P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, Enriching word vectors with subword information, Transactions of the Association for Computational Linguistics, vol.5, pp.135-146, 2017.

J. P. Chiu and E. Nichols, Named entity recognition with bidirectional lstm-cnns, Transactions of the Association for Computational Linguistics, vol.4, pp.357-370, 2016.

. Delft, , 2018.

J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, 2018.

M. Ehrmann, M. Romanello, A. Flückiger, S. Clematide, A. Arampatzis et al., Overview of CLEF HIPE 2020: Named Entity Recognition and Linking on Historical Newspapers, Experimental IR Meets Multilinguality, Multimodality, and Interaction. Proceedings of the 11th International Conference of the CLEF Association (CLEF 2020). Lecture Notes in Computer Science (LNCS), vol.12260, 2020.

L. Foppiano and L. Romary, entity-fishing: a DARIAH entity recognition and disambiguation service, Digital Scholarship in the Humanities, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01812100

M. Habibi, L. Weber, M. Neves, D. L. Wiegandt, and U. Leser, Deep learning with word embeddings improves biomedical named entity recognition, Bioinformatics, vol.33, issue.14, pp.37-48, 2017.

G. Lample, M. Ballesteros, S. Subramanian, K. Kawakami, and C. Dyer, Neural architectures for named entity recognition, 2016.

G. Luo, X. Huang, C. Y. Lin, and Z. Nie, Joint entity recognition and disambiguation, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp.879-888, 2015.

X. Ma and E. Hovy, End-to-end sequence labeling via bi-directional lstm-cnns-crf, 2016.

T. Mikolov, E. Grave, P. Bojanowski, C. Puhrsch, and A. Joulin, Advances in pretraining distributed word representations, 2017.

D. B. Nguyen, J. Hoffart, M. Theobald, and G. Weikum, Aida-light: High-throughput named-entity disambiguation, LDOW, vol.1184, 2014.

A. Passos, V. Kumar, and A. Mccallum, Lexicon infused phrase embeddings for named entity resolution, 2014.

J. Pennington, R. Socher, and C. D. Manning, Glove: Global vectors for word representation, Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp.1532-1543, 2014.

M. E. Peters, W. Ammar, C. Bhagavatula, and R. Power, Semi-supervised sequence tagging with bidirectional language models, 2017.

M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark et al., Deep contextualized word representations, 2018.

S. Pradhan, A. Moschitti, N. Xue, O. Uryupina, and Y. Zhang, Conll-2012 shared task: Modeling multilingual unrestricted coreference in ontonotes, Joint Conference on EMNLP and CoNLL-Shared Task, pp.1-40, 2012.

L. Ratinov and D. Roth, Design challenges and misconceptions in named entity recognition, Proceedings of the Thirteenth Conference on Computational Natural Language Learning, pp.147-155, 2009.

B. Sagot, M. Richard, and R. Stern, Annotation référentielle du Corpus Arboré de Paris 7 en entités nommées

, Traitement Automatique des Langues Naturelles (TALN). Actes de la conférence conjointe JEP-TALN-RECITAL 2012, vol.2, 2012.

E. F. Sang and F. De-meulder, Introduction to the conll-2003 shared task: Languageindependent named entity recognition, 2003.

C. N. Santos and V. Guimaraes, Boosting named entity recognition with neural character embeddings, 2015.