ParsiPy: NLP Toolkit for Historical Persian Texts in Python
This toolkit addresses the problem of digital analysis and preservation for researchers in computational philology, though it is incremental as it adapts existing NLP methods to a specific domain.
The authors tackled the challenge of analyzing historical Persian texts by developing ParsiPy, an NLP toolkit that includes modules for tokenization, lemmatization, part-of-speech tagging, phoneme-to-transliteration conversion, and word embedding, and demonstrated its utility on Parsig (Middle Persian) texts to expand computational methods in historical language study.
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.