Leung Wai Liu
2022
AIR-JPMC@SMM4H’22: BERT + Ensembling = Too Cool: Using Multiple BERT Models Together for Various COVID-19 Tweet Identification Tasks
Leung Wai Liu | Akshat Gupta | Saheed Obitayo | Xiaomo Liu | Sameena Shah
Proceedings of the Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
Leung Wai Liu | Akshat Gupta | Saheed Obitayo | Xiaomo Liu | Sameena Shah
Proceedings of the Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
This paper presents my submission for Tasks 1 and 2 for the Social Media Mining of Health (SMM4H) 2022 Shared Tasks competition. I first describe the background behind each of these tasks, followed by the descriptions of the various subtasks of Tasks 1 and 2, then present the methodology. Through model ensembling, this methodology was able to achieve higher results than the mean and median of the competition for the classification tasks.
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
Proceedings of the Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
Adrian Garcia Hernandez | Leung Wai Liu | Akshat Gupta | Vineeth Ravi | Saheed O. Obitayo | Xiaomo Liu | Sameena Shah
Proceedings of the Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
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.