  Automatic identification and expansion of ambiguous abbreviations are
essential for biomedical natural language processing applications, such as
information retrieval and question answering systems. In this paper, we present
DEep Contextualized Biomedical. Abbreviation Expansion (DECBAE) model. DECBAE
automatically collects substantial and relatively clean annotated contexts for
950 ambiguous abbreviations from PubMed abstracts using a simple heuristic.
Then it utilizes BioELMo to extract the contextualized features of words, and
feed those features to abbreviation-specific bidirectional LSTMs, where the
hidden states of the ambiguous abbreviations are used to assign the exact
definitions. Our DECBAE model outperforms other baselines by large margins,
achieving average accuracy of 0.961 and macro-F1 of 0.917 on the dataset. It
also surpasses human performance for expanding a sample abbreviation, and
remains robust in imbalanced, low-resources and clinical settings.
