A Comprehensive Part-of-Speech Tagging to Standardize Central-Kurdish Language: A Research Guide for Kurdish Natural Language Processing Tasks

- The field of natural language processing (NLP) has dramatically expanded within the last decade. Many human-being applications are conducted daily via NLP tasks, starting from machine translation, speech recognition, text generation and recommendations, Part-of-Speech tagging (POS), and Named-Entity Recognition (NER). However, low-resourced languages, such as the Central-Kurdish language (CKL), mainly remain unexamined due to shortage of necessary resources to support their development. The POS tagging task is the base of other NLP tasks; for example, the POS tag set has been used to standardized languages to provide the relationship between words among the sentences, followed by machine translation and text recommendation. Specifically, for the CKL, most of the utilized or provided POS tagsets are neither standardized nor comprehensive. To this end, this study presented an accurate and comprehensive POS tagset for the CKL to provide better performance of the Kurdish NLP tasks. The article also collected most of the POS tags from different studies as well as from Kurdish linguistic experts to standardized part-of-speech tags. The proposed POS tagset is designed to annotate a large CKL corpus and support Kurdish NLP tasks. The initial investigations of this study via comparison with the Universal Dependencies framework for standard languages, show that the proposed POS tagset can streamline or correct sentences more accurately for Kurdish NLP tasks.

@article{sabr2025_2504.19645,
  title={ A Comprehensive Part-of-Speech Tagging to Standardize Central-Kurdish Language: A Research Guide for Kurdish Natural Language Processing Tasks },
  author={ Shadan Shukr Sabr and Nazira Sabr Mustafa and Talar Sabah Omar and Salah Hwayyiz Rasool and Nawzad Anwer Omer and Darya Sabir Hamad and Hemin Abdulhameed Shams and Omer Mahmood Kareem and Rozhan Noori Abdullah and Khabat Atar Abdullah and Mahabad Azad Mohammad and Haneen Al-Raghefy and Safar M. Asaad and Sara Jamal Mohammed and Twana Saeed Ali and Fazil Shawrow and Halgurd S. Maghdid },
  journal={arXiv preprint arXiv:2504.19645},
  year={ 2025 }
}