Fine-tuning XLM - R o BERT a for Named Entity Recognition in K urmanji K urdish (2025)

Abstract: Named Entity Recognition (NER) is the information extraction task of identifying predefined named entities such as person names, location names, organization names and more. High-resource languages have made significant progress in NER tasks. However, low-resource languages such as Kurmanji Kurdish have not seen the same advancements, due to these languages having less available data online. This research aims to close this gap by developing an NER system via fine-tuning XLM-RoBERTa on a manually annotated dataset for Kurmanji. The dataset used for fine-tuning consists of 7,919 annotated sentences, which were manually annotated by three native Kurmanji speakers. The classes labeled in the dataset are Person (PER), Organization (ORG), and Location (LOC). A web-based application has also been developed using Streamlit to make the model more accessible. The model achieved an F1 score of 0.8735, precision of 0.8668, and recall of 0.8803, demonstrating the effectiveness of fine-tuning transformer-based models for NER tasks in low-resource languages. This work establishes a methodology that can be applied to other low-resource languages and Kurdish varieties.

## introduction despite recent advances in nlp technologies, lowresource languages such as kurdish continue to lag behind high-resource languages. among kurdish varieties, kurmanji (northern kurdish) is the most widely spoken, used by approximately 65% of the kurdish population @xcite . it also constitutes the largest group in terms of geographical distribution and speaker numbers @xcite , yet it remains overshadowed by sorani, which holds official language status in iraq. a key challenge for kurmanji nlp is script variation. the language employs different writing systems across regions: a modified perso-arabic script in iraqi kurdistan and iran, and the latin-based hawar alphabet in turkey and syria @xcite . this fragmentation complicates resource development and limits crossregional data sharing. this research focuses on the hawar alphabet due to its broader geographic usage and greater presence in digital text sources. named entity recognition (ner) serves as a fundamental building block for downstream nlp applications including information retrieval, machine translation, and question answering. recent research has shown that fine-tuning pre-trained transformer models achieves superior performance for ner tasks on low-resource languages @xcite . following this approach, we develop a reliable ner system for kurmanji by fine-tuning xlm-roberta on a manually annotated dataset. our contributions include: ## discussion our results demonstrate that relatively small, highquality annotated datasets can achieve strong ner performance for low-resource languages. with 7,919 annotated sentences, we achieved performance competitive with systems trained on much larger datasets for high-resource languages. the 11.0 percentage point improvement over the multilingual model is particularly significant given that both models share the same underlying architecture. this validates the importance of language-specific fine-tuning and suggests that cross-lingual transfer alone is insufficient for optimal performance on low-resource languages. the performance variation across entity types (loc: 0.904, per: 0.901, org: 0.805) reflects inherent linguistic challenges. kurdish organizational names often lack standardization and may incorporate location names, making them harder to distinguish. looking ahead, future work should explore developing script-agnostic models that can handle both latin and perso-arabic kurmanji, enabling broader accessibility. additional directions include expanding the dataset to cover more domains such as social media, legal, and medical texts; applying data augmentation techniques to mitigate data scarcity; and extending the system to other kurdish dialects such as sorani and zazaki. ## conclusion we presented the first publicly available ner system for kurmanji kurdish, achieving an f1 score of 0.8735 through fine-tuning xlm-roberta on a manually annotated dataset. our work demonstrates that transformer-based approaches can successfully address nlp challenges in low-resource languages, even with modest amounts of training data. beyond technical achievements, this research contributes to digital inclusion and preservation of kurdish linguistic heritage. the methodology established here provides a replicable framework for developing ner systems for other low-resource languages and kurdish varieties. manji speakers who volunteered for this research project and were fully informed about the purpose and intended use of their annotations. our dataset was constructed from publicly available sources including news websites and wikipedia, containing no private or personally identifiable information beyond public figure names that naturally appear in news contexts. we acknowledge that the system currently supports only the latin-based hawar alphabet. this decision was based on the greater availability of online kurmanji text in this script, but we recognize that it may limit accessibility for speakers who use the arabic script in regions such as iraq and iran. we recognize that language technology development for minority languages carries both opportunities and risks. while ner systems can help preserve and promote kurdish digital presence, they could potentially be misused for surveillance or discrimination. we encourage responsible use of our resources and will clearly document these considerations in our public release. the developed resources will be released under an open license to benefit the kurdish nlp research community while including clear guidelines for ethical use.