@inproceedings{wang-etal-2020-islab,
title = "{ISL}ab System for {SMM}4{H} Shared Task 2020",
author = "Wang, Chen-Kai and
Dai, Hong-Jie and
Zhang, You-Chen and
Xu, Bo-Chun and
Wang, Bo-Hong and
Xu, You-Ning and
Chen, Po-Hao and
Lee, Chung-Hong",
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-1.6",
pages = "42--45",
abstract = "In this paper, we described our systems for the first and second subtasks of Social Media Mining for Health Applications (SMM4H) shared task in 2020. The two subtasks are automatic classi-fication of medication mentions and adverse effect in tweets. Our systems for both subtasks are based on Robustly optimized BERT approach (RoBERTa) and our previous work at SMM4H{'}19. The best F1-scores achieved by our systems for subtask 1 and 2 were 0.7974 and 0.64 respec-tively, which outperformed the average F1-scores among all teams{'} best runs by at least 0.13.",
}
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<abstract>In this paper, we described our systems for the first and second subtasks of Social Media Mining for Health Applications (SMM4H) shared task in 2020. The two subtasks are automatic classi-fication of medication mentions and adverse effect in tweets. Our systems for both subtasks are based on Robustly optimized BERT approach (RoBERTa) and our previous work at SMM4H’19. The best F1-scores achieved by our systems for subtask 1 and 2 were 0.7974 and 0.64 respec-tively, which outperformed the average F1-scores among all teams’ best runs by at least 0.13.</abstract>
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%0 Conference Proceedings
%T ISLab System for SMM4H Shared Task 2020
%A Wang, Chen-Kai
%A Dai, Hong-Jie
%A Zhang, You-Chen
%A Xu, Bo-Chun
%A Wang, Bo-Hong
%A Xu, You-Ning
%A Chen, Po-Hao
%A Lee, Chung-Hong
%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 wang-etal-2020-islab
%X In this paper, we described our systems for the first and second subtasks of Social Media Mining for Health Applications (SMM4H) shared task in 2020. The two subtasks are automatic classi-fication of medication mentions and adverse effect in tweets. Our systems for both subtasks are based on Robustly optimized BERT approach (RoBERTa) and our previous work at SMM4H’19. The best F1-scores achieved by our systems for subtask 1 and 2 were 0.7974 and 0.64 respec-tively, which outperformed the average F1-scores among all teams’ best runs by at least 0.13.
%U https://aclanthology.org/2020.smm4h-1.6
%P 42-45
Markdown (Informal)
[ISLab System for SMM4H Shared Task 2020](https://aclanthology.org/2020.smm4h-1.6) (Wang et al., SMM4H 2020)
ACL
- Chen-Kai Wang, Hong-Jie Dai, You-Chen Zhang, Bo-Chun Xu, Bo-Hong Wang, You-Ning Xu, Po-Hao Chen, and Chung-Hong Lee. 2020. ISLab System for SMM4H Shared Task 2020. In Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task, pages 42–45, Barcelona, Spain (Online). Association for Computational Linguistics.