PAII-NLP at SMM4H 2021: Joint Extraction and Normalization of Adverse Drug Effect Mentions in Tweets
Abstract
This paper describes our system developed for the subtask 1c of the sixth Social Media Mining for Health Applications (SMM4H) shared task in 2021. The aim of the subtask is to recognize the adverse drug effect (ADE) mentions from tweets and normalize the identified mentions to their mapping MedDRA preferred term IDs. Our system is based on a neural transition-based joint model, which is to perform recognition and normalization simultaneously. Our final two submissions outperform the average F1 score by 1-2%.- Anthology ID:
- 2021.smm4h-1.26
- Volume:
- Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
- Month:
- June
- Year:
- 2021
- Address:
- Mexico City, Mexico
- Editors:
- Arjun Magge, Ari Klein, Antonio Miranda-Escalada, Mohammed Ali Al-garadi, Ilseyar Alimova, Zulfat Miftahutdinov, Eulalia Farre-Maduell, Salvador Lima Lopez, Ivan Flores, Karen O'Connor, Davy Weissenbacher, Elena Tutubalina, Abeed Sarker, Juan M Banda, Martin Krallinger, Graciela Gonzalez-Hernandez
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 126–127
- Language:
- URL:
- https://aclanthology.org/2021.smm4h-1.26
- DOI:
- 10.18653/v1/2021.smm4h-1.26
- Cite (ACL):
- Zongcheng Ji, Tian Xia, and Mei Han. 2021. PAII-NLP at SMM4H 2021: Joint Extraction and Normalization of Adverse Drug Effect Mentions in Tweets. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 126–127, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- PAII-NLP at SMM4H 2021: Joint Extraction and Normalization of Adverse Drug Effect Mentions in Tweets (Ji et al., SMM4H 2021)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.smm4h-1.26.pdf