title: Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction.
abstract: 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.
  
  
  RESULTS
  Based on the TREC-2003 Genomics collection for GeneRiF identification, the LASt extraction strategy is already competitive (52.78%). When used in a combined approach, the extraction task clearly shows improvement, achieving a Dice score of over 57% (+10%).
  
  
  CONCLUSIONS
  Argumentative representation levels and conceptual density estimation using Gene Ontology contents appear complementary for functional annotation in proteomics.
authors.affiliation: University and Hospitals of Geneva, Geneva, Switzerland.
authors.forename: Julien
authors.initials: J
authors.lastname: Gobeill
authors.forename: Imad
authors.initials: I
authors.lastname: Tbahriti
authors.forename: Frédéric
authors.initials: F
authors.lastname: Ehrler
authors.forename: Anaïs
authors.initials: A
authors.lastname: Mottaz
authors.forename: Anne-Lise
authors.initials: AL
authors.lastname: Veuthey
authors.forename: Patrick
authors.initials: P
authors.lastname: Ruch
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005796:Genes / Q000235:genetics*; D016239:MEDLINE; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000145:classification* / Q000235:genetics*; D012680:Sensitivity and Specificity; D009626:Terminology as Topic; D018875:Vocabulary, Controlled
chemical_list: D011506:Proteins