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 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.pdf https://www.aclweb.org/anthology/W19-3217.pdf],
: [https://www.aclweb.org/anthology/W19-3217 https://www.aclweb.org/anthology/W19-3217],
: [https://academic.microsoft.com/#/detail/2972602516 https://academic.microsoft.com/#/detail/2972602516] under the license cc-by