NERCat: Fine-Tuning for Enhanced Named Entity Recognition in Catalan

Named Entity Recognition (NER) is a critical component of Natural Language Processing (NLP) for extracting structured information from unstructured text. However, for low-resource languages like Catalan, the performance of NER systems often suffers due to the lack of high-quality annotated datasets. This paper introduces NERCat, a fine-tuned version of the GLiNER[1] model, designed to improve NER performance specifically for Catalan text. We used a dataset of manually annotated Catalan television transcriptions to train and fine-tune the model, focusing on domains such as politics, sports, and culture. The evaluation results show significant improvements in precision, recall, and F1-score, particularly for underrepresented named entity categories such as Law, Product, and Facility. This study demonstrates the effectiveness of domain-specific fine-tuning in low-resource languages and highlights the potential for enhancing Catalan NLP applications through manual annotation and high-quality datasets.

@article{ferreres2025_2503.14173,
  title={ NERCat: Fine-Tuning for Enhanced Named Entity Recognition in Catalan },
  author={ Guillem Cadevall Ferreres and Marc Serrano Sanz and Marc Bardeli Gámez and Pol Gerdt Basullas and Francesc Tarres Ruiz and Raul Quijada Ferrero },
  journal={arXiv preprint arXiv:2503.14173},
  year={ 2025 }
}