BioInfo@UAVR@SMM4H’22: Classification and Extraction of Adverse Event mentions in Tweets using Transformer Models
Edgar Morais, José Luis Oliveira, Alina Trifan, Olga Fajarda
Abstract
This paper describes BioInfo@UAVR team’s approach for adressing subtasks 1a and 1b of the Social Media Mining for Health Applications 2022 shared task. These sub-tasks deal with the classification of tweets that contain an Adverse Drug Event mentions and the detection of spans that correspond to those mentions. Our approach relies on transformer-based models, data augmentation, and an external dataset.- Anthology ID:
- 2022.smm4h-1.19
- 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:
- 65–67
- Language:
- URL:
- https://aclanthology.org/2022.smm4h-1.19
- DOI:
- Cite (ACL):
- Edgar Morais, José Luis Oliveira, Alina Trifan, and Olga Fajarda. 2022. BioInfo@UAVR@SMM4H’22: Classification and Extraction of Adverse Event mentions in Tweets using Transformer Models. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 65–67, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- BioInfo@UAVR@SMM4H’22: Classification and Extraction of Adverse Event mentions in Tweets using Transformer Models (Morais et al., SMM4H 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.smm4h-1.19.pdf