NERCat: Fine-Tuning for Enhanced Named Entity Recognition in Catalan
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arXiv
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| Format: | Preprint |
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2025
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| author | Ferreres, Guillem Cadevall Sanz, Marc Serrano Gámez, Marc Bardeli Basullas, Pol Gerdt Ruiz, Francesc Tarres Ferrero, Raul Quijada |
| author_facet | Ferreres, Guillem Cadevall Sanz, Marc Serrano Gámez, Marc Bardeli Basullas, Pol Gerdt Ruiz, Francesc Tarres Ferrero, Raul Quijada |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14173 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NERCat: Fine-Tuning for Enhanced Named Entity Recognition in Catalan Ferreres, Guillem Cadevall Sanz, Marc Serrano Gámez, Marc Bardeli Basullas, Pol Gerdt Ruiz, Francesc Tarres Ferrero, Raul Quijada Computation and Language 68T50 I.2.7 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. |
| title | NERCat: Fine-Tuning for Enhanced Named Entity Recognition in Catalan |
| topic | Computation and Language 68T50 I.2.7 |
| url | https://arxiv.org/abs/2503.14173 |