Extraction of Medication Names from Twitter Using Augmentation and an Ensemble of Language Models
This work addresses the extraction of medication names from social media data, which is important for public health monitoring, but it is incremental as it builds on existing methods with data augmentation and ensemble techniques.
The paper tackled the problem of identifying medication names in Twitter timelines by using data augmentation and an ensemble of language models, achieving high ranking in the BioCreative VII Track 3 challenge and outperforming the prior state-of-the-art algorithm Kusuri.
The BioCreative VII Track 3 challenge focused on the identification of medication names in Twitter user timelines. For our submission to this challenge, we expanded the available training data by using several data augmentation techniques. The augmented data was then used to fine-tune an ensemble of language models that had been pre-trained on general-domain Twitter content. The proposed approach outperformed the prior state-of-the-art algorithm Kusuri and ranked high in the competition for our selected objective function, overlapping F1 score.