  Abbreviations present a significant challenge for NLP systems because they
cause tokenization and out-of-vocabulary errors. They can also make the text
less readable, especially in reference printed books, where they are
extensively used. Abbreviations are especially problematic in low-resource
settings, where systems are less robust to begin with. In this paper, we
propose a new method for addressing the problems caused by a high density of
domain-specific abbreviations in a text. We apply this method to the case of a
Slovenian biographical lexicon and evaluate it on a newly developed
gold-standard dataset of 51 Slovenian biographies. Our abbreviation
identification method performs significantly better than commonly used ad-hoc
solutions, especially at identifying unseen abbreviations. We also propose and
present the results of a method for expanding the identified abbreviations in
context.
