Abstract
This paper describes models developed for the Social Media Mining for Health (SMM4H) 2021 shared tasks. Our team participated in the first subtask that classifies tweets with Adverse Drug Effect (ADE) mentions. Our best performing model utilizes BERTweet followed by a single layer of BiLSTM. The system achieves an F-score of 0.45 on the test set without the use of any auxiliary resources such as Part-of-Speech tags, dependency tags, or knowledge from medical dictionaries.- Anthology ID:
- 2021.smm4h-1.15
- Volume:
- Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
- Month:
- June
- Year:
- 2021
- Address:
- Mexico City, Mexico
- Editors:
- Arjun Magge, Ari Klein, Antonio Miranda-Escalada, Mohammed Ali Al-garadi, Ilseyar Alimova, Zulfat Miftahutdinov, Eulalia Farre-Maduell, Salvador Lima Lopez, Ivan Flores, Karen O'Connor, Davy Weissenbacher, Elena Tutubalina, Abeed Sarker, Juan M Banda, Martin Krallinger, Graciela Gonzalez-Hernandez
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 88–90
- Language:
- URL:
- https://aclanthology.org/2021.smm4h-1.15
- DOI:
- 10.18653/v1/2021.smm4h-1.15
- Bibkey:
- Cite (ACL):
- Tanay Kayastha, Pranjal Gupta, and Pushpak Bhattacharyya. 2021. BERT based Adverse Drug Effect Tweet Classification. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 88–90, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- BERT based Adverse Drug Effect Tweet Classification (Kayastha et al., SMM4H 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.smm4h-1.15.pdf
Export citation
@inproceedings{kayastha-etal-2021-bert,
title = "{BERT} based Adverse Drug Effect Tweet Classification",
author = "Kayastha, Tanay and
Gupta, Pranjal and
Bhattacharyya, Pushpak",
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-1.15",
doi = "10.18653/v1/2021.smm4h-1.15",
pages = "88--90",
abstract = "This paper describes models developed for the Social Media Mining for Health (SMM4H) 2021 shared tasks. Our team participated in the first subtask that classifies tweets with Adverse Drug Effect (ADE) mentions. Our best performing model utilizes BERTweet followed by a single layer of BiLSTM. The system achieves an F-score of 0.45 on the test set without the use of any auxiliary resources such as Part-of-Speech tags, dependency tags, or knowledge from medical dictionaries.",
}
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%0 Conference Proceedings %T BERT based Adverse Drug Effect Tweet Classification %A Kayastha, Tanay %A Gupta, Pranjal %A Bhattacharyya, Pushpak %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 kayastha-etal-2021-bert %X This paper describes models developed for the Social Media Mining for Health (SMM4H) 2021 shared tasks. Our team participated in the first subtask that classifies tweets with Adverse Drug Effect (ADE) mentions. Our best performing model utilizes BERTweet followed by a single layer of BiLSTM. The system achieves an F-score of 0.45 on the test set without the use of any auxiliary resources such as Part-of-Speech tags, dependency tags, or knowledge from medical dictionaries. %R 10.18653/v1/2021.smm4h-1.15 %U https://aclanthology.org/2021.smm4h-1.15 %U https://doi.org/10.18653/v1/2021.smm4h-1.15 %P 88-90
Markdown (Informal)
[BERT based Adverse Drug Effect Tweet Classification](https://aclanthology.org/2021.smm4h-1.15) (Kayastha et al., SMM4H 2021)
- BERT based Adverse Drug Effect Tweet Classification (Kayastha et al., SMM4H 2021)
ACL
- Tanay Kayastha, Pranjal Gupta, and Pushpak Bhattacharyya. 2021. BERT based Adverse Drug Effect Tweet Classification. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 88–90, Mexico City, Mexico. Association for Computational Linguistics.