NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding
Journal article, 2021
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 punctuated advances in MR methodology and automated knowledge extraction systems in the same way that ImageNet4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named entity analysis tool for biomedicine: (a) a new, Named Entity Recognition Ontology (NERO) developed specifically for describing textual entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named entity recognition (NER) automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus.
Author
Kanix Wang
University of Chicago
Robert Stevens
University of Manchester
Halima Alachram
University of Göttingen
Yu Li
King Abdullah University of Science and Technology (KAUST)
Larisa N. Soldatova
Goldsmiths, University of London
Ross King
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Alan Turing Institute
University of Cambridge
Sophia Ananiadou
University of Manchester
Annika M. Schoene
University of Manchester
Maolin Li
University of Manchester
Fenia Christopoulou
University of Manchester
José Luis Ambite
Information Sciences Institute
Joel Matthew
Information Sciences Institute
Sahil Garg
Information Sciences Institute
Ulf Hermjakob
Information Sciences Institute
Daniel Marcu
Information Sciences Institute
Emily Sheng
Information Sciences Institute
Tim Beißbarth
University of Göttingen
Edgar Wingender
geneXplain GmbH
Aram Galstyan
Information Sciences Institute
Xin Gao
King Abdullah University of Science and Technology (KAUST)
Brendan Chambers
University of Chicago
Weidi Pan
University of Chicago
Bohdan B. Khomtchouk
University of Chicago
James A. Evans
University of Chicago
Andrey Rzhetsky
University of Chicago
npj Systems Biology and Applications
20567189 (eISSN)
Vol. 7 1 38Subject Categories
Language Technology (Computational Linguistics)
Embedded Systems
Computer Systems
DOI
10.1038/s41540-021-00200-x