PARSI: Persian Authorship Recognition via Stylometric Integration

TL;DR

This paper introduces PARSI, a neural framework combining deep language models and domain-specific features to accurately attribute Persian poetry to authors, achieving up to 97% confidence-based accuracy.

Contribution

It presents a novel multi-input neural approach integrating transformer embeddings and stylometric features for Persian authorship recognition, with extensive corpus validation.

Findings

Weighted voting achieves 71% accuracy.

Threshold filtering reaches 97% accuracy at 0.9 confidence.

The framework effectively combines deep representations with domain features.

Abstract

The intricate linguistic, stylistic, and metrical aspects of Persian classical poetry pose a challenge for computational authorship attribution. In this work, we present a versatile framework to determine authorship among 67 prominent poets. We employ a multi-input neural framework consisting of a transformer-based language encoder complemented by features addressing the semantic, stylometric, and metrical dimensions of Persian poetry. Our feature set encompasses 100-dimensional Word2Vec embeddings, seven stylometric measures, and categorical encodings of poetic form and meter. We compiled a vast corpus of 647,653 verses of the Ganjoor digital collection, validating the data through strict preprocessing and author verification while preserving poem-level splitting to prevent overlap. This work employs verse-level classification and majority and weighted voting schemes in evaluation, revealing that weighted voting yields 71% accuracy. We further investigate threshold-based decision filtering, allowing the model to generate highly confident predictions, achieving 97% accuracy at a 0.9 threshold, though at lower coverage. Our work focuses on the integration of deep representational forms with domain-specific features for improved authorship attribution. The results illustrate the potential of our approach for automated classification and the contribution to stylistic analysis, authorship disputes, and general computational literature research. This research will facilitate further research on multilingual author attribution, style shift, and generative modeling of Persian poetry.