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Bioinformatics Advance Access originally published online on October 18, 2006
Bioinformatics 2006 22(24):3089-3095; doi:10.1093/bioinformatics/btl534
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Building an abbreviation dictionary using a term recognition approach

Naoaki Okazaki 1,2,* and Sophia Ananiadou 3,4

1 Graduate School of Information Science and Technology, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8651, Japan
2 Japan Society for the Promotion of Science (JSPS), 8 Ichiban-cho, Chiyoda-ku, Tokyo Japan
3 School of Computer Science, The University of Manchester Oxford Road, Manchester, M13 9PL, UK
4 National Centre for Text Mining (NaCTeM), Manchester Interdisciplinary Biocentre Oxford Road, Manchester, M13 9PL, UK

*To whom correspondence should be addressed.

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{at}mi.ci.i.u-tokyo.ac.jp

Received on July 1, 2006; revised on October 10, 2006; accepted on October 12, 2006

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