title: An easily implemented method for abbreviation expansion for the medical domain in Japanese text. A preliminary study.
abstract: BACKGROUND
  One of the barriers for the effective use of computerized health-care related text is the ambiguity of abbreviations. To date, the task of disambiguating abbreviations has been treated as a classification task based on surrounding words. Application of this framework for languages that have no word boundaries requires pre-processing to segment a sentence into separate word sequences. While the segmentation processing is often a source of problem, it is unknown whether word information is really requisite for abbreviation expansion.
  
  
  OBJECTIVE
  The present study examined and compared abbreviation expansion methods with and without the incorporation of word information as a preliminary study.
  
  
  METHODS
  We implemented two abbreviation expansion methods: 1) a morpheme-based method that relied on word information and therefore required pre-processing, and 2) a character-based method that relied on simple character information. We compared the expansion accuracies for these two methods using eight medical abbreviations. Experimental data were automatically built as a pseudo-annotated corpus using the Internet.
  
  
  RESULTS
  As a result of the experiment, accuracies for the character-based method were from 0.890 to 0.942 while accuracies for the morpheme-based method were from 0.796 to 0.932. The character-based method significantly outperformed the morpheme-based method for three of the eight abbreviations (p < 0.05). For the remaining five abbreviations, no significant differences were found between the two methods.
  
  
  CONCLUSIONS
  Character information may be a good alternative in terms of simplicity to morphological information for abbreviation expansion in English medical abbreviations appeared in Japanese texts on the Internet.
authors.affiliation: Department of Planning, Information and Management, The University of Tokyo Hospital, Tokyo, Japan. emiko-tky@umin.net
authors.forename: E Y
authors.initials: EY
authors.lastname: Shinohara
authors.forename: E
authors.initials: E
authors.lastname: Aramaki
authors.forename: T
authors.initials: T
authors.lastname: Imai
authors.forename: Y
authors.initials: Y
authors.lastname: Miura
authors.forename: M
authors.initials: M
authors.lastname: Tonoike
authors.forename: T
authors.initials: T
authors.lastname: Ohkuma
authors.forename: H
authors.initials: H
authors.lastname: Masuichi
authors.forename: K
authors.initials: K
authors.lastname: Ohe
mesh_terms: D000004:Abbreviations as Topic; D000465:Algorithms; D001185:Artificial Intelligence; D003431:Cross-Cultural Comparison; D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval; D007564:Japan; D009323:Natural Language Processing