Clinical narratives are typically produced under time pressure, which incites the use of abbreviations and acronyms. To expand such short forms in a correct way eases text comprehension and further semantic processing. We propose a completely unsupervised and data-driven algorithm for the resolution of non-lexicalised and potentially ambiguous abbreviations. Based on the lookup of word bigrams and unigrams extracted from a corpus of 30,000 pseudonymised cardiology reports in German, our method achieved an F1 score of 0.91, evaluated with a test set of 200 text excerpts. The results are statistically significantly better (p < 0.001) than a baseline approach and show that a simple and domain-independent strategy may be enough to resolve abbreviations when a large corpus of similar texts is available. Further work is needed to combine this strategy with sentence and abbreviation detection modules, to adapt it to acronym resolution and to evaluate it with different datasets.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 firstname.lastname@example.org
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 email@example.com