@inproceedings{aji-etal-2021-bert,
title = "{BERT} Goes Brrr: A Venture Towards the Lesser Error in Classifying Medical Self-Reporters on {T}witter",
author = "Aji, Alham Fikri and
Nityasya, Made Nindyatama and
Wibowo, Haryo Akbarianto and
Prasojo, Radityo Eko and
Fatyanosa, Tirana",
editor = "Magge, Arjun and
Klein, Ari and
Miranda-Escalada, Antonio and
Al-garadi, Mohammed Ali and
Alimova, Ilseyar and
Miftahutdinov, Zulfat and
Farre-Maduell, Eulalia and
Lopez, Salvador Lima and
Flores, Ivan and
O'Connor, Karen and
Weissenbacher, Davy and
Tutubalina, Elena and
Sarker, Abeed and
Banda, Juan M and
Krallinger, Martin and
Gonzalez-Hernandez, Graciela",
booktitle = "Proceedings of the Sixth Social Media Mining for Health ({\#}SMM4H) Workshop and Shared Task",
month = jun,
year = "2021",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.smm4h-1.9",
doi = "10.18653/v1/2021.smm4h-1.9",
pages = "58--64",
abstract = "This paper describes our team{'}s submission for the Social Media Mining for Health (SMM4H) 2021 shared task. We participated in three subtasks: Classifying adverse drug effect, COVID-19 self-report, and COVID-19 symptoms. Our system is based on BERT model pre-trained on the domain-specific text. In addition, we perform data cleaning and augmentation, as well as hyperparameter optimization and model ensemble to further boost the BERT performance. We achieved the first rank in both classifying adverse drug effects and COVID-19 self-report tasks.",
}
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<abstract>This paper describes our team’s submission for the Social Media Mining for Health (SMM4H) 2021 shared task. We participated in three subtasks: Classifying adverse drug effect, COVID-19 self-report, and COVID-19 symptoms. Our system is based on BERT model pre-trained on the domain-specific text. In addition, we perform data cleaning and augmentation, as well as hyperparameter optimization and model ensemble to further boost the BERT performance. We achieved the first rank in both classifying adverse drug effects and COVID-19 self-report tasks.</abstract>
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%0 Conference Proceedings
%T BERT Goes Brrr: A Venture Towards the Lesser Error in Classifying Medical Self-Reporters on Twitter
%A Aji, Alham Fikri
%A Nityasya, Made Nindyatama
%A Wibowo, Haryo Akbarianto
%A Prasojo, Radityo Eko
%A Fatyanosa, Tirana
%Y Magge, Arjun
%Y Klein, Ari
%Y Miranda-Escalada, Antonio
%Y Al-garadi, Mohammed Ali
%Y Alimova, Ilseyar
%Y Miftahutdinov, Zulfat
%Y Farre-Maduell, Eulalia
%Y Lopez, Salvador Lima
%Y Flores, Ivan
%Y O’Connor, Karen
%Y Weissenbacher, Davy
%Y Tutubalina, Elena
%Y Sarker, Abeed
%Y Banda, Juan M.
%Y Krallinger, Martin
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
%D 2021
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F aji-etal-2021-bert
%X This paper describes our team’s submission for the Social Media Mining for Health (SMM4H) 2021 shared task. We participated in three subtasks: Classifying adverse drug effect, COVID-19 self-report, and COVID-19 symptoms. Our system is based on BERT model pre-trained on the domain-specific text. In addition, we perform data cleaning and augmentation, as well as hyperparameter optimization and model ensemble to further boost the BERT performance. We achieved the first rank in both classifying adverse drug effects and COVID-19 self-report tasks.
%R 10.18653/v1/2021.smm4h-1.9
%U https://aclanthology.org/2021.smm4h-1.9
%U https://doi.org/10.18653/v1/2021.smm4h-1.9
%P 58-64
Markdown (Informal)
[BERT Goes Brrr: A Venture Towards the Lesser Error in Classifying Medical Self-Reporters on Twitter](https://aclanthology.org/2021.smm4h-1.9) (Aji et al., SMM4H 2021)
ACL