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