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ALICE: An Algorithm to Extract Abbreviations from MEDLINE

Affiliations of the authors: Department of Computational Biology, University of Tokyo, Chiba, Japan (HA, TT); Basic Research Laboratory, Kanebo Cosmetics, Inc., Kanagawa, Japan (HA).

Correspondence and reprints: Hiroko Ao, MSc, Department of Computational Biology, University of Tokyo CB01, 5-1-5, Kashiwanoha, Kashiwa-shi, Chiba, 277-8561, Japan; e-mail: <aohiroko{at}hgc.jp>.

Received for publication: 12/02/04; accepted for publication: 04/23/05.

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 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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