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The BioCreative VII Track 3 challenge focused on the identification ofmedication names in Twitter user timelines. For our submission to thischallenge, we expanded the available training data by using several dataaugmentation techniques. The augmented data was then used to fine-tune anensemble of language models that had been pre-trained on general-domain Twittercontent. The proposed approach outperformed the prior state-of-the-artalgorithm Kusuri and ranked high in the competition for our selected objectivefunction, overlapping F1 score.

Extraction of Medication Names from Twitter Using Augmentation and an Ensemble of Language Models