Top

Published in:

2018 | OriginalPaper | Chapter

LeNER-Br: A Dataset for Named Entity Recognition in Brazilian Legal Text

Authors : Pedro Henrique Luz de Araujo, Teófilo E. de Campos, Renato R. R. de Oliveira, Matheus Stauffer, Samuel Couto, Paulo Bermejo

Published in: Computational Processing of the Portuguese Language

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Named entity recognition systems have the untapped potential to extract information from legal documents, which can improve information retrieval and decision-making processes. In this paper, a dataset for named entity recognition in Brazilian legal documents is presented. Unlike other Portuguese language datasets, this dataset is composed entirely of legal documents. In addition to tags for persons, locations, time entities and organizations, the dataset contains specific tags for law and legal cases entities. To establish a set of baseline results, we first performed experiments on another Portuguese dataset: Paramopama. This evaluation demonstrate that LSTM-CRF gives results that are significantly better than those previously reported. We then retrained LSTM-CRF, on our dataset and obtained \({\text {F}}_1\) scores of 97.04% and 88.82% for Legislation and Legal case entities, respectively. These results show the viability of the proposed dataset for legal applications.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter SICK-BR: A Portuguese Corpus for Inference

next chapter Contributions to the Study of Fake News in Portuguese: New Corpus and Automatic Detection Results

Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media Inc., Newton (2009)MATH

Cardellino, C., Teruel, M., Alonso Alemany, L., Villata, S.: A low-cost, high-coverage legal named entity recognizer, classifier and linker. In: Proceedings of the 16th International Conference on Artificial Intelligence and Law (ICAIL), London, United Kingdom, June 2017. https://hal.archives-ouvertes.fr/hal-01541446

de Castilho, R.E., et al.: A web-based tool for the integrated annotation of semantic and syntactic structures. In: Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH), pp. 76–84 (2016)

Dozier, C., Kondadadi, R., Light, M., Vachher, A., Veeramachaneni, S., Wudali, R.: Named entity recognition and resolution in legal text. In: Francesconi, E., Montemagni, S., Peters, W., Tiscornia, D. (eds.) Semantic Processing of Legal Texts. LNCS (LNAI), vol. 6036, pp. 27–43. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12837-0_2CrossRef

Freitas, C., Mota, C., Santos, D., Oliveira, H.G., Carvalho, P.: Second HAREM: advancing the state of the art of named entity recognition in Portuguese. In: Language Resources and Evaluation Conference (LREC). European Language Resources Association (2010)

Genthial, G.: Sequence tagging - named entity recognition with Tensorflow (2017). GitHub repository https://github.com/guillaumegenthial/sequence_tagging/tree/0048d604f7a4e15037875593b331e1268ad6e887

Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5–6), 602–610 (2005)CrossRef

Hartmann, N., Fonseca, E., Shulby, C., Treviso, M., Rodrigues, J., Aluisio, S.: Portuguese word embeddings: evaluating on word analogies and natural language tasks. In: Proceedings of Symposium in Information and Human Language Technology. Sociedade Brasileira de Computação, Uberlandia, MG, Brazil, 2–5 October 2017. https://arxiv.org/abs/1708.06025

Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef

10.

Hoekstra, R., Breuker, J., Bello, M.D., Boer, A.: The LKIF core ontology of basic legal concepts. In: Proceedings of the Workshop on Legal Ontologies and Artificial Intelligence Techniques (2007)

11.

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015). https://arxiv.org/abs/1412.6980

12.

Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning (ICML). ACM (2001). http://portal.acm.org/citation.cfm?id=655813

13.

Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. In: Proceedings of NAACL-HLT, pp. 260–270. Association for Computational Linguistics (ACL), San Diego, 12–17 June 2016. https://arxiv.org/abs/1603.01360

14.

Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1064–1074. ACL, Berlin, 7–12 August 2016. https://arxiv.org/abs/1603.01354

15.

Mansouri, A., Affendey, L.S., Mamat, A.: Named entity recognition approaches. Int. J. Comput. Sci. Netw. Secur. 8(2), 339–344 (2008)

16.

Mendonça Jr., C.A.E., Barbosa, L.A., Macedo, H.T., São Cristóvão, S.: Uma arquitetura híbrida LSTM-CNN para reconhecimento de entidades nomeadas em textos naturais em língua portuguesa. In: XIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC). SBC (2016)

17.

Mendonça Jr., C.A.E., Macedo, H., Bispo, T., Santos, F., Silva, N., Barbosa, L.: Paramopama: a Brazilian-Portuguese corpus for named entity recognition. In: XII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC). SBC (2015)

18.

Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1), 3–26 (2007)CrossRef

19.

Nothman, J., Ringland, N., Radford, W., Murphy, T., Curran, J.R.: Learning multilingual named entity recognition from Wikipedia. Artif. Intell. 194, 151–175 (2013)MathSciNetCrossRef

20.

Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

21.

Ramshaw, L.A., Marcus, M.P.: Text chunking using transformation-based learning. In: Armstrong, S., Church, K., Isabelle, P., Manzi, S., Tzoukermann, E., Yarowsky, D. (eds.) Natural Language Processing Using Very Large Corpora. TLTB, pp. 157–176. Springer, Dordrecht (1999). https://doi.org/10.1007/978-94-017-2390-9_10. Preprint available at: http://arxiv.org/abs/cmp-lg/9505040

22.

Santos, D., Cardoso, N.: A golden resource for named entity recognition in Portuguese. In: Vieira, R., Quaresma, P., Nunes, M.G.V., Mamede, N.J., Oliveira, C., Dias, M.C. (eds.) PROPOR 2006. LNCS (LNAI), vol. 3960, pp. 69–79. Springer, Heidelberg (2006). https://doi.org/10.1007/11751984_8CrossRef

23.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetMATH

24.

Tjong Kim Sang, E.F., De Meulder, F.: Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL, vol. 4, pp. 142–147. Association for Computational Linguistics (2003)

Title: LeNER-Br: A Dataset for Named Entity Recognition in Brazilian Legal Text
Authors: Pedro Henrique Luz de Araujo
Teófilo E. de Campos
Renato R. R. de Oliveira
Matheus Stauffer
Samuel Couto
Paulo Bermejo
Publisher: Springer International Publishing
Book: Computational Processing of the Portuguese Language
Print ISBN: 978-3-319-99721-6

Electronic ISBN: 978-3-319-99722-3

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-99722-3_32

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner