MariNER: A Dataset for Historical Brazilian Portuguese Named Entity Recognition

• URL: http://arxiv.org/abs/2506.23051v1
• Date: Sun, 29 Jun 2025 01:23:57 GMT
• Title: MariNER: A Dataset for Historical Brazilian Portuguese Named Entity Recognition
• Authors: João Lucas Luz Lima Sarcinelli, Marina Lages Gonçalves Teixeira, Jade Bortot de Paiva, Diego Furtado Silva,
• Abstract summary: This paper outlines the construction of MariNER: textitMapeamento e Anotaccoes para NER (Mapping of Historical Records for NER), the first gold-standard dataset for early 20th-century Brazilian Portuguese.
• Score: 1.5224436211478216
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task that aims to identify and classify entity mentions in texts across different categories. While languages such as English possess a large number of high-quality resources for this task, Brazilian Portuguese still lacks in quantity of gold-standard NER datasets, especially when considering specific domains. Particularly, this paper considers the importance of NER for analyzing historical texts in the context of digital humanities. To address this gap, this work outlines the construction of MariNER: \textit{Mapeamento e Anota\c{c}\~oes de Registros hIst\'oricos para NER} (Mapping and Annotation of Historical Records for NER), the first gold-standard dataset for early 20th-century Brazilian Portuguese, with more than 9,000 manually annotated sentences. We also assess and compare the performance of state-of-the-art NER models for the dataset.

Related papers

• EDU-NER-2025: Named Entity Recognition in Urdu Educational Texts using XLM-RoBERTa with X (formerly Twitter) [41.29397464848022]
  We create a manually annotated dataset in the education domain, named EDU-NER-2025, which contains 13 most unique entities related to education domain.<n>We address and analyzed key linguistic challenges, such as morphological complexity and ambiguity, which are prevalent in formal Urdu texts.
  arXiv  Detail & Related papers  (2025-04-25T07:50:58Z)
• NERCat: Fine-Tuning for Enhanced Named Entity Recognition in Catalan [0.0]
  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.
  arXiv  Detail & Related papers  (2025-03-18T11:44:19Z)
• ESNLIR: A Spanish Multi-Genre Dataset with Causal Relationships [0.0]
  Natural Language Inference (NLI) serves as a crucial area within the domain of Natural Language Processing (NLP)<n>This paper focuses on generating a multi-genre Spanish dataset for NLI, ESNLIR, particularly accounting for causal Relationships.<n>The findings signify that the enrichment of genres essentially contributes to the enrichment of the model's capability to generalize.
  arXiv  Detail & Related papers  (2025-03-11T18:32:16Z)
• A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus [71.77214818319054]
  Natural language inference is a proxy for natural language understanding. There is no publicly available NLI corpus for the Romanian language. We introduce the first Romanian NLI corpus (RoNLI) comprising 58K training sentence pairs.
  arXiv  Detail & Related papers  (2024-05-20T08:41:15Z)
• 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)
• NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval [49.827932299460514]
  We argue that capabilities provided by large language models are not the end of NER research, but rather an exciting beginning. We present three variants of the NER task, together with a dataset to support them. We provide a large, silver-annotated corpus of 4 million paragraphs covering 500 entity types.
  arXiv  Detail & Related papers  (2023-10-22T12:23:00Z)
• IXA/Cogcomp at SemEval-2023 Task 2: Context-enriched Multilingual Named Entity Recognition using Knowledge Bases [53.054598423181844]
  We present a novel NER cascade approach comprising three steps. We empirically demonstrate the significance of external knowledge bases in accurately classifying fine-grained and emerging entities. Our system exhibits robust performance in the MultiCoNER2 shared task, even in the low-resource language setting.
  arXiv  Detail & Related papers  (2023-04-20T20:30:34Z)
• 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)
• AsNER -- Annotated Dataset and Baseline for Assamese Named Entity recognition [7.252817150901275]
  The proposed NER dataset is likely to be a significant resource for deep neural based Assamese language processing. We benchmark the dataset by training NER models and evaluating using state-of-the-art architectures for supervised named entity recognition. The highest F1-score among all baselines achieves an accuracy of 80.69% when using MuRIL as a word embedding method.
  arXiv  Detail & Related papers  (2022-07-07T16:45:55Z)
• HiNER: A Large Hindi Named Entity Recognition Dataset [29.300418937509317]
  This paper releases a standard-abiding Hindi NER dataset containing 109,146 sentences and 2,220,856 tokens, annotated with 11 tags. The statistics of tag-set in our dataset show a healthy per-tag distribution, especially for prominent classes like Person, Location and Organisation. Our dataset helps achieve a weighted F1 score of 88.78 with all the tags and 92.22 when we collapse the tag-set, as discussed in the paper.
  arXiv  Detail & Related papers  (2022-04-28T19:14:21Z)
• 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)
• MasakhaNER: Named Entity Recognition for African Languages [48.34339599387944]
  We create the first large publicly available high-quality dataset for named entity recognition in ten African languages. We detail characteristics of the languages to help researchers understand the challenges that these languages pose for NER.
  arXiv  Detail & Related papers  (2021-03-22T13:12:44Z)

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.