Abstract
This work describes the participation of the Universidad Autónoma de Chihuahua - Instituto Nacional de Astrofísica, Óptica y Electrónica team at the Social Media Mining for Health Applications (SMM4H) 2021 shared task. Our team participated in task 5 and 6, both focused on the automatic classification of Twitter posts related to COVID-19. Task 5 was oriented on solving a binary classification problem, trying to identify self-reporting tweets of potential cases of COVID-19. Task 6 objective was to classify tweets containing COVID-19 symptoms. For both tasks we used models based on bidirectional encoder representations from transformers (BERT). Our objective was to determine if a model pretrained on a corpus in the domain of interest can outperform one trained on a much larger general domain corpus. Our F1 results were encouraging, 0.77 and 0.95 for task 5 and 6 respectively, having achieved the highest score among all the participants in the latter.- Anthology ID:
- 2021.smm4h-1.10
- Volume:
- Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
- Month:
- June
- Year:
- 2021
- Address:
- Mexico City, Mexico
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 65–68
- Language:
- URL:
- https://aclanthology.org/2021.smm4h-1.10
- DOI:
- 10.18653/v1/2021.smm4h-1.10
- Cite (ACL):
- Alberto Valdes, Jesus Lopez, and Manuel Montes. 2021. UACH-INAOE at SMM4H: a BERT based approach for classification of COVID-19 Twitter posts. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 65–68, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- UACH-INAOE at SMM4H: a BERT based approach for classification of COVID-19 Twitter posts (Valdes et al., SMM4H 2021)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2021.smm4h-1.10.pdf