@inproceedings{farsi-etal-2025-parsipy,
    title = "{P}arsi{P}y: {NLP} Toolkit for Historical {P}ersian Texts in Python",
    author = "Farsi, Farhan  and
      Fazel, Parnian  and
      Haghighi, Sepand  and
      Sabouri, Sadra  and
      Goshtasb, Farzaneh  and
      Hajipour, Nadia  and
      Asgari, Ehsaneddin  and
      Sameti, Hossein",
    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/Author-page-Marten-During-lu/2025.alp-1.17/",
    pages = "137--149",
    ISBN = "979-8-89176-235-0",
    abstract = "The study of historical languages presents unique challenges due to their complex ortho-graphic systems, fragmentary textual evidence, and the absence of standardized digital repre-sentations of text in those languages. Tack-ling these challenges needs special NLP digi-tal tools to handle phonetic transcriptions and analyze ancient texts. This work introduces ParsiPy1, an NLP toolkit designed to facili-tate the analysis of historical Persian languages by offering modules for tokenization, lemma-tization, part-of-speech tagging, phoneme-to-transliteration conversion, and word embed-ding. We demonstrate the utility of our toolkit through the processing of Parsig (Middle Per-sian) texts, highlighting its potential for ex-panding computational methods in the study of historical languages. Through this work, we contribute to the field of computational philol-ogy, offering tools that can be adapted for the broader study of ancient texts and their digital preservation."
}