no code implementations • SMM4H (COLING) 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
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).
no code implementations • WS 2019 • Xinyan Zhao, Deahan Yu, V.G.Vinod Vydiswaran
Identifying mentions of medical concepts in social media is challenging because of high variability in free text.
no code implementations • WS 2019 • V.G.Vinod Vydiswaran, Grace Ganzel, Bryan Romas, Deahan Yu, Amy Austin, Neha Bhomia, Socheatha Chan, Stephanie Hall, Van Le, Aaron Miller, Olawunmi Oduyebo, Aulia Song, Radhika Sondhi, Danny Teng, Hao Tseng, Kim Vuong, Stephanie Zimmerman
We participated in Task 1 of the Social Media Mining for Health Applications (SMM4H) 2019 Shared Tasks on detecting mentions of adverse drug events (ADEs) in tweets.
no code implementations • IJCNLP 2017 • Shibamouli Lahiri, V.G.Vinod Vydiswaran, Rada Mihalcea
The system combines lexical, syntactic, and semantic features in a product-agnostic fashion to yield good classification performance.