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Despite their general capabilities, LLMs still struggle on biomedical NER tasks, which are difficult due to the presence of specialized terminology and lack of training data.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Medmentions: A large biomedical corpus annotated with umls concepts
Sunil Mohan and Donghui Li. 2019 · 1902
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The unified medical language system (umls): integrating biomedical terminology
Olivier Bodenreider. 2004 · 2004
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Ncbi disease corpus: a resource for disease name recognition and concept normalization
Rezarta Islamaj Doğan, Robert Leaman, and Zhiyong Lu. 2014 · 2014
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Biocreative V CDR task corpus: a resource for chemical disease relation extraction
Jiao Li, Yueping Sun, Robin J. Johnson, Daniela Sciaky, Chih-Hsuan Wei, Robert Leaman, Allan Peter Davis, Carolyn J. Mattingly, Thomas C. Wiegers, and Zhiyong Lu. 2016 · 2016
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spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing
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Overview of the biocreative vi chemical-protein interaction track
Martin Krallinger, Obdulia Rabal, Saber A Akhondi, Martın Pérez Pérez, Jesús Santamaría, Gael Pérez Rodríguez, Georgios Tsatsaronis, Ander Intxaurrondo, José Antonio López, Umesh Nandal, et al. 2017 · 2017
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A corpus with multi-level annotations of patients, interventions and outcomes to support language processing for medical literature
Benjamin Nye, Junyi Jessy Li, Roma Patel, Yinfei Yang, Iain Marshall, Ani Nenkova, and Byron Wallace. 2018 · 2018
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Mark Neumann, Daniel King, Iz Beltagy, and Waleed Ammar. 2019 · 2019
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True few-shot learning with language models
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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