HITSZ-ICRC: A Report for SMM4H Shared Task 2020-Automatic Classification of Medications and Adverse Effect in Tweets

Xiaoyu Zhao, Ying Xiong, Buzhou Tang


Abstract
This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fifth Social Media Mining for Health Applications (SMM4H) shared task in 2020. The first task is automatic classification of tweets that mention medications and the second task is automatic classification of tweets in English that report adverse effects. The system we propose for these tasks is based on bidirectional encoder representations from transformers (BERT) incorporating with knowledge graph and retrieving evidence from online information. Our system achieves an F1 of 0.7553 in task 1 and an F1 of 0.5455 in task 2.
Anthology ID:
2020.smm4h-1.26
Volume:
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venues:
COLING | SMM4H
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
146–149
Language:
URL:
https://www.aclweb.org/anthology/2020.smm4h-1.26
DOI:
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/2020.smm4h-1.26.pdf