# Dealing with Abbreviations in the Slovenian Biographical Lexicon

arXiv: https://arxiv.org/abs/2211.02429 · HTML: https://mlsanity.com/paper/2211.02429/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 4 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [A Large-Scale Comparison of Historical Text Normalization Systems](https://mlsanity.com/paper/1904.02036/) (2019)
- [Deep Contextualized Biomedical Abbreviation Expansion](https://mlsanity.com/paper/1906.03360/) (2019)
- [Evaluating historical text normalization systems: How well do they generalize?](https://mlsanity.com/paper/1804.02545/) (2018)
- [Structured abbreviation expansion in context](https://mlsanity.com/paper/2110.01140/) (2021)
- [Few-Shot and Zero-Shot Learning for Historical Text Normalization](https://mlsanity.com/paper/1903.04870/) (2019)

## Abstract (arXiv)

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.