Using weak supervision to generate training datasets from social media data: a proof of concept to identify drug mentions
Author:
Funder
National Institute on Aging
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-021-06614-2.pdf
Reference34 articles.
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2. Zhou Z-H (2017) A brief introduction to weakly supervised learning. Natl Sci Rev 5:44–53. https://doi.org/10.1093/nsr/nwx106
3. Devlin J, Chang M-W, Lee K, Toutanova K (2018) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv [cs.CL]
4. Cocos A, Fiks AG, Masino AJ (2017) Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts. J Am Med Inform Assoc 24:813–821. https://doi.org/10.1093/jamia/ocw180
5. Nikfarjam A, Sarker A, O’Connor K et al (2015) Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. J Am Med Inform Assoc 22:671–681. https://doi.org/10.1093/jamia/ocu041
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