Building an abbreviation dictionary using a term recognition approach

  • Authors:

  • Naoaki Okazaki;Sophia Ananiadou

  • Affiliations:

  • Graduate School of Information Science and Technology, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8651, Japan;School of Computer Science, The University of Manchester Oxford Road, Manchester, M13 9PL, UK

  • Venue:
  • Bioinformatics
  • Year:
  • 2006

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Abstract

Motivation: Acronyms result from a highly productive type of term variation and trigger the need for an acronym dictionary to establish associations between acronyms and their expanded forms. Results: We propose a novel method for recognizing acronym definitions in a text collection. Assuming a word sequence co-occurring frequently with a parenthetical expression to be a potential expanded form, our method identifies acronym definitions in a similar manner to the statistical term recognition task. Applied to the whole MEDLINE (7 811 582 abstracts), the implemented system extracted 886 755 acronym candidates and recognized 300 954 expanded forms in reasonable time. Our method outperformed base-line systems, achieving 99% precision and 82--95% recall on our evaluation corpus that roughly emulates the whole MEDLINE. Availability and Supplementary information: The implementations and supplementary information are available at our web site: http://www.chokkan.org/research/acromine/ Contact: okazaki@mi.ci.i.u-tokyo.ac.jp