  Abbreviation disambiguation is important for automated clinical note
processing due to the frequent use of abbreviations in clinical settings.
Current models for automated abbreviation disambiguation are restricted by the
scarcity and imbalance of labeled training data, decreasing their
generalizability to orthogonal sources. In this work we propose a novel data
augmentation technique that utilizes information from related medical concepts,
which improves our model's ability to generalize. Furthermore, we show that
incorporating the global context information within the whole medical note (in
addition to the traditional local context window), can significantly improve
the model's representation for abbreviations. We train our model on a public
dataset (MIMIC III) and test its performance on datasets from different sources
(CASI, i2b2). Together, these two techniques boost the accuracy of abbreviation
disambiguation by almost 14% on the CASI dataset and 4% on i2b2.
