Mårtensson L, Hensing G. Health literacy-a heterogeneous phenomenon: a literature review. Scand J Caring Sci. 2012;26(1):151–60.
Article
PubMed
Google Scholar
Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform. 2008;17(01):128–44.
Article
Google Scholar
Storks S, Gao Q, Chai JY. Recent advances in natural language inference: a survey of benchmarks, resources, and approaches. 2019. arXiv:1904.01172.
Peters M, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L. Deep contextualized word representations. In: Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, vol 1 (Long Papers). Association for Computational Linguistics; 2018, pp. 2227–2237. https://doi.org/10.18653/v1/N18-1202. http://aclweb.org/anthology/N18-1202.
Devlin J, Chang M-W, Lee K, Toutanova K. Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, vol 1 (long and short papers). 2019, pp. 4171–4186.
Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R. ALBERT: a lite BERT for self-supervised learning of language representations. 2019. arXiv:1909.11942.
Krallinger M, Rabal O, Akhondi SA, Pérez MP, Santamaría J, Rodríguez GP, et al. Overview of the biocreative vi chemical–protein interaction track. In: Proceedings of the sixth BioCreative challenge evaluation workshop, vol 1. 2017, pp. 141–146.
Pyysalo S, Ginter F, Moen H, Salakoski T, Ananiadou S. Distributional semantics resources for biomedical text processing. 2013.
Jin Q, Dhingra B, Cohen WW, Lu X. Probing biomedical embeddings from language models. 2019. arXiv:1904.02181.
Si Y, Wang J, Xu H, Roberts K. Enhancing clinical concept extraction with contextual embeddings. J Am Med Inform Assoc. 2019;26(11):1297–304. https://doi.org/10.1093/jamia/ocz096.
Article
PubMed
PubMed Central
Google Scholar
Beltagy I, Lo K, Cohan A. SciBERT: a pretrained language model for scientific text. 2019. arXiv:1903.10676.
Peng Y, Yan S, Lu Z. Transfer learning in biomedical natural language processing: an evaluation of BERT and ELMo on ten benchmarking datasets. 2019. arXiv:1906:05474.
Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, Kang J. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. 2019. arXiv:1901.08746.
Gu Y, Tinn R, Cheng H, Lucas M, Usuyama N, Liu X, Naumann T, Gao J, Poon H. Domain-specific language model pretraining for biomedical natural language processing. 2020. arXiv preprint arXiv:2007.15779.
Yuan Z, Liu Y, Tan C, Huang S, Huang F. Improving biomedical pretrained language models with knowledge. 2021. arXiv preprint arXiv:2104.10344.
Naseem U, Khushi M, Reddy V, Rajendran S, Razzak I, Kim J. Bioalbert: a simple and effective pre-trained language model for biomedical named entity recognition. 2020. arXiv preprint arXiv:2009.09223.
Suominen H, Salanterä S, Velupillai S, Chapman WW, Savova G, Elhadad N, Pradhan S, South BR, Mowery DL, Jones GJ, et al. Overview of the share/clef ehealth evaluation lab 2013. In: International conference of the cross-language evaluation forum for European languages. Springer; 2013, pp. 212–231.
Li J, Sun Y, Johnson RJ, Sciaky D, Wei C-H, Leaman R, Davis AP, Mattingly CJ, Wiegers TC, Lu Z. Biocreative V CDR task corpus: a resource for chemical disease relation extraction. Database J Biol Databases Curation. 2016;2016:baw068.
Google Scholar
Kim, J-D, Ohta T, Tsuruoka Y, Tateisi Y, Collier N. Introduction to the bio-entity recognition task at JNLPBA. In: Proceedings of the international joint workshop on natural language processing in biomedicine and its applications. JNLPBA ’04. Association for Computational Linguistics, USA; 2004, pp. 70–75.
