title: Exploring the boundaries: gene and protein identification in biomedical text.
abstract: BACKGROUND
  Good automatic information extraction tools offer hope for automatic processing of the exploding biomedical literature, and successful named entity recognition is a key component for such tools.
  
  
  METHODS
  We present a maximum-entropy based system incorporating a diverse set of features for identifying gene and protein names in biomedical abstracts.
  
  
  RESULTS
  This system was entered in the BioCreative comparative evaluation and achieved a precision of 0.83 and recall of 0.84 in the "open" evaluation and a precision of 0.78 and recall of 0.85 in the "closed" evaluation.
  
  
  CONCLUSIONS
  Central contributions are rich use of features derived from the training data at multiple levels of granularity, a focus on correctly identifying entity boundaries, and the innovative use of several external knowledge sources including full MEDLINE abstracts and web searches.
authors.affiliation: Department of Computer Science, Stanford University, Stanford, CA 94305-9040, USA. jrfinkel@stanford.edu
authors.forename: Jenny
authors.initials: J
authors.lastname: Finkel
authors.forename: Shipra
authors.initials: S
authors.lastname: Dingare
authors.forename: Christopher D
authors.initials: CD
authors.lastname: Manning
authors.forename: Malvina
authors.initials: M
authors.lastname: Nissim
authors.forename: Beatrice
authors.initials: B
authors.lastname: Alex
authors.forename: Claire
authors.initials: C
authors.lastname: Grover
mesh_terms: D035843:Biomedical Research / Q000145:classification* / Q000379:methods; D019295:Computational Biology / Q000145:classification / Q000379:methods; D005796:Genes; D016247:Information Storage and Retrieval / Q000145:classification / Q000379:methods; D008091:Literature; D011506:Proteins / Q000145:classification*; D009626:Terminology as Topic
chemical_list: D011506:Proteins