Y. Bengio, P. Simard, and P. Frasconi, Learning long-term dependencies with gradient descent is difficult, IEEE Transactions on Neural Networks, pp.157-166, 1994.
DOI : 10.1109/72.279181

URL : http://www.research.microsoft.com/~patrice/PDF/long_term.pdf

P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, Enriching word vectors with subword information, 2016.

J. P. Chiu and E. Nichols, Named entity recognition with bidirectional lstm-cnns. arXiv preprint, 2015.

A. Graves and J. Schmidhuber, Framewise phoneme classification with bidirectional LSTM networks, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., pp.2047-2052, 2005.
DOI : 10.1109/IJCNN.2005.1556215

URL : http://www6.in.tum.de/pub/Main/Publications/Graves2005a.pdf

H. Gurulingappa, A. Mateen-rajpu, and L. Toldo, Extraction of potential adverse drug events from medical case reports, Journal of Biomedical Semantics, vol.3, issue.1, p.15, 2012.
DOI : 10.1177/0165551509360123

URL : https://jbiomedsem.biomedcentral.com/track/pdf/10.1186/2041-1480-3-15?site=jbiomedsem.biomedcentral.com

T. Huynh, Y. He, A. Willis, and S. Rüger, Adverse drug reaction classification with deep neural networks, Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp.877-887, 2016.

A. Jagannatha and H. Yu, Structured prediction models for RNN based sequence labeling in clinical text, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, p.856, 2016.
DOI : 10.18653/v1/D16-1082

URL : https://doi.org/10.18653/v1/d16-1082

A. N. Jagannatha and H. Yu, Bidirectional RNN for Medical Event Detection in Electronic Health Records, Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, p.473, 2016.
DOI : 10.18653/v1/N16-1056

URL : http://europepmc.org/articles/pmc5119627?pdf=render

G. Lample, M. Ballesteros, S. Subramanian, K. Kawakami, and C. Dyer, Learning longterm dependencies with gradient descent is difficult, 2016.

Z. Liu, M. Yang, X. Wang, Q. Chen, B. Tang et al., Entity recognition from clinical texts via recurrent neural network, BMC Medical Informatics and Decision Making, vol.86, issue.18, p.67, 2017.
DOI : 10.1109/5.726791

URL : https://bmcmedinformdecismak.biomedcentral.com/track/pdf/10.1186/s12911-017-0468-7?site=bmcmedinformdecismak.biomedcentral.com

M. Liwicki, A. Graves, S. Fernàndez, H. Bunke, and J. Schmidhuber, A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks, Proceedings of the 9th International Conference on Document Analysis and Recognition, 2007.

A. Névéol, C. Grouin, J. Leixa, S. Rosset, and P. Zweigenbaum, The quaero french medical corpus: A ressource for medical entity recognition and normalization, Proc BioTextM, 2014.

A. Névéol, C. Grouin, X. Tannier, T. Hamon, L. Kelly et al., Clef ehealth evaluation lab 2015 task 1b: Clinical named entity recognition, CLEF (Working Notes), 2015.

A. Nikfarjam and G. H. Gonzalez, Pattern mining for extraction of mentions of adverse drug reactions from user comments, AMIA Annual Symposium Proceedings, p.1019, 2011.

A. Nikfarjam, A. Sarker, K. O-'connor, R. Ginn, and G. Gonzalez, Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features, Journal of the American Medical Informatics Association, vol.7, issue.7, pp.671-681, 2015.
DOI : 10.1186/1471-2105-7-92

URL : https://academic.oup.com/jamia/article-pdf/22/3/671/5245252/ocu041.pdf

A. Sarker, R. Ginn, A. Nikfarjam, K. O-'connor, K. Smith et al., Utilizing social media data for pharmacovigilance: A review, Journal of Biomedical Informatics, vol.54, pp.202-212, 2015.
DOI : 10.1016/j.jbi.2015.02.004

URL : https://doi.org/10.1016/j.jbi.2015.02.004

H. Yu, A. Jagannatha, F. Liu, and W. Liu, Nlp challenges for detecting medication and adverse drug events from electronic health records. https://bionlp .org/index, p.39, 2018.