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Abstract
In this paper, we describe our approaches for task six of Social Media Mining for Health Applications (SMM4H) shared task in 2021. The task is to classify twitter tweets containing COVID-19 symptoms in three classes (self-reports, non-personal reports & literature/news mentions). We implemented BERT and XLNet for this text classification task. Best result was achieved by XLNet approach, which is F1 score 0.94, precision 0.9448 and recall 0.94448. This is slightly better than the average score, i.e. F1 score 0.93, precision 0.93235 and recall 0.93235.- Anthology ID:
- 2021.smm4h-1.19
- 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:
- 102–104
- Language:
- URL:
- https://aclanthology.org/2021.smm4h-1.19/
- DOI:
- 10.18653/v1/2021.smm4h-1.19
- Bibkey:
- Cite (ACL):
- Deepak Kumar, Nalin Kumar, and Subhankar Mishra. 2021. NLP@NISER: Classification of COVID19 tweets containing symptoms. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 102–104, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- NLP@NISER: Classification of COVID19 tweets containing symptoms (Kumar et al., SMM4H 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.smm4h-1.19.pdf
Export citation
@inproceedings{kumar-etal-2021-nlp,
title = "{NLP}@{NISER}: Classification of {COVID}19 tweets containing symptoms",
author = "Kumar, Deepak and
Kumar, Nalin and
Mishra, Subhankar",
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-1.19/",
doi = "10.18653/v1/2021.smm4h-1.19",
pages = "102--104",
abstract = "In this paper, we describe our approaches for task six of Social Media Mining for Health Applications (SMM4H) shared task in 2021. The task is to classify twitter tweets containing COVID-19 symptoms in three classes (self-reports, non-personal reports {\&} literature/news mentions). We implemented BERT and XLNet for this text classification task. Best result was achieved by XLNet approach, which is F1 score 0.94, precision 0.9448 and recall 0.94448. This is slightly better than the average score, i.e. F1 score 0.93, precision 0.93235 and recall 0.93235."
}
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%0 Conference Proceedings %T NLP@NISER: Classification of COVID19 tweets containing symptoms %A Kumar, Deepak %A Kumar, Nalin %A Mishra, Subhankar %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 kumar-etal-2021-nlp %X In this paper, we describe our approaches for task six of Social Media Mining for Health Applications (SMM4H) shared task in 2021. The task is to classify twitter tweets containing COVID-19 symptoms in three classes (self-reports, non-personal reports & literature/news mentions). We implemented BERT and XLNet for this text classification task. Best result was achieved by XLNet approach, which is F1 score 0.94, precision 0.9448 and recall 0.94448. This is slightly better than the average score, i.e. F1 score 0.93, precision 0.93235 and recall 0.93235. %R 10.18653/v1/2021.smm4h-1.19 %U https://aclanthology.org/2021.smm4h-1.19/ %U https://doi.org/10.18653/v1/2021.smm4h-1.19 %P 102-104
Markdown (Informal)
[NLP@NISER: Classification of COVID19 tweets containing symptoms](https://aclanthology.org/2021.smm4h-1.19/) (Kumar et al., SMM4H 2021)
- NLP@NISER: Classification of COVID19 tweets containing symptoms (Kumar et al., SMM4H 2021)
ACL
- Deepak Kumar, Nalin Kumar, and Subhankar Mishra. 2021. NLP@NISER: Classification of COVID19 tweets containing symptoms. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 102–104, Mexico City, Mexico. Association for Computational Linguistics.