@inproceedings{wu-etal-2018-detecting,
title = "Detecting Tweets Mentioning Drug Name and Adverse Drug Reaction with Hierarchical Tweet Representation and Multi-Head Self-Attention",
author = "Wu, Chuhan and
Wu, Fangzhao and
Liu, Junxin and
Wu, Sixing and
Huang, Yongfeng and
Xie, Xing",
editor = "Gonzalez-Hernandez, Graciela and
Weissenbacher, Davy and
Sarker, Abeed and
Paul, Michael",
booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5909",
doi = "10.18653/v1/W18-5909",
pages = "34--37",
abstract = "This paper describes our system for the first and third shared tasks of the third Social Media Mining for Health Applications (SMM4H) workshop, which aims to detect the tweets mentioning drug names and adverse drug reactions. In our system we propose a neural approach with hierarchical tweet representation and multi-head self-attention (HTR-MSA) for both tasks. Our system achieved the first place in both the first and third shared tasks of SMM4H with an F-score of 91.83{\%} and 52.20{\%} respectively.",
}
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<abstract>This paper describes our system for the first and third shared tasks of the third Social Media Mining for Health Applications (SMM4H) workshop, which aims to detect the tweets mentioning drug names and adverse drug reactions. In our system we propose a neural approach with hierarchical tweet representation and multi-head self-attention (HTR-MSA) for both tasks. Our system achieved the first place in both the first and third shared tasks of SMM4H with an F-score of 91.83% and 52.20% respectively.</abstract>
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%0 Conference Proceedings
%T Detecting Tweets Mentioning Drug Name and Adverse Drug Reaction with Hierarchical Tweet Representation and Multi-Head Self-Attention
%A Wu, Chuhan
%A Wu, Fangzhao
%A Liu, Junxin
%A Wu, Sixing
%A Huang, Yongfeng
%A Xie, Xing
%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 wu-etal-2018-detecting
%X This paper describes our system for the first and third shared tasks of the third Social Media Mining for Health Applications (SMM4H) workshop, which aims to detect the tweets mentioning drug names and adverse drug reactions. In our system we propose a neural approach with hierarchical tweet representation and multi-head self-attention (HTR-MSA) for both tasks. Our system achieved the first place in both the first and third shared tasks of SMM4H with an F-score of 91.83% and 52.20% respectively.
%R 10.18653/v1/W18-5909
%U https://aclanthology.org/W18-5909
%U https://doi.org/10.18653/v1/W18-5909
%P 34-37
Markdown (Informal)
[Detecting Tweets Mentioning Drug Name and Adverse Drug Reaction with Hierarchical Tweet Representation and Multi-Head Self-Attention](https://aclanthology.org/W18-5909) (Wu et al., EMNLP 2018)
ACL