Kahyun Lee


Ensemble BERT for Classifying Medication-mentioning Tweets
Huong Dang | Kahyun Lee | Sam Henry | Özlem Uzuner
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task

Twitter is a valuable source of patient-generated data that has been used in various population health studies. The first step in many of these studies is to identify and capture Twitter messages (tweets) containing medication mentions. In this article, we describe our submission to Task 1 of the Social Media Mining for Health Applications (SMM4H) Shared Task 2020. This task challenged participants to detect tweets that mention medications or dietary supplements in a natural, highly imbalance dataset. Our system combined a handcrafted preprocessing step with an ensemble of 20 BERT-based classifiers generated by dividing the training dataset into subsets using 10-fold cross validation and exploiting two BERT embedding models. Our system ranked first in this task, and improved the average F1 score across all participating teams by 19.07% with a precision, recall, and F1 on the test set of 83.75%, 87.01%, and 85.35% respectively.