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
- Editors:
- Davy Weissenbacher, Graciela Gonzalez-Hernandez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 96–98
- Language:
- URL:
- https://preview.aclanthology.org/remove-affiliations/W19-3214/
- DOI:
- 10.18653/v1/W19-3214
- 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)
- PDF:
- https://preview.aclanthology.org/remove-affiliations/W19-3214.pdf
- Data
- SMM4H