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

arXiv: https://arxiv.org/abs/1802.05121 · HTML: https://mlsanity.com/paper/1802.05121/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2018.

## Built on by (most cited first, 2 of 2)

- [An analysis of full-size Russian complexly NER labelled corpus of Internet user reviews on the drugs based on deep learning and language neural nets](https://mlsanity.com/paper/2105.00059/) (2021)
- [Selection of pseudo-annotated data for adverse drug reaction classification across drug groups](https://mlsanity.com/paper/2111.12477/) (2021)

## Abstract (arXiv)

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 0.1M tweets show that the proposed approach outperforms the state-of-the-art methods for the ADR mention extraction task by 5% in terms of F1 score.