Abstract
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.
M. Gupta is also a Principal Applied Scientist at Microsoft.
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Gupta, S., Gupta, M., Varma, V., Pawar, S., Ramrakhiyani, N., Palshikar, G.K. (2018). Co-training for Extraction of Adverse Drug Reaction Mentions from Tweets. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_44
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DOI: https://doi.org/10.1007/978-3-319-76941-7_44
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