PAII-NLP at SMM4H 2021: Joint Extraction and Normalization of Adverse Drug Effect Mentions in Tweets

Zongcheng Ji, Tian Xia, Mei Han


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
Venues:
NAACL | 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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2021.smm4h-1.26.pdf