Abstract
Social media used for health applications usually contains a large amount of data posted by users, which brings various challenges to NLP, such as spoken language, spelling errors, novel/creative phrases, etc. In this paper, we describe our system submitted to SMM4H 2020: Social Media Mining for Health Applications Shared Task which consists of five sub-tasks. We participate in subtask 1, subtask 2-English, and subtask 5. Our final submitted approach is an ensemble of various fine-tuned transformer-based models. We illustrate that these approaches perform well in imbalanced datasets (For example, the class ratio is 1:10 in subtask 2), but our model performance is not good in extremely imbalanced datasets (For example, the class ratio is 1:400 in subtask 1). Finally, in subtask 1, our result is lower than the average score, in subtask 2-English, our result is higher than the average score, and in subtask 5, our result achieves the highest score. The code is available online.- Anthology ID:
- 2020.smm4h-1.10
- 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:
- 63–69
- Language:
- URL:
- https://aclanthology.org/2020.smm4h-1.10
- DOI:
- Cite (ACL):
- Yang Bai and Xiaobing Zhou. 2020. Automatic Detecting for Health-related Twitter Data with BioBERT. In Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task, pages 63–69, Barcelona, Spain (Online). Association for Computational Linguistics.
- Cite (Informal):
- Automatic Detecting for Health-related Twitter Data with BioBERT (Bai & Zhou, SMM4H 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.smm4h-1.10.pdf
- Data
- SMM4H