Awesome
UlyssesNER-Br
Repository for PT-br Legal Named Entity Recognition (NER) resources.
Spreadsheet containing the results of NER models trained with the datasets: https://docs.google.com/spreadsheets/d/1uygceS1sOaSLkBWRXzevWAcWoCNWUbv8-nppoisOXio/
Related papers:
@InProceedings{10.1007/978-3-030-98305-5_1,
author="Albuquerque, Hidelberg O.
and Costa, Rosimeire
and Silvestre, Gabriel
and Souza, Ellen
and da Silva, N{\'a}dia F. F.
and Vit{\'o}rio, Douglas
and Moriyama, Gyovana
and Martins, Lucas
and Soezima, Luiza
and Nunes, Augusto
and Siqueira, Felipe
and Tarrega, Jo{\~a}o P.
and Beinotti, Joao V.
and Dias, Marcio
and Silva, Matheus
and Gardini, Miguel
and Silva, Vinicius
and de Carvalho, Andr{\'e} C. P. L. F.
and Oliveira, Adriano L. I.",
editor="Pinheiro, Vl{\'a}dia
and Gamallo, Pablo
and Amaro, Raquel
and Scarton, Carolina
and Batista, Fernando
and Silva, Diego
and Magro, Catarina
and Pinto, Hugo",
title="UlyssesNER-Br: A Corpus of Brazilian Legislative Documents for Named Entity Recognition",
booktitle="Computational Processing of the Portuguese Language",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="3--14",
abstract="The amount of legislative documents produced within the past decade has risen dramatically, making it difficult for law practitioners to consult and update legislation. Named Entity Recognition (NER) systems have the untapped potential to extract information from legal documents, which can improve information retrieval and decision-making processes. We introduce the UlyssesNER-Br, a corpus of Brazilian Legislative Documents for NER with quality baselines. The presented corpus consists of bills and legislative consultations from Brazilian Chamber of Deputies. We implemented Conditional Random Field (CRF) and Hidden Markov Model (HMM) models, and the promising F1-score of 80.8{\%} in the analysis by categories and 81.04{\%} in the analysis by types, was achieved with the CRF model. The entities with the best average F1-score results were ``FUNDlei'' and ``DATA'', and the ones with the worst results were ``EVENTO'' and ``PESSOAgrupoind''. The corpus was also evaluated using a BiLSTM-CRF and Glove architectures provided by the pioneering state-of-the-art paper, achieving F1-score of 76.89{\%} in the analysis by categories and 59.67{\%} in the analysis by types.",
isbn="978-3-030-98305-5"
}
@InProceedings{10.1007/978-3-031-16474-3_62,
author="Costa, Rosimeire
and Albuquerque, Hidelberg Oliveira
and Silvestre, Gabriel
and Silva, N{\'a}dia F{\'e}lix F.
and Souza, Ellen
and Vit{\'o}rio, Douglas
and Nunes, Augusto
and Siqueira, Felipe
and Pedro Tarrega, Jo{\~a}o
and Vitor Beinotti, Jo{\~a}o
and de Souza Dias, M{\'a}rcio
and Pereira, Fab{\'i}ola S. F.
and Silva, Matheus
and Gardini, Miguel
and Silva, Vinicius
and de Carvalho, Andr{\'e} C. P. L. F.
and Oliveira, Adriano L. I.",
editor="Marreiros, Goreti
and Martins, Bruno
and Paiva, Ana
and Ribeiro, Bernardete
and Sardinha, Alberto",
title="Expanding UlyssesNER-Br Named Entity Recognition Corpus with Informal User-Generated Text",
booktitle="Progress in Artificial Intelligence",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="767--779",
abstract="Named Entity Recognition (NER) is a challenging Natural Language Processing task for a language as rich as Portuguese. When applied in a scenario appropriate to informal language and short texts, the task acquires a new layer of complexity, handling a particular lexicon to the domain in question. In this paper, we expanded the UlyssesNER-Br corpus for NER task with Brazilian Portuguese comments about bills. Additionally, we enriched the annotated set with a formal corpora, in order to analyze whether the combination of formal and informal texts from the same domain could improve NER. Finally, we carry out experiments with a Conditional Random Fields (CRF) model, a Bidirectional LSTM-CRF (BiLSTM-CRF) model, and subsequently, we realized fine-tuning of a language model BERT on NER task with our dataset. We concluded that formal texts helped identification of entities in informal texts. The best model was the fine-tuned BERT which achieved an F1-score of 73.90{\%}, beating the benchmark of related works.",
isbn="978-3-031-16474-3"
}