  Abbreviations often have several distinct meanings, often making their use in
text ambiguous. Expanding them to their intended meaning in context is
important for Machine Reading tasks such as document search, recommendation and
question answering. Existing approaches mostly rely on manually labeled
examples of abbreviations and their correct long-forms. Such data sets are
costly to create and result in trained models with limited applicability and
flexibility. Importantly, most current methods must be subjected to a full
empirical evaluation in order to understand their limitations, which is
cumbersome in practice.
  In this paper, we present an entirely unsupervised abbreviation
disambiguation method (called UAD) that picks up abbreviation definitions from
unstructured text. Creating distinct tokens per meaning, we learn context
representations as word vectors. We demonstrate how to further boost
abbreviation disambiguation performance by obtaining better context
representations using additional unstructured text. Our method is the first
abbreviation disambiguation approach with a transparent model that allows
performance analysis without requiring full-scale evaluation, making it highly
relevant for real-world deployments.
  In our thorough empirical evaluation, UAD achieves high performance on large
real-world data sets from different domains and outperforms both baseline and
state-of-the-art methods. UAD scales well and supports thousands of
abbreviations with multiple different meanings within a single model.
  In order to spur more research into abbreviation disambiguation, we publish a
new data set, that we also use in our experiments.

Computation and Language