@inproceedings{elkaref-hassan-2021-joint,
title = "A Joint Training Approach to Tweet Classification and Adverse Effect Extraction and Normalization for {SMM}4{H} 2021",
author = "Elkaref, Mohab and
Hassan, Lamiece",
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-1.16",
doi = "10.18653/v1/2021.smm4h-1.16",
pages = "91--94",
abstract = "In this work we describe our submissions to the Social Media Mining for Health (SMM4H) 2021 Shared Task. We investigated the effectiveness of a joint training approach to Task 1, specifically classification, extraction and normalization of Adverse Drug Effect (ADE) mentions in English tweets. Our approach performed well on the normalization task, achieving an above average f1 score of 24{\%}, but less so on classification and extraction, with f1 scores of 22{\%} and 37{\%} respectively. Our experiments also showed that a larger dataset with more negative results led to stronger results than a smaller more balanced dataset, even when both datasets have the same positive examples. Finally we also submitted a tuned BERT model for Task 6: Classification of Covid-19 tweets containing symptoms, which achieved an above average f1 score of 96{\%}.",
}
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%0 Conference Proceedings
%T A Joint Training Approach to Tweet Classification and Adverse Effect Extraction and Normalization for SMM4H 2021
%A Elkaref, Mohab
%A Hassan, Lamiece
%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 elkaref-hassan-2021-joint
%X In this work we describe our submissions to the Social Media Mining for Health (SMM4H) 2021 Shared Task. We investigated the effectiveness of a joint training approach to Task 1, specifically classification, extraction and normalization of Adverse Drug Effect (ADE) mentions in English tweets. Our approach performed well on the normalization task, achieving an above average f1 score of 24%, but less so on classification and extraction, with f1 scores of 22% and 37% respectively. Our experiments also showed that a larger dataset with more negative results led to stronger results than a smaller more balanced dataset, even when both datasets have the same positive examples. Finally we also submitted a tuned BERT model for Task 6: Classification of Covid-19 tweets containing symptoms, which achieved an above average f1 score of 96%.
%R 10.18653/v1/2021.smm4h-1.16
%U https://aclanthology.org/2021.smm4h-1.16
%U https://doi.org/10.18653/v1/2021.smm4h-1.16
%P 91-94
Markdown (Informal)
[A Joint Training Approach to Tweet Classification and Adverse Effect Extraction and Normalization for SMM4H 2021](https://aclanthology.org/2021.smm4h-1.16) (Elkaref & Hassan, SMM4H 2021)
ACL