Background: This paper describes and evaluates a sentence selection engine that extracts a GeneRiF (Gene Reference into Functions) as defined in ENTREZ-Gene based on a MEDLINE record. Inputs for this task include both a gene and a pointer to a MEDLINE reference. In the suggested approach we merge two independent sentence extraction strategies. The first proposed strategy (LASt) uses argumentative features, inspired by discourse-analysis models. The second extraction scheme (GOEx) uses an automatic text categorizer to estimate the density of Gene Ontology categories in every sentence; thus providing a full ranking of all possible candidate GeneRiFs. A combination of the two approaches is proposed, which also aims at reducing the size of the selected segment by filtering out non-content bearing rhetorical phrases.
REF combined argumentative features using discourse-analysis models (LASt) and an automatic text categorizer to estimate the density of Gene Ontology categories (GOEx).
Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction
BMC Bioinformatics
BMC Bioinformatics
Computer Science Medicine