AIR-JPMC@SMM4H’22: Identifying Self-Reported Spanish COVID-19 Symptom Tweets Through Multiple-Model Ensembling
Adrian Garcia Hernandez, Leung Wai Liu, Akshat Gupta, Vineeth Ravi, Saheed O. Obitayo, Xiaomo Liu, Sameena Shah
Abstract
We present our response to Task 5 of the Social Media Mining for Health Applications (SMM4H) 2022 competition. We share our approach into classifying whether a tweet in Spanish about COVID-19 symptoms pertain to themselves, others, or not at all. Using a combination of BERT based models, we were able to achieve results that were higher than the median result of the competition.- Anthology ID:
- 2022.smm4h-1.43
- 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:
- 160–162
- Language:
- URL:
- https://aclanthology.org/2022.smm4h-1.43
- DOI:
- Cite (ACL):
- Adrian Garcia Hernandez, Leung Wai Liu, Akshat Gupta, Vineeth Ravi, Saheed O. Obitayo, Xiaomo Liu, and Sameena Shah. 2022. AIR-JPMC@SMM4H’22: Identifying Self-Reported Spanish COVID-19 Symptom Tweets Through Multiple-Model Ensembling. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 160–162, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- AIR-JPMC@SMM4H’22: Identifying Self-Reported Spanish COVID-19 Symptom Tweets Through Multiple-Model Ensembling (Garcia Hernandez et al., SMM4H 2022)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2022.smm4h-1.43.pdf