title: Cascaded classifiers for confidence-based chemical named entity recognition.
abstract: BACKGROUND
  Chemical named entities represent an important facet of biomedical text.
  
  
  RESULTS
  We have developed a system to use character-based n-grams, Maximum Entropy Markov Models and rescoring to recognise chemical names and other such entities, and to make confidence estimates for the extracted entities. An adjustable threshold allows the system to be tuned to high precision or high recall. At a threshold set for balanced precision and recall, we were able to extract named entities at an F score of 80.7% from chemistry papers and 83.2% from PubMed abstracts. Furthermore, we were able to achieve 57.6% and 60.3% recall at 95% precision, and 58.9% and 49.1% precision at 90% recall.
  
  
  CONCLUSIONS
  These results show that chemical named entities can be extracted with good performance, and that the properties of the extraction can be tuned to suit the demands of the task.
authors.affiliation: Unilever Centre For Molecular Science Informatics, Chemical Laboratory, University of Cambridge, CB21EW UK. ptc24@cam.ac.uk
authors.forename: Peter
authors.initials: P
authors.lastname: Corbett
authors.forename: Ann
authors.initials: A
authors.lastname: Copestake
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008956:Models, Chemical; D015233:Models, Statistical; D009323:Natural Language Processing; D012984:Software; D009626:Terminology as Topic