Building an abbreviation dictionary using a term recognition approach

Naoaki Okazaki, Sophia Ananiadou

Research output: Contribution to journalArticle

58 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)3089-3095
Number of pages7
JournalBioinformatics
Volume22
Issue number24
DOIs
Publication statusPublished - 2006 Dec 15

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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