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)
- Editors:
- Graciela Gonzalez-Hernandez, Ari Z. Klein, Ivan Flores, Davy Weissenbacher, Arjun Magge, Karen O'Connor, Abeed Sarker, Anne-Lyse Minard, Elena Tutubalina, Zulfat Miftahutdinov, Ilseyar Alimova
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 146–149
- Language:
- URL:
- https://aclanthology.org/2020.smm4h-1.26
- DOI:
- Cite (ACL):
- Xiaoyu Zhao, Ying Xiong, and Buzhou Tang. 2020. HITSZ-ICRC: A Report for SMM4H Shared Task 2020-Automatic Classification of Medications and Adverse Effect in Tweets. In Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task, pages 146–149, Barcelona, Spain (Online). Association for Computational Linguistics.
- Cite (Informal):
- HITSZ-ICRC: A Report for SMM4H Shared Task 2020-Automatic Classification of Medications and Adverse Effect in Tweets (Zhao et al., SMM4H 2020)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2020.smm4h-1.26.pdf