@inproceedings{ge-etal-2019-detecting,
title = "Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention",
author = "Ge, Suyu and
Qi, Tao and
Wu, Chuhan and
Huang, Yongfeng",
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/W19-3214",
doi = "10.18653/v1/W19-3214",
pages = "96--98",
abstract = "This paper describes our system for the first and second shared tasks of the fourth Social Media Mining for Health Applications (SMM4H) workshop. We enhance tweet representation with a language model and distinguish the importance of different words with Multi-Head Self-Attention. In addition, transfer learning is exploited to make up for the data shortage. Our system achieved competitive results on both tasks with an F1-score of 0.5718 for task 1 and 0.653 (overlap) / 0.357 (strict) for task 2.",
}
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%0 Conference Proceedings
%T Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention
%A Ge, Suyu
%A Qi, Tao
%A Wu, Chuhan
%A Huang, Yongfeng
%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 ge-etal-2019-detecting
%X This paper describes our system for the first and second shared tasks of the fourth Social Media Mining for Health Applications (SMM4H) workshop. We enhance tweet representation with a language model and distinguish the importance of different words with Multi-Head Self-Attention. In addition, transfer learning is exploited to make up for the data shortage. Our system achieved competitive results on both tasks with an F1-score of 0.5718 for task 1 and 0.653 (overlap) / 0.357 (strict) for task 2.
%R 10.18653/v1/W19-3214
%U https://aclanthology.org/W19-3214
%U https://doi.org/10.18653/v1/W19-3214
%P 96-98
Markdown (Informal)
[Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention](https://aclanthology.org/W19-3214) (Ge et al., ACL 2019)
ACL