A PubMedBERT-based Classifier with Data Augmentation Strategy for Detecting Medication Mentions in Tweets
This work addresses the need for accurate named entity recognition in social media data for public health applications, representing an incremental improvement over existing methods.
The paper tackled the problem of detecting medication mentions in tweets, a challenging task due to nonstandard expressions and class imbalance, by using a PubMedBERT-based classifier with data augmentation, achieving an F1 score of 0.762, which is higher than the mean submission score of 0.696.
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).