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We introduce CoWeSe (the Corpus Web Salud Espa\~nol), the largest Spanish biomedical corpus to date, consisting of 4.5GB (about 750M tokens) of clean plain text.
Improving language understanding by generative pre-training
Alec Radford and Karthik Narasimhan · 2018
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SciBERT: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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PharmaCoNER: Pharmacological substances, compounds and proteins named entity recognition track
Aitor Gonzalez-Agirre, Montserrat Marimon, Ander Intxaurrondo, Obdulia Rabal, Marta Villegas, and Martin Krallinger · 2019
Earlier work this paper cites.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang · 2019
Cited alongside, same era.
Transfer learning in natural language processing
Sebastian Ruder, Matthew E. Peters, Swabha Swayamdipta, and Thomas Wolf · 2019
Cited alongside, same era.
Medical word embeddings for Spanish: Development and evaluation
Felipe Soares, Marta Villegas, Aitor Gonzalez-Agirre, Martin Krallinger, and Jordi Armengol-Estapé · 2019
Cited alongside, same era.
Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results
A Miranda-Escalada, E Farré, and M Krallinger · 2020
Later among the works it cites.
A monolingual approach to contextualized word embeddings for mid-resource languages
Pedro Javier Ortiz Suárez, Laurent Romary, and Benoît Sagot · 2020
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Are multilingual models the best choice for moderately under-resourced languages? A comprehensive assessment for Catalan
Jordi Armengol-Estapé, Casimiro Pio Carrino, Carlos Rodriguez-Penagos, Ona de Gibert Bonet, Carme Armentano-Oller, Aitor Gonzalez-Agirre, Maite Melero, and Marta Villegas · 2021
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