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48.1 Practical Aspects of Computational Linguistics in the Biomedical Domain: or, Biomedical Natural Language Processing 48.1 Practical Aspects of Computational Linguistics in the Biomedical Domain: or, Biomedical Natural Language Processing
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48.2 User Types and Use Cases in Biomedical Natural Language Processing 48.2 User Types and Use Cases in Biomedical Natural Language Processing
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48.2.1 Clinicians 48.2.1 Clinicians
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48.2.2 Bench Scientists 48.2.2 Bench Scientists
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48.2.3 Database Curators 48.2.3 Database Curators
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48.3 Foundational challenges in BioNLP 48.3 Foundational challenges in BioNLP
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48.3.1 Named Entity Recognition 48.3.1 Named Entity Recognition
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48.3.2 Concept Normalization 48.3.2 Concept Normalization
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48.4 Textual Genres and Text Sources 48.4 Textual Genres and Text Sources
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48.5 Biomedical Sublanguages 48.5 Biomedical Sublanguages
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48.6 Multilinguality 48.6 Multilinguality
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48.7 Shared tasks in BioNLP 48.7 Shared tasks in BioNLP
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48.8 Increased Necessity for Software Testing and Quality Assurance 48.8 Increased Necessity for Software Testing and Quality Assurance
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48.9 Data Availability 48.9 Data Availability
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48.9.1 Annotation Research Within the Biomedical Natural Language Processing Community 48.9.1 Annotation Research Within the Biomedical Natural Language Processing Community
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48.10 Engineering Issues: Architectures and Scaling 48.10 Engineering Issues: Architectures and Scaling
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48.11 Conclusions—and the Future 48.11 Conclusions—and the Future
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Further Reading and Relevant Resources Further Reading and Relevant Resources
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Acknowledgements Acknowledgements
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References References
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48 Natural Language Processing for Biomedical Texts
Get accessKevin B. Cohen received his PhD from the University of Colorado, Boulder in 2010. He began his career in patient care, primarily working with people with heart and lung disease, and his linguistic research has primarily dealt with biomedical language. He is the Director of the Biomedical Text Mining Group in the Computational Bioscience Program at the University of Colorado School of Medicine, and the emeritus d’Alembert Chair in Natural Language Processing for the Biomedical Domain at the Université Paris-Saclay.
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Published:14 April 2021
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Abstract
Computational linguistics has its origins in the post-Second World War research on translation of Russian-language scientific journal articles in the United States. Today, biomedical natural language processing treats clinical data, the scientific literature, and social media, with use cases ranging from studying adverse effects of drugs to interpreting high-throughput genomic assays (Névéol and Zweigenbaum 2018). Many of the most prominent research areas in the field involve extracting information from text and normalizing it to enormous databases of domain-relevant semantic classes, such as genes, diseases, and biological processes. Moving forward, the field is expected to play a significant role in understanding reproducibility in natural language processing.
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