@inproceedings{mehnaz-2020-automatic,
title = "Automatic Classification of Tweets Mentioning a Medication Using Pre-trained Sentence Encoders",
author = "Mehnaz, Laiba",
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-1.27",
pages = "150--152",
abstract = "This paper describes our submission to the 5th edition of the Social Media Mining for Health Applications (SMM4H) shared task 1. Task 1 aims at the automatic classification of tweets that mention a medication or a dietary supplement. This task is specifically challenging due to its highly imbalanced dataset, with only 0.2{\%} of the tweets mentioning a drug. For our submission, we particularly focused on several pretrained encoders for text classification. We achieve an F1 score of 0.75 for the positive class on the test set.",
}
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<abstract>This paper describes our submission to the 5th edition of the Social Media Mining for Health Applications (SMM4H) shared task 1. Task 1 aims at the automatic classification of tweets that mention a medication or a dietary supplement. This task is specifically challenging due to its highly imbalanced dataset, with only 0.2% of the tweets mentioning a drug. For our submission, we particularly focused on several pretrained encoders for text classification. We achieve an F1 score of 0.75 for the positive class on the test set.</abstract>
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%0 Conference Proceedings
%T Automatic Classification of Tweets Mentioning a Medication Using Pre-trained Sentence Encoders
%A Mehnaz, Laiba
%S Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
%D 2020
%8 dec
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F mehnaz-2020-automatic
%X This paper describes our submission to the 5th edition of the Social Media Mining for Health Applications (SMM4H) shared task 1. Task 1 aims at the automatic classification of tweets that mention a medication or a dietary supplement. This task is specifically challenging due to its highly imbalanced dataset, with only 0.2% of the tweets mentioning a drug. For our submission, we particularly focused on several pretrained encoders for text classification. We achieve an F1 score of 0.75 for the positive class on the test set.
%U https://aclanthology.org/2020.smm4h-1.27
%P 150-152
Markdown (Informal)
[Automatic Classification of Tweets Mentioning a Medication Using Pre-trained Sentence Encoders](https://aclanthology.org/2020.smm4h-1.27) (Mehnaz, SMM4H 2020)
ACL