We present an algorithm for extracting abbre- viation definitions from biomedical text. Our approach is based on an alignment HMM, matching abbreviations and their definitions. We report 98% precision and 93% recall on a standard data set, and 95% precision and 91% recall on an additional test set. Our re- sults show an improvement over previously re- ported methods and our model has several ad- vantages. Our model: (1) is simpler and faster than a comparable alignment-based abbrevia- tion extractor; (2) is naturally generalizable to specific types of abbreviations, e.g., abbrevia- tions of chemical formulas; (3) is trained on a set of unlabeled examples; and (4) associates a probability with each predicted definition. Us- ing the abbreviation alignment model we were able to extract over 1...

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