  Automated analysis of clinical notes is attracting increasing attention.
However, there has not been much work on medical term abbreviation
disambiguation. Such abbreviations are abundant, and highly ambiguous, in
clinical documents. One of the main obstacles is the lack of large scale,
balance labeled data sets. To address the issue, we propose a few-shot learning
approach to take advantage of limited labeled data. Specifically, a neural
topic-attention model is applied to learn improved contextualized sentence
representations for medical term abbreviation disambiguation. Another vital
issue is that the existing scarce annotations are noisy and missing. We
re-examine and correct an existing dataset for training and collect a test set
to evaluate the models fairly especially for rare senses. We train our model on
the training set which contains 30 abbreviation terms as categories (on
average, 479 samples and 3.24 classes in each term) selected from a public
abbreviation disambiguation dataset, and then test on a manually-created
balanced dataset (each class in each term has 15 samples). We show that
enhancing the sentence representation with topic information improves the
performance on small-scale unbalanced training datasets by a large margin,
compared to a number of baseline models.
