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First published May 19, 2005 as JAMIA PrePrint; doi:10.1197/jamia.M1757
Journal of the American Medical Informatics Association 2005;12(5):576-586
© 2005 American Medical Informatics Association

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Submitted on December 2, 2004
Accepted on April 23, 2005

ALICE: An Algorithm to Extract Abbreviations from MEDLINE

Hiroko Ao MSc1* and Toshihisa Takagi PhD2

Affiliation of the authors: 1 Department of Computational Biology, University of Tokyo, Chiba, Japan; Basic Research Laboratory, Kanebo, COSMETICS, INC., Kanagawa, Japan; 2 Department of Computational Biology, University of Tokyo, Chiba, Japan

* To whom correspondence should be addressed.

Objective To help biomedical researchers recognize dynamically introduced abbreviations in biomedical literature, such as gene and protein names, we have constructed a support system called ALICE (Abbreviation LIfter using Corpus-based Extraction). ALICE aims to extract all types of abbreviations with their expansions from a target paper on the fly.

Methods ALICE extracts an abbreviation and its expansion from the literature by using heuristic pattern-matching rules. This system consists of three phases and potentially identifies valid 320 abbreviation-expansion patterns as combinations of the rules.

Results It achieved 95% recall and 97% precision on randomly selected titles and abstracts from the MEDLINE database.

Conclusion ALICE extracted abbreviations and their expansions from the literature efficiently. The subtly compiled heuristics enabled it to extract abbreviations with high recall without significantly reducing precision. ALICE does not only facilitate recognition of an undefined abbreviation in a paper by constructing an abbreviation database or a dictionary, but also makes biomedical literature retrieval more accurate. This system is freely available at http://uvdb3.hgc.jp/ALICE/ALICE_index.html.




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