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

Guillem Cadevall Ferreres; Marc Serrano Sanz; Marc Bardeli Gámez; Pol Gerdt Basullas; Francesc Tarres Ruiz; Raul Quijada Ferrero

arXiv · 2503.14173

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

Guillem Cadevall Ferreres·Marc Serrano Sanz·Marc Bardeli Gámez·Pol Gerdt Basullas·Francesc Tarres Ruiz·Raul Quijada Ferrero

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Abstract

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.

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Guillem Cadevall Ferreres, Marc Serrano Sanz, Marc Bardeli Gámez, Pol Gerdt Basullas, Francesc Tarres Ruiz, Raul Quijada Ferrero. 2025-03-18. NERCat: Fine-Tuning for Enhanced Named Entity Recognition in Catalan. https://arxiv.org/abs/2503.14173

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