@inproceedings{kazakov-etal-2025-towards,
    title = "Towards Creating a {B}ulgarian Readability Index",
    author = "Kazakov, Dimitar  and
      Minkov, Stefan  and
      Margova, Ruslana  and
      Temnikova, Irina  and
      Emauilov, Ivo",
    editor = "Estevanell-Valladares, Ernesto Luis  and
      Picazo-Izquierdo, Alicia  and
      Ranasinghe, Tharindu  and
      Mikaberidze, Besik  and
      Ostermann, Simon  and
      Gurgurov, Daniil  and
      Mueller, Philipp  and
      Borg, Claudia  and
      {\v{S}}imko, Mari{\'a}n",
    booktitle = "Proceedings of the First Workshop on Advancing NLP for Low-Resource Languages",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.lowresnlp-1.18/",
    pages = "192--200",
    abstract = "Readability assessment plays a crucial role in education and text accessibility. While numerous indices exist for English and have been extended to Romance and Slavic languages, Bulgarian remains under- served in this regard. This paper reviews established readability metrics across these language families, examining their underlying features and modelling methods. We then report the first attempt to develop a readability index for Bulgarian, using end-of-school-year assessment questions and literary works targeted at children of various ages. Key linguistic attributes, namely, word length, sentence length, syllable count, and information content (based on word frequency), were extracted, and their first two statistical moments, mean and variance, were modelled against grade levels using linear and polynomial regression. Results suggest that polynomial models outperform linear ones by capturing non-linear relationships between textual features and perceived difficulty, but may be harder to interpret. This work provides an initial framework for building a reliable readability measure for Bulgarian, with applications in educational text design, adaptive learning, and corpus annotation."
}