title: Detection of interaction articles and experimental methods in biomedical literature.
abstract: BACKGROUND
  This article describes the approaches taken by the OntoGene group at the University of Zurich in dealing with two tasks of the BioCreative III competition: classification of articles which contain curatable protein-protein interactions (PPI-ACT) and extraction of experimental methods (PPI-IMT).
  
  
  RESULTS
  Two main achievements are described in this paper: (a) a system for document classification which crucially relies on the results of an advanced pipeline of natural language processing tools; (b) a system which is capable of detecting all experimental methods mentioned in scientific literature, and listing them with a competitive ranking (AUC iP/R > 0.5).
  
  
  CONCLUSIONS
  The results of the BioCreative III shared evaluation clearly demonstrate that significant progress has been achieved in the domain of biomedical text mining in the past few years. Our own contribution, together with the results of other participants, provides evidence that natural language processing techniques have become by now an integral part of advanced text mining approaches.
authors.affiliation: Institute of Computational Linguistics, University of Zurich, 8050 Zurich, Switzerland.
authors.forename: Gerold
authors.initials: G
authors.lastname: Schneider
authors.forename: Simon
authors.initials: S
authors.lastname: Clematide
authors.forename: Fabio
authors.initials: F
authors.lastname: Rinaldi
mesh_terms: D019295:Computational Biology / Q000379:methods; D057225:Data Mining / Q000379:methods*; D009323:Natural Language Processing; D025941:Protein Interaction Mapping / Q000379:methods; D011506:Proteins / Q000378:metabolism*
chemical_list: D011506:Proteins