Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention

Suyu Ge, Tao Qi, Chuhan Wu, Yongfeng Huang


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.
Anthology ID:
W19-3214
Volume:
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
96–98
Language:
URL:
https://aclanthology.org/W19-3214
DOI:
10.18653/v1/W19-3214
Bibkey:
Cite (ACL):
Suyu Ge, Tao Qi, Chuhan Wu, and Yongfeng Huang. 2019. Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 96–98, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention (Ge et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-3214.pdf
Data
SMM4H