Abstract
This paper describes our submission to the 5th edition of the Social Media Mining for Health Applications (SMM4H) shared task 1. Task 1 aims at the automatic classification of tweets that mention a medication or a dietary supplement. This task is specifically challenging due to its highly imbalanced dataset, with only 0.2% of the tweets mentioning a drug. For our submission, we particularly focused on several pretrained encoders for text classification. We achieve an F1 score of 0.75 for the positive class on the test set.- Anthology ID:
- 2020.smm4h-1.27
- 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:
- 150–152
- Language:
- URL:
- https://aclanthology.org/2020.smm4h-1.27
- DOI:
- Cite (ACL):
- Laiba Mehnaz. 2020. Automatic Classification of Tweets Mentioning a Medication Using Pre-trained Sentence Encoders. In Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task, pages 150–152, Barcelona, Spain (Online). Association for Computational Linguistics.
- Cite (Informal):
- Automatic Classification of Tweets Mentioning a Medication Using Pre-trained Sentence Encoders (Mehnaz, SMM4H 2020)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2020.smm4h-1.27.pdf