# ParsiPy: NLP Toolkit for Historical Persian Texts in Python

arXiv: https://arxiv.org/abs/2503.17810 · HTML: https://mlsanity.com/paper/2503.17810/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 62 references that are in the map.
- First posted on arXiv 22 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP](https://mlsanity.com/paper/2112.10508/) (2021)
- [Low-resource Languages: A Review of Past Work and Future Challenges](https://mlsanity.com/paper/2006.07264/) (2020)
- [LFTK: Handcrafted Features in Computational Linguistics](https://mlsanity.com/paper/2305.15878/) (2023)
- [A tailored Handwritten-Text-Recognition System for Medieval Latin](https://mlsanity.com/paper/2308.09368/) (2023)
- [Data-Driven Machine Learning Approaches for Predicting In-Hospital Sepsis Mortality](https://mlsanity.com/paper/2408.01612/) (2024)
- [Larth: Dataset and Machine Translation for Etruscan](https://mlsanity.com/paper/2310.05688/) (2023)
- [PyMilo: A Python Library for ML I/O](https://mlsanity.com/paper/2501.00528/) (2024)

## Built on by (most cited first, 1 of 1)

- [TabletCraft: Bridging a 4,000-Year Cultural Gap with Bidirectional Akkadian NMT and Cuneiform Rendering](https://mlsanity.com/paper/2608.02609/) (2026)

## Abstract (arXiv)

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.