NERCat: Fine-Tuning for Enhanced Named Entity Recognition in Catalan
This work addresses the challenge of improving NER for Catalan NLP applications, but it is incremental as it applies an existing fine-tuning method to a new dataset.
The paper tackled the problem of low performance in Named Entity Recognition (NER) for Catalan, a low-resource language, by fine-tuning the GLiNER model on manually annotated television transcriptions, resulting in significant improvements in precision, recall, and F1-score, especially for underrepresented categories like Law, Product, and Facility.
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.