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:
Bibkey:
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)
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PDF:
https://aclanthology.org/2022.smm4h-1.19.pdf