title: ChemSpot: a hybrid system for chemical named entity recognition.
abstract: BACKGROUND
  The accurate identification of chemicals in text is important for many applications, including computer-assisted reconstruction of metabolic networks or retrieval of information about substances in drug development. But due to the diversity of naming conventions and traditions for such molecules, this task is highly complex and should be supported by computational tools.
  
  
  RESULTS
  We present ChemSpot, a named entity recognition (NER) tool for identifying mentions of chemicals in natural language texts, including trivial names, drugs, abbreviations, molecular formulas and International Union of Pure and Applied Chemistry entities. Since the different classes of relevant entities have rather different naming characteristics, ChemSpot uses a hybrid approach combining a Conditional Random Field with a dictionary. It achieves an F(1) measure of 68.1% on the SCAI corpus, outperforming the only other freely available chemical NER tool, OSCAR4, by 10.8 percentage points.
  
  
  BACKGROUND
  ChemSpot is freely available at: http://www.informatik.hu-berlin.de/wbi/resources.
authors.affiliation: Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, 12489 Berlin, Germany.
authors.forename: Tim
authors.initials: T
authors.lastname: Rocktäschel
authors.forename: Michael
authors.initials: M
authors.lastname: Weidlich
authors.forename: Ulf
authors.initials: U
authors.lastname: Leser
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D004015:Dictionaries, Chemical as Topic; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D004364:Pharmaceutical Preparations / Q000145:classification*; D012984:Software; D009626:Terminology as Topic
chemical_list: D004364:Pharmaceutical Preparations