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ALICE: an algorithm to extract abbreviations from MEDLINE.

Hiroko Ao1, Toshihisa Takagi

  • 1Department of Computational Biology, University of Tokyo CB01, 5-1-5, Kashiwanoha, Kashiwa-shi, Chiba, 277-8561, Japan. aohiroko@hgc.jp

Journal of the American Medical Informatics Association : JAMIA
|May 21, 2005
PubMed
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Researchers can now efficiently identify gene and protein abbreviations in biomedical literature using ALICE (Abbreviation LIfter using Corpus-based Extraction). This system achieves high accuracy, improving literature retrieval and understanding.

Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Biomedical literature contains numerous dynamically introduced abbreviations, such as gene and protein names.
  • Recognizing these abbreviations is crucial for accurate literature retrieval and comprehension.

Purpose of the Study:

  • To develop a support system, ALICE (Abbreviation LIfter using Corpus-based Extraction), for recognizing dynamically introduced abbreviations in biomedical literature.
  • To enable on-the-fly extraction of all types of abbreviations and their expansions from target papers.

Main Methods:

  • ALICE employs heuristic pattern-matching rules to extract abbreviations and their expansions.
  • The system comprises three phases and identifies 320 potential abbreviation-expansion patterns.

Related Experiment Videos

Main Results:

  • ALICE achieved 95% recall and 97% precision on MEDLINE titles and abstracts.
  • The system efficiently extracts abbreviations and their expansions.

Conclusions:

  • ALICE's heuristics allow for high recall in abbreviation extraction without significant precision loss.
  • The system enhances biomedical literature retrieval accuracy and aids in creating abbreviation databases.
  • ALICE is freely available online.