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2019 ACL ACL 2019

Towards Text Processing Pipelines to Identify Adverse Drug Events-related Tweets: University of Michigan @ SMM4H 2019 Task 1

Abstract

AbstractWe 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. Our approach relied on a text processing pipeline for tweets, and training traditional machine learning and deep learning models. Our submitted runs performed above average for the task.

🌉 Interdisciplinary Bridge — Deep Learning and Healthcare & Medicine and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — text processing pipeline
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

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

Topics

Machine Learning > Application Areas > Risk Management Deep Learning > Architectures > Neural Networks Machine Learning > Learning Types > Supervised Learning Natural Language Processing > Applications > Named Entity Recognition Healthcare & Medicine > Clinical > Medical NLP

Keywords

text classification named entity recognition deep learning text processing pipeline text processing adverse drug event tweet processing
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