Building an abbreviation dictionary using a term recognition approach

Naoaki Okazaki, Sophia Ananiadou

    Research output: Contribution to journalArticlepeer-review


    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. © 2006 Oxford University Press.
    Original languageEnglish
    Pages (from-to)3089-3095
    Number of pages6
    Issue number24
    Publication statusPublished - 15 Dec 2006


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