The study of historical languages presents unique challenges due to their complex orthographic systems, fragmentary textual evidence, and the absence of standardized digital representations of text in those languages. Tackling these challenges needs special NLP digital tools to handle phonetic transcriptions and analyze ancient texts. This work introduces ParsiPy, an NLP toolkit designed to facilitate the analysis of historical Persian languages by offering modules for tokenization, lemmatization, part-of-speech tagging, phoneme-to-transliteration conversion, and word embedding. We demonstrate the utility of our toolkit through the processing of Parsig (Middle Persian) texts, highlighting its potential for expanding computational methods in the study of historical languages. Through this work, we contribute to computational philology, offering tools that can be adapted for the broader study of ancient texts and their digital preservation.
View on arXiv@article{farsi2025_2503.17810,
title={ ParsiPy: NLP Toolkit for Historical Persian Texts in Python },
author={ Farhan Farsi and Parnian Fazel and Sepand Haghighi and Sadra Sabouri and Farzaneh Goshtasb and Nadia Hajipour and Ehsaneddin Asgari and Hossein Sameti },
journal={arXiv preprint arXiv:2503.17810},
year={ 2025 }
}