title: Identifying gene and protein mentions in text using conditional random fields.
abstract: BACKGROUND
  We present a model for tagging gene and protein mentions from text using the probabilistic sequence tagging framework of conditional random fields (CRFs). Conditional random fields model the probability P(t/o) of a tag sequence given an observation sequence directly, and have previously been employed successfully for other tagging tasks. The mechanics of CRFs and their relationship to maximum entropy are discussed in detail.
  
  
  RESULTS
  We employ a diverse feature set containing standard orthographic features combined with expert features in the form of gene and biological term lexicons to achieve a precision of 86.4% and recall of 78.7%. An analysis of the contribution of the various features of the model is provided.
authors.affiliation: Department of Computer and Information Science, University of Pennsylvania, Levine Hall, 3330 Walnut Street, Philadelphia, Pennsylvania 19104, USA. ryantm@cis.upenn.edu
authors.forename: Ryan
authors.initials: R
authors.lastname: McDonald
authors.forename: Fernando
authors.initials: F
authors.lastname: Pereira
mesh_terms: D005796:Genes; D010363:Pattern Recognition, Automated / Q000145:classification / Q000379:methods*; D011336:Probability; D011506:Proteins / Q000145:classification* / Q000235:genetics*; D018875:Vocabulary, Controlled
chemical_list: D011506:Proteins