@inproceedings{khademi-etal-2020-adverse,
title = "Adverse Drug Reaction Detection in {T}witter Using {R}o{BERT}a and Rules",
author = "Khademi, Sedigh and
Delirhaghighi, Pari and
Burstein, Frada",
editor = "Gonzalez-Hernandez, Graciela and
Klein, Ari Z. and
Flores, Ivan and
Weissenbacher, Davy and
Magge, Arjun and
O'Connor, Karen and
Sarker, Abeed and
Minard, Anne-Lyse and
Tutubalina, Elena and
Miftahutdinov, Zulfat and
Alimova, Ilseyar",
booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.smm4h-1.18",
pages = "113--117",
abstract = "This paper describes the method we developed for the Task 2 English variation of the Social Media Mining for Health Applications (SMM4H) 2020 shared task. The task was to classify tweets containing adverse effects (AE) after medication intake. Our approach combined transfer learning using a RoBERTa Large Transformer model with a rule-based post-prediction correction to improve model precision. The model{'}s F1-Score of 0.56 on the test dataset was 10{\%} better than the mean of the F1-Score of the best submissions in the task.",
}
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%0 Conference Proceedings
%T Adverse Drug Reaction Detection in Twitter Using RoBERTa and Rules
%A Khademi, Sedigh
%A Delirhaghighi, Pari
%A Burstein, Frada
%Y Gonzalez-Hernandez, Graciela
%Y Klein, Ari Z.
%Y Flores, Ivan
%Y Weissenbacher, Davy
%Y Magge, Arjun
%Y O’Connor, Karen
%Y Sarker, Abeed
%Y Minard, Anne-Lyse
%Y Tutubalina, Elena
%Y Miftahutdinov, Zulfat
%Y Alimova, Ilseyar
%S Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F khademi-etal-2020-adverse
%X This paper describes the method we developed for the Task 2 English variation of the Social Media Mining for Health Applications (SMM4H) 2020 shared task. The task was to classify tweets containing adverse effects (AE) after medication intake. Our approach combined transfer learning using a RoBERTa Large Transformer model with a rule-based post-prediction correction to improve model precision. The model’s F1-Score of 0.56 on the test dataset was 10% better than the mean of the F1-Score of the best submissions in the task.
%U https://aclanthology.org/2020.smm4h-1.18
%P 113-117
Markdown (Informal)
[Adverse Drug Reaction Detection in Twitter Using RoBERTa and Rules](https://aclanthology.org/2020.smm4h-1.18) (Khademi et al., SMM4H 2020)
ACL