title: ALICE: an algorithm to extract abbreviations from MEDLINE.
abstract: OBJECTIVE
  To help biomedical researchers recognize dynamically introduced abbreviations in biomedical literature, such as gene and protein names, we have constructed a support system called ALICE (Abbreviation LIfter using Corpus-based Extraction). ALICE aims to extract all types of abbreviations with their expansions from a target paper on the fly.
  
  
  METHODS
  ALICE extracts an abbreviation and its expansion from the literature by using heuristic pattern-matching rules. This system consists of three phases and potentially identifies valid 320 abbreviation-expansion patterns as combinations of the rules.
  
  
  RESULTS
  It achieved 95% recall and 97% precision on randomly selected titles and abstracts from the MEDLINE database.
  
  
  CONCLUSIONS
  ALICE extracted abbreviations and their expansions from the literature efficiently. The subtly compiled heuristics enabled it to extract abbreviations with high recall without significantly reducing precision. ALICE does not only facilitate recognition of an undefined abbreviation in a paper by constructing an abbreviation database or dictionary, but also makes biomedical literature retrieval more accurate. This system is freely available at http://uvdb3.hgc.jp/ALICE/ALICE_index.html.
authors.affiliation: Department of Computational Biology, University of Tokyo CB01, 5-1-5, Kashiwanoha, Kashiwa-shi, Chiba, 277-8561, Japan. aohiroko@hgc.jp
authors.forename: Hiroko
authors.initials: H
authors.lastname: Ao
authors.forename: Toshihisa
authors.initials: T
authors.lastname: Takagi
mesh_terms: D000004:Abbreviations as Topic; D000465:Algorithms; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D010363:Pattern Recognition, Automated / Q000379:methods*