Orthographic Robustness of Persian Named Entity Recognition Models

AbstractNamed Entity Recognition (NER) models trained on clean text often fail on real-world data containing orthographic noise. Work on NER for Persian is emerging, but it has not yet explored the orthographic robustness of models to perturbations often exhibited in user-generated content. We evaluate ParsBERT, ParsBERT v2.0, BertNER, and two XLM-r-based models on a subset of Persian-NER-Dataset-500k after applying eleven different perturbations, including simulated typos, code-switching, and segmentation errors. All models were competitive with each other, but XLM-r-large consistently displayed the best robustness to perturbations. Code-switching, typos, similar character swaps, segmentation errors, and noisy text all decreased F1 scores, while Latinized numbers increased F1 scores in ParsBERT. Removing diacritics, zero-width non-joiners, and normalizing Yeh/Kaf all did not have an effect on F1. These findings suggest that Persian NER models require improvement for performance on noisy text, and that the Perso-Arabic script introduces unique factors into NER not present in many high-resource languages, such as code-switching and Eastern Arabic numerals. This work creates a foundation for the development of robust Persian NER models and highlights the necessity of evaluating low-resource NER models under challenging and realistic conditions.