  In the medical domain, identifying and expanding abbreviations in clinical
texts is a vital task for both better human and machine understanding. It is a
challenging task because many abbreviations are ambiguous especially for
intensive care medicine texts, in which phrase abbreviations are frequently
used. Besides the fact that there is no universal dictionary of clinical
abbreviations and no universal rules for abbreviation writing, such texts are
difficult to acquire, expensive to annotate and even sometimes, confusing to
domain experts. This paper proposes a novel and effective approach - exploiting
task-oriented resources to learn word embeddings for expanding abbreviations in
clinical notes. We achieved 82.27% accuracy, close to expert human performance.
