Abstract

Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades,, the most dramatic advances in MR have followed in the wake of critical corpus development. Large, well-annotated corpora have been associated with punctuated advances in MR methodology and automated knowledge extraction systems in the same way that ImageNet 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 …

Date

October 20, 2021

Authors

Kanix Wang, Robert Stevens, Halima Alachram, Yu Li, Larisa Soldatova, Ross King, Sophia Ananiadou, Annika M Schoene, Maolin Li, Fenia Christopoulou, José Luis Ambite, Joel Matthew, Sahil Garg, Ulf Hermjakob, Daniel Marcu, Emily Sheng, Tim Beißbarth, Edgar Wingender, Aram Galstyan, Xin Gao, Brendan Chambers, Weidi Pan, Bohdan B Khomtchouk, James A Evans, Andrey Rzhetsky

Journal

NPJ systems biology and applications

Volume

7

Issue

1

Pages

38

Publisher

Nature Publishing Group UK