Co-training for Extraction of Adverse Drug Reaction Mentions from Tweets

Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care. Current ADR surveillance systems are often associated with a substantial time lag before such events are officially published. On the other hand, online social media such as Twitter contain information about ADR events in real-time, much before any official reporting. Current state-of-the-art methods in ADR mention extraction use Recurrent Neural Networks (RNN), which typically need large labeled corpora. Towards this end, we propose a semi-supervised method based on co-training which can exploit a large pool of unlabeled tweets to augment the limited supervised training data, and as a result enhance the performance. Experiments with \(\sim \)0.1M tweets show that the proposed approach outperforms the state-of-the-art methods for the ADR mention extraction task by \(\sim \)5% in terms of F1 score.