, Martin Krallinger, Graciela Gonzalez-Hernandez
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 102–104
- Language:
- URL:
- https://aclanthology.org/2021.smm4h-1.19
- DOI:
- 10.18653/v1/2021.smm4h-1.19
- Cite (ACL):
- Deepak Kumar, Nalin Kumar, and Subhankar Mishra. 2021. NLP@NISER: Classification of COVID19 tweets containing symptoms. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 102–104, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- NLP@NISER: Classification of COVID19 tweets containing symptoms (Kumar et al., SMM4H 2021)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2021.smm4h-1.19.pdf