Improving classification of low-resource COVID-19 literature by using Named Entity Recognition.

ABSTRACT: Automatic document classification for highly interrelated classes is a demanding task that becomes more challenging when there is little labeled data for training. Such is the case of the coronavirus disease 2019 (COVID-19) Clinical repository-a repository of classified and translated academic articles related to COVID-19 and relevant to the clinical practice-where a 3-way classification scheme is being applied to COVID-19 literature. During the 7th Biomedical Linked Annotation Hackathon (BLAH7) hackathon, we performed experiments to explore the use of named-entity-recognition (NER) to improve the classification. We processed the literature with OntoGene's Biomedical Entity Recogniser (OGER) and used the resulting identified Named Entities (NE) and their links to major biological databases

SUBMITTER: Lithgow-Serrano O 

PROVIDER: S-EPMC8510872 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature