  As a major social media platform, Twitter publishes a large number of
user-generated text (tweets) on a daily basis. Mining such data can be used to
address important social, public health, and emergency management issues that
are infeasible through other means. An essential step in many text mining
pipelines is named entity recognition (NER), which presents some special
challenges for tweet data. Among them are nonstandard expressions, extreme
imbalanced classes, and lack of context information, etc. The track 3 of
BioCreative challenge VII (BC7) was organized to evaluate methods for detecting
medication mentions in tweets. In this paper, we report our work on BC7 track
3, where we explored a PubMedBERT-based classifier trained with a combination
of multiple data augmentation approaches. Our method achieved an F1 score of
0.762, which is substantially higher than the mean of all submissions (0.696).