Gerner M, Nenadic G, Bergman CM. Linnaeus: a species name identification system for biomedical literature. BMC Bioinform. 2010;11(1):85.
Article
Google Scholar
Doundefinedan RI, Leaman R, Lu Z. NCBI disease corpus. J Biomed Inform. 2014;47(C):1–10.
Google Scholar
Pafilis E, Frankild SP, Fanini L, Faulwetter S, Pavloudi C, Vasileiadou A, Arvanitidis C, Jensen LJ. The species and organisms resources for fast and accurate identification of taxonomic names in text. PLoS ONE. 2013;8(6):1–6. https://doi.org/10.1371/journal.pone.0065390.
Article
CAS
Google Scholar
Ando RK. Biocreative II gene mention tagging system at IBM WATSON. 2007.
Herrero-Zazo M, Segura-Bedmar I, Martínez P, Declerck T. The DDI corpus: an annotated corpus with pharmacological substances and drug–drug interactions. J Biomed Inform. 2013;46(5):914–20.
Article
PubMed
Google Scholar
Uzuner Ö, South BR, Shen S, DuVall SL. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc. 2011;18(5):552–6.
Article
PubMed
PubMed Central
Google Scholar
Van Mulligen EM, Fourrier-Reglat A, Gurwitz D, Molokhia M, Nieto A, Trifiro G, Kors JA, Furlong LI. The EU-ADR corpus: annotated drugs, diseases, targets, and their relationships. J Biomed Inform. 2012;45(5):879–84.
Article
PubMed
Google Scholar
Bravo À, Piñero J, Queralt-Rosinach N, Rautschka M, Furlong LI. Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research. BMC Bioinform. 2015;16(1):1–17.
Article
Google Scholar
Soğancıoğlu G, Öztürk H, Özgür A. Biosses: a semantic sentence similarity estimation system for the biomedical domain. Bioinformatics. 2017;33(14):49–58.
Article
Google Scholar
Wang Y, Afzal N, Fu S, Wang L, Shen F, Rastegar-Mojarad M, Liu H. Medsts: a resource for clinical semantic textual similarity. Lang Resour Eval. 2020;54(1):57–72.
Article
Google Scholar
Romanov A, Shivade C. Lessons from natural language inference in the clinical domain. In: Proceedings of the 2018 conference on empirical methods in natural language processing. 2018, pp. 1586–1596.
Baker S, Silins I, Guo Y, Ali I, Högberg J, Stenius U, Korhonen A. Automatic semantic classification of scientific literature according to the hallmarks of cancer. Bioinformatics. 2016;32(3):432–40.
Article
CAS
PubMed
Google Scholar
Tsatsaronis G, Balikas G, Malakasiotis P, Partalas I, Zschunke M, Alvers MR, Weissenborn D, Krithara A, Petridis S, Polychronopoulos D, et al. An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition. BMC Bioinform. 2015;16(1):1–28.
Article
Google Scholar
Giorgi JM, Bader GD. Transfer learning for biomedical named entity recognition with neural networks. Bioinformatics. 2018;34(23):4087–94.
Article
CAS
PubMed
PubMed Central
Google Scholar
Poerner N, Waltinger U, Schütze H. Inexpensive domain adaptation of pretrained language models: case studies on biomedical NER and covid-19 QA. 2020. arXiv preprint arXiv:2004.03354.
Devlin J, Chang M-W, Lee K, Toutanova K. Bert: pre-training of deep bidirectional transformers for language understanding. 2018. arXiv preprint arXiv:1810.04805.
Chao W-L, Changpinyo S, Gong B, Sha F. An empirical study and analysis of generalized zero-shot learning for object recognition in the wild. In: European conference on computer vision. Springer; 2016, pp. 52–68
Kalyan KS, Rajasekharan A, Sangeetha S. AMMU: a survey of transformer-based biomedical pretrained language models. J Biomed Inform. 2021;126:103982.
Article
PubMed
Google Scholar