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

Abstract

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. 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.

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.18653/v1/w19-3217 under the license https://creativecommons.org/licenses/by

• https://www.aclweb.org/anthology/W19-3217.pdf

• https://www.aclweb.org/anthology/W19-3217.pdf,

https://www.aclweb.org/anthology/W19-3217,

https://academic.microsoft.com/#/detail/2972602516 under the license cc-by