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Bioinformatics Advance Access published online on October 18, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl534
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© 2006 The Author(s)
Received July 1, 2006
Revised October 10, 2006
Accepted October 12, 2006

Article

Building an abbreviation dictionary using a term recognition approach

Naoaki Okazaki 1 * and Sophia Ananiadou 2

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

* To whom correspondence should be addressed.
Naoaki Okazaki, E-mail: okazaki{at}mi.ci.i.u-tokyo.ac.jp


  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 cooccurring 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 baseline 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/.

Associate Editor: Golan Yona
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