Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers
Lung-Hao Lee, Man-Chen Hung, Chien-Huan Lu, Chang-Hao Chen, Po-Lei Lee, Kuo-Kai Shyu
Abstract
This study describes our proposed model design for SMM4H 2021 shared tasks. We fine-tune the language model of RoBERTa transformers and their connecting classifier to complete the classification tasks of tweets for adverse pregnancy outcomes (Task 4) and potential COVID-19 cases (Task 5). The evaluation metric is F1-score of the positive class for both tasks. For Task 4, our best score of 0.93 exceeded the mean score of 0.925. For Task 5, our best of 0.75 exceeded the mean score of 0.745.- Anthology ID:
- 2021.smm4h-1.18
- 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:
- 98–101
- Language:
- URL:
- https://aclanthology.org/2021.smm4h-1.18
- DOI:
- 10.18653/v1/2021.smm4h-1.18
- Bibkey:
- Cite (ACL):
- Lung-Hao Lee, Man-Chen Hung, Chien-Huan Lu, Chang-Hao Chen, Po-Lei Lee, and Kuo-Kai Shyu. 2021. Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 98–101, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers (Lee et al., SMM4H 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.smm4h-1.18.pdf
Export citation
@inproceedings{lee-etal-2021-classification,
title = "Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential {COVID}-19 Cases Using {R}o{BERT}a Transformers",
author = "Lee, Lung-Hao and
Hung, Man-Chen and
Lu, Chien-Huan and
Chen, Chang-Hao and
Lee, Po-Lei and
Shyu, Kuo-Kai",
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-1.18",
doi = "10.18653/v1/2021.smm4h-1.18",
pages = "98--101",
abstract = "This study describes our proposed model design for SMM4H 2021 shared tasks. We fine-tune the language model of RoBERTa transformers and their connecting classifier to complete the classification tasks of tweets for adverse pregnancy outcomes (Task 4) and potential COVID-19 cases (Task 5). The evaluation metric is F1-score of the positive class for both tasks. For Task 4, our best score of 0.93 exceeded the mean score of 0.925. For Task 5, our best of 0.75 exceeded the mean score of 0.745.",
}
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%0 Conference Proceedings %T Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers %A Lee, Lung-Hao %A Hung, Man-Chen %A Lu, Chien-Huan %A Chen, Chang-Hao %A Lee, Po-Lei %A Shyu, Kuo-Kai %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 lee-etal-2021-classification %X This study describes our proposed model design for SMM4H 2021 shared tasks. We fine-tune the language model of RoBERTa transformers and their connecting classifier to complete the classification tasks of tweets for adverse pregnancy outcomes (Task 4) and potential COVID-19 cases (Task 5). The evaluation metric is F1-score of the positive class for both tasks. For Task 4, our best score of 0.93 exceeded the mean score of 0.925. For Task 5, our best of 0.75 exceeded the mean score of 0.745. %R 10.18653/v1/2021.smm4h-1.18 %U https://aclanthology.org/2021.smm4h-1.18 %U https://doi.org/10.18653/v1/2021.smm4h-1.18 %P 98-101
Markdown (Informal)
[Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers](https://aclanthology.org/2021.smm4h-1.18) (Lee et al., SMM4H 2021)
- Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers (Lee et al., SMM4H 2021)
ACL
- Lung-Hao Lee, Man-Chen Hung, Chien-Huan Lu, Chang-Hao Chen, Po-Lei Lee, and Kuo-Kai Shyu. 2021. Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 98–101, Mexico City, Mexico. Association for Computational Linguistics.