@inproceedings{sahala-lincke-2025-neural,
    title = "Neural Models for Lemmatization and {POS}-Tagging of Earlier and Late {E}gyptian (Supporting Hieroglyphic Input) and Demotic",
    author = "Sahala, Aleksi  and
      Lincke, Eliese-Sophia",
    editor = "Anderson, Adam  and
      Gordin, Shai  and
      Li, Bin  and
      Liu, Yudong  and
      Passarotti, Marco C.  and
      Sprugnoli, Rachele",
    booktitle = "Proceedings of the Second Workshop on Ancient Language Processing",
    month = may,
    year = "2025",
    address = "The Albuquerque Convention Center, Laguna",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.alp-1.12/",
    pages = "99--104",
    ISBN = "979-8-89176-235-0",
    abstract = "We present updated models for BabyLemma-tizer for lemmatizing and POS-tagging De-motic, Late Egyptian and Earlier Egyptian with a support for using hieroglyphs as an input. In this paper, we also use data that has not been cleaned from breakages. We achieve consistent UPOS tagging accuracy of 94{\%} or higher and an XPOS tagging accuracy of 93{\%} and higher for all languages. For lemmatization, which is challenging in all of our test languages due to extensive ambiguity, we demonstrate accu-racies from 77{\%} up to 92{\%} depending on the language and the input script."
}