BCH-NLP at BioCreative VII Track 3: medications detection in tweets using transformer networks and multi-task learning
This work addresses medication detection in social media for biomedical informatics, representing an incremental improvement in a specific domain.
The paper tackled the problem of automatically extracting medication names from tweets, achieving a strict F1 score of 80.4, which ranked first and was over 10 points higher than the average score of all participants.
In this paper, we present our work participating in the BioCreative VII Track 3 - automatic extraction of medication names in tweets, where we implemented a multi-task learning model that is jointly trained on text classification and sequence labelling. Our best system run achieved a strict F1 of 80.4, ranking first and more than 10 points higher than the average score of all participants. Our analyses show that the ensemble technique, multi-task learning, and data augmentation are all beneficial for medication detection in tweets.