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

• URL: http://arxiv.org/abs/2503.14173v1
• Date: Tue, 18 Mar 2025 11:44:19 GMT
• Title: NERCat: Fine-Tuning for Enhanced Named Entity Recognition in Catalan
• Authors: Guillem Cadevall Ferreres, Marc Serrano Sanz, Marc Bardeli Gámez, Pol Gerdt Basullas, Francesc Tarres Ruiz, Raul Quijada Ferrero,
• Abstract summary: This paper introduces NERCat, a fine-tuned version of the GLiNER[1] model, designed to improve NER performance specifically for Catalan text.<n>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.<n>The evaluation results show significant improvements in precision, recall, and F1-score, particularly for underrepresented named entity categories such as Law, Product, and Facility.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Pruning for Performance: Efficient Idiom and Metaphor Classification in Low-Resource Konkani Using mBERT [3.790013563494571]
  figurative language expressions pose persistent challenges for natural language processing systems.<n>We present a hybrid model that integrates a pre-trained Multilingual BERT with a bidirectional LSTM and a linear classifier.
  arXiv  Detail & Related papers  (2025-05-24T03:23:39Z)
• Autoencoder-Based Framework to Capture Vocabulary Quality in NLP [2.41710192205034]
  We introduce an autoencoder-based framework that uses neural network capacity as a proxy for vocabulary richness, diversity, and complexity. We validate our approach on two distinct datasets: the DIFrauD dataset, which spans multiple domains of deceptive and fraudulent text, and the Project Gutenberg dataset, representing diverse languages, genres, and historical periods.
  arXiv  Detail & Related papers  (2025-02-28T21:45:28Z)
• MAGE: Multi-Head Attention Guided Embeddings for Low Resource Sentiment Classification [0.19381162067627603]
  We introduce an advanced model combining Language-Independent Data Augmentation (LiDA) with Multi-Head Attention based weighted embeddings. This approach not only addresses the data scarcity issue but also sets a foundation for future research in low-resource language processing and classification tasks.
  arXiv  Detail & Related papers  (2025-02-25T08:53:27Z)
• A Thorough Investigation into the Application of Deep CNN for Enhancing Natural Language Processing Capabilities [0.0]
  This paper introduces Deep Convolutional Neural Networks (DCNN) into Natural Language Processing. By integrating DCNN, machine learning algorithms, and generative adversarial networks (GAN), the study improves language understanding, reduces ambiguity, and enhances task performance. The high-performance NLP model shows a 10% improvement in segmentation accuracy and a 4% increase in recall rate compared to traditional models.
  arXiv  Detail & Related papers  (2024-12-20T13:53:41Z)
• Natural Language Processing for Dialects of a Language: A Survey [56.93337350526933]
  State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets.<n>This survey delves into an important attribute of these datasets: the dialect of a language.<n>Motivated by the performance degradation of NLP models for dialectal datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches.
  arXiv  Detail & Related papers  (2024-01-11T03:04:38Z)
• On Significance of Subword tokenization for Low Resource and Efficient Named Entity Recognition: A case study in Marathi [1.6383036433216434]
  We focus on NER for low-resource language and present our case study in the context of the Indian language Marathi. We propose a hybrid approach for efficient NER by integrating a BERT-based subword tokenizer into vanilla CNN/LSTM models. We show that this simple approach of replacing a traditional word-based tokenizer with a BERT-tokenizer brings the accuracy of vanilla single-layer models closer to that of deep pre-trained models like BERT.
  arXiv  Detail & Related papers  (2023-12-03T06:53:53Z)
• Improving Domain-Specific Retrieval by NLI Fine-Tuning [64.79760042717822]
  This article investigates the fine-tuning potential of natural language inference (NLI) data to improve information retrieval and ranking. We employ both monolingual and multilingual sentence encoders fine-tuned by a supervised method utilizing contrastive loss and NLI data. Our results point to the fact that NLI fine-tuning increases the performance of the models in both tasks and both languages, with the potential to improve mono- and multilingual models.
  arXiv  Detail & Related papers  (2023-08-06T12:40:58Z)
• CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation [113.99145386490639]
  Cross-lingual NER can transfer knowledge between languages via aligned cross-lingual representations or machine translation results. We propose a Cross-lingual Entity Projection framework (CROP) to enable zero-shot cross-lingual NER. We adopt a multilingual labeled sequence translation model to project the tagged sequence back to the target language and label the target raw sentence.
  arXiv  Detail & Related papers  (2022-10-13T13:32:36Z)
• Nested Named Entity Recognition as Holistic Structure Parsing [92.8397338250383]
  This work models the full nested NEs in a sentence as a holistic structure, then we propose a holistic structure parsing algorithm to disclose the entire NEs once for all. Experiments show that our model yields promising results on widely-used benchmarks which approach or even achieve state-of-the-art.
  arXiv  Detail & Related papers  (2022-04-17T12:48:20Z)
• An Open-Source Dataset and A Multi-Task Model for Malay Named Entity Recognition [3.511753382329252]
  We build a Malay NER dataset (MYNER) comprising 28,991 sentences (over 384 thousand tokens) An auxiliary task, boundary detection, is introduced to improve NER training in both explicit and implicit ways.
  arXiv  Detail & Related papers  (2021-09-03T03:29:25Z)
• Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition [54.92161571089808]
  Cross-lingual NER transfers knowledge from rich-resource language to languages with low resources. Existing cross-lingual NER methods do not make good use of rich unlabeled data in target languages. We develop a novel approach based on the ideas of semi-supervised learning and reinforcement learning.
  arXiv  Detail & Related papers  (2021-06-01T05:46:22Z)
• Soft Gazetteers for Low-Resource Named Entity Recognition [78.00856159473393]
  We propose a method of "soft gazetteers" that incorporates ubiquitously available information from English knowledge bases into neural named entity recognition models. Our experiments on four low-resource languages show an average improvement of 4 points in F1 score.
  arXiv  Detail & Related papers  (2020-05-04T21:58:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.