Abstract

1 To whom correspondence should be addressed The immense volume and rapid growth of biomedical literature present special challenges for humans as well as computer programs analyzing it. One such challenge comes from the common use of abbreviations that effectively augments the size of the vocabulary for the field. To cope with this, we have developed an algorithm to identify abbreviations in text. It uses a statistical learning algorithm logistic regression to score abbreviations based on their resemblance to previously identified ones, achieving up to 84 % recall at 81 % precision. We then scanned all of MEDLINE and found 781,632 high-scoring abbreviation definitions. We are making these available as a public abbreviation server

Citations