ARGUABLY@SMM4H’22: Classification of Health Related Tweets using Ensemble, Zero-Shot and Fine-Tuned Language Model

Prabsimran Kaur, Guneet Kohli, Jatin Bedi


Abstract
With the increase in the use of social media, people have become more outspoken and are using platforms like Reddit, Facebook, and Twitter to express their views and share the medical challenges they are facing. This data is a valuable source of medical insight and is often used for healthcare research. This paper describes our participation in Task 1a, 2a, 2b, 3, 5, 6, 7, and 9 organized by SMM4H 2022. We have proposed two transformer-based approaches to handle the classification tasks. The first approach is fine-tuning single language models. The second approach is ensembling the results of BERT, RoBERTa, and ERNIE 2.0.
Anthology ID:
2022.smm4h-1.38
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:
138–142
Language:
URL:
https://aclanthology.org/2022.smm4h-1.38
DOI:
Bibkey:
Cite (ACL):
Prabsimran Kaur, Guneet Kohli, and Jatin Bedi. 2022. ARGUABLY@SMM4H’22: Classification of Health Related Tweets using Ensemble, Zero-Shot and Fine-Tuned Language Model. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 138–142, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
ARGUABLY@SMM4H’22: Classification of Health Related Tweets using Ensemble, Zero-Shot and Fine-Tuned Language Model (Kaur et al., SMM4H 2022)
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PDF:
https://aclanthology.org/2022.smm4h-1.38.pdf