@inproceedings{vydiswaran-etal-2019-towards,
title = "Towards Text Processing Pipelines to Identify Adverse Drug Events-related Tweets: {U}niversity of {M}ichigan @ {SMM}4{H} 2019 Task 1",
author = "Vydiswaran, V.G.Vinod and
Ganzel, Grace and
Romas, Bryan and
Yu, Deahan and
Austin, Amy and
Bhomia, Neha and
Chan, Socheatha and
Hall, Stephanie and
Le, Van and
Miller, Aaron and
Oduyebo, Olawunmi and
Song, Aulia and
Sondhi, Radhika and
Teng, Danny and
Tseng, Hao and
Vuong, Kim and
Zimmerman, Stephanie",
editor = "Weissenbacher, Davy and
Gonzalez-Hernandez, Graciela",
booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3217",
doi = "10.18653/v1/W19-3217",
pages = "107--109",
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.",
}
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%0 Conference Proceedings
%T Towards Text Processing Pipelines to Identify Adverse Drug Events-related Tweets: University of Michigan @ SMM4H 2019 Task 1
%A Vydiswaran, V.G.Vinod
%A Ganzel, Grace
%A Romas, Bryan
%A Yu, Deahan
%A Austin, Amy
%A Bhomia, Neha
%A Chan, Socheatha
%A Hall, Stephanie
%A Le, Van
%A Miller, Aaron
%A Oduyebo, Olawunmi
%A Song, Aulia
%A Sondhi, Radhika
%A Teng, Danny
%A Tseng, Hao
%A Vuong, Kim
%A Zimmerman, Stephanie
%Y Weissenbacher, Davy
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F vydiswaran-etal-2019-towards
%X 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.
%R 10.18653/v1/W19-3217
%U https://aclanthology.org/W19-3217
%U https://doi.org/10.18653/v1/W19-3217
%P 107-109
Markdown (Informal)
[Towards Text Processing Pipelines to Identify Adverse Drug Events-related Tweets: University of Michigan @ SMM4H 2019 Task 1](https://aclanthology.org/W19-3217) (Vydiswaran et al., ACL 2019)
ACL
- 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, and Stephanie Zimmerman. 2019. Towards Text Processing Pipelines to Identify Adverse Drug Events-related Tweets: University of Michigan @ SMM4H 2019 Task 1. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 107–109, Florence, Italy. Association for Computational Linguistics.