Using Distributional Semantic Models and Levenshtein Distance Normalization

In the medical domain, especially in clinical texts, non-standard abbreviations are prevalent, which impairs readability for patients. To ease the understanding of the physicians’ notes, abbreviations need to be identified and expanded to their original forms. This thesis presents a distributional semantic approach to find candidates of the original form of the abbreviation, which is combined with Levenshtein distance to choose the correct candidate among the semantically related words. The method is applied to radiology reports and medical journal texts, and a comparison is made to general Swedish. The results show that the correct expansion of the abbreviation can be found in 40% of the cases, an improvement by 24 percentage points compared to the baseline (0.16), and an increase by 22 percentage points compared to using word space models alone (0.18).