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:
Bibkey:
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)
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PDF:
https://aclanthology.org/2022.smm4h-1.43.pdf