@inproceedings{lee-etal-2020-medication,
title = "Medication Mention Detection in Tweets Using {ELECTRA} Transformers and Decision Trees",
author = "Lee, Lung-Hao and
Chen, Po-Han and
Kao, Hao-Chuan and
Hung, Ting-Chun and
Lee, Po-Lei and
Shyu, Kuo-Kai",
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-1.23",
pages = "131--133",
abstract = "This study describes our proposed model design for the SMM4H 2020 Task 1. We fine-tune ELECTRA transformers using our trained SVM filter for data augmentation, along with decision trees to detect medication mentions in tweets. Our best F1-score of 0.7578 exceeded the mean score 0.6646 of all 15 submitting teams.",
}
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<abstract>This study describes our proposed model design for the SMM4H 2020 Task 1. We fine-tune ELECTRA transformers using our trained SVM filter for data augmentation, along with decision trees to detect medication mentions in tweets. Our best F1-score of 0.7578 exceeded the mean score 0.6646 of all 15 submitting teams.</abstract>
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%0 Conference Proceedings
%T Medication Mention Detection in Tweets Using ELECTRA Transformers and Decision Trees
%A Lee, Lung-Hao
%A Chen, Po-Han
%A Kao, Hao-Chuan
%A Hung, Ting-Chun
%A Lee, Po-Lei
%A Shyu, Kuo-Kai
%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 lee-etal-2020-medication
%X This study describes our proposed model design for the SMM4H 2020 Task 1. We fine-tune ELECTRA transformers using our trained SVM filter for data augmentation, along with decision trees to detect medication mentions in tweets. Our best F1-score of 0.7578 exceeded the mean score 0.6646 of all 15 submitting teams.
%U https://aclanthology.org/2020.smm4h-1.23
%P 131-133
Markdown (Informal)
[Medication Mention Detection in Tweets Using ELECTRA Transformers and Decision Trees](https://aclanthology.org/2020.smm4h-1.23) (Lee et al., SMM4H 2020)
ACL