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            <title>Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop &amp; Shared Task</title>
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            <namePart type="given">Graciela</namePart>
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    <abstract>The goals of the SMM4H shared tasks are to release annotated social media based health related datasets to the research community, and to compare the performances of natural language processing and machine learning systems on tasks involving these datasets. The third execution of the SMM4H shared tasks, co-hosted with EMNLP-2018, comprised of four subtasks. These subtasks involve annotated user posts from Twitter (tweets) and focus on the (i) automatic classification of tweets mentioning a drug name, (ii) automatic classification of tweets containing reports of first-person medication intake, (iii) automatic classification of tweets presenting self-reports of adverse drug reaction (ADR) detection, and (iv) automatic classification of vaccine behavior mentions in tweets. A total of 14 teams participated and 78 system runs were submitted (23 for task 1, 20 for task 2, 18 for task 3, 17 for task 4).</abstract>
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%0 Conference Proceedings
%T Overview of the Third Social Media Mining for Health (SMM4H) Shared Tasks at EMNLP 2018
%A Weissenbacher, Davy
%A Sarker, Abeed
%A Paul, Michael J.
%A Gonzalez-Hernandez, Graciela
%Y Gonzalez-Hernandez, Graciela
%Y Weissenbacher, Davy
%Y Sarker, Abeed
%Y Paul, Michael
%S Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F weissenbacher-etal-2018-overview
%X The goals of the SMM4H shared tasks are to release annotated social media based health related datasets to the research community, and to compare the performances of natural language processing and machine learning systems on tasks involving these datasets. The third execution of the SMM4H shared tasks, co-hosted with EMNLP-2018, comprised of four subtasks. These subtasks involve annotated user posts from Twitter (tweets) and focus on the (i) automatic classification of tweets mentioning a drug name, (ii) automatic classification of tweets containing reports of first-person medication intake, (iii) automatic classification of tweets presenting self-reports of adverse drug reaction (ADR) detection, and (iv) automatic classification of vaccine behavior mentions in tweets. A total of 14 teams participated and 78 system runs were submitted (23 for task 1, 20 for task 2, 18 for task 3, 17 for task 4).
%R 10.18653/v1/W18-5904
%U https://aclanthology.org/W18-5904
%U https://doi.org/10.18653/v1/W18-5904
%P 13-16

Markdown (Informal)

[Overview of the Third Social Media Mining for Health (SMM4H) Shared Tasks at EMNLP 2018](https://aclanthology.org/W18-5904) (Weissenbacher et al., EMNLP 2018)

• Overview of the Third Social Media Mining for Health (SMM4H) Shared Tasks at EMNLP 2018 (Weissenbacher et al., EMNLP 2018)

ACL

• Davy Weissenbacher, Abeed Sarker, Michael J. Paul, and Graciela Gonzalez-Hernandez. 2018. Overview of the Third Social Media Mining for Health (SMM4H) Shared Tasks at EMNLP 2018. In Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task, pages 13–16, Brussels, Belgium. Association for Computational Linguistics.