Identifying Medication Abuse and Adverse Effects from Tweets: University of Michigan at #SMM4H 2020

V.G.Vinod Vydiswaran, Deahan Yu, Xinyan Zhao, Ermioni Carr, Jonathan Martindale, Jingcheng Xiao, Noha Ghannam, Matteo Althoen, Alexis Castellanos, Neel Patel, Daniel Vasquez


Abstract
The team from the University of Michigan participated in three tasks in the Social Media Mining for Health Applications (#SMM4H) 2020 shared tasks – on detecting mentions of adverse effects (Task 2), extracting and normalizing them (Task 3), and detecting mentions of medication abuse (Task 4). Our approaches relied on a combination of traditional machine learning and deep learning models. On Tasks 2 and 4, our submitted runs performed at or above the task average.
Anthology ID:
2020.smm4h-1.13
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:
90–94
Language:
URL:
https://www.aclweb.org/anthology/2020.smm4h-1.13
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.smm4h-1.13.pdf