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

ABSTRACT: Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades^1,2, 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 scientifi

SUBMITTER: Wang K 

PROVIDER: S-EPMC8528865 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature