title: Distinguishing the species of biomedical named entities for term identification.
abstract: BACKGROUND
  Term identification is the task of grounding ambiguous mentions of biomedical named entities in text to unique database identifiers. Previous work on term identification has focused on studying species-specific documents. However, full-length articles often describe entities across a number of species, in which case resolving the ambiguity of model organisms in entities is critical to achieving accurate term identification.
  
  
  RESULTS
  We developed and compared a number of rule-based and machine-learning based approaches to resolving species ambiguity in mentions of biomedical named entities, and demonstrated that a hybrid method achieved the best overall accuracy at 71.7%, as tested on the gold-standard ITI-TXM corpora. By utilising the species information predicted by the hybrid tagger, our rule-based term identification system was improved significantly by up to 11.6%.
  
  
  CONCLUSIONS
  This paper shows that, in the context of identifying terms involving multiple model organisms, integration of an accurate species disambiguation system can significantly improve the performance of term identification systems.
authors.affiliation: National Centre for Text Mining, University of Manchester, 131 Princess Street, Manchester M17DN, UK. xinglong.wang@manchester.ed.ac.uk
authors.forename: Xinglong
authors.initials: X
authors.lastname: Wang
authors.forename: Michael
authors.initials: M
authors.lastname: Matthews
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods; D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D019992:Databases as Topic; D016247:Information Storage and Retrieval / Q000379:methods; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D013045:Species Specificity; D009626:Terminology as Topic