Automatic Classification of Tweets Mentioning a Medication Using Pre-trained Sentence Encoders

Laiba Mehnaz


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.
Anthology ID:
2020.smm4h-1.27
Volume:
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venues:
COLING | SMM4H
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
150–152
Language:
URL:
https://www.aclweb.org/anthology/2020.smm4h-1.27
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.smm4h-1.27.pdf