NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding

NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding

Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades1,2, the most dramatic advances in MR have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with...

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Main Authors: Alachram, H. (Author), Ambite, J.L (Author), Ananiadou, S. (Author), Beißbarth, T. (Author), Chambers, B. (Author), Christopoulou, F. (Author), Evans, J.A (Author), Galstyan, A. (Author), Gao, X. (Author), Garg, S. (Author), Hermjakob, U. (Author), Khomtchouk, B.B (Author), King, R. (Author), Li, M. (Author), Li, Y. (Author), Marcu, D. (Author), Matthew, J. (Author), Pan, W. (Author), Rzhetsky, A. (Author), Schoene, A.M (Author), Sheng, E. (Author), Soldatova, L. (Author), Stevens, R. (Author), Wang, K. (Author), Wingender, E. (Author)
Format: Article
Language:English
Published: Nature Research 2021
Subjects:
article
biochemistry
embedding
extraction
human
molecular biology
ontology
practice guideline
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