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Abbreviations present a significant challenge for NLP systems because theycause tokenization and out-of-vocabulary errors. They can also make the textless readable, especially in reference printed books, where they areextensively used. Abbreviations are especially problematic in low-resourcesettings, where systems are less robust to begin with. In this paper, wepropose a new method for addressing the problems caused by a high density ofdomain-specific abbreviations in a text. We apply this method to the case of aSlovenian biographical lexicon and evaluate it on a newly developedgold-standard dataset of 51 Slovenian biographies. Our abbreviationidentification method performs significantly better than commonly used ad-hocsolutions, especially at identifying unseen abbreviations. We also propose andpresent the results of a method for expanding the identified abbreviations incontext.

Dealing with Abbreviations in the Slovenian Biographical Lexicon