Skip to main content
SpringerLink
Log in

Using Topic Modeling in Classification of Brazilian Lawsuits

  • Conference paper
  • First Online:
Computational Processing of the Portuguese Language (PROPOR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13208))

Abstract

Legal text processing is a challenging task for modeling approaches due to the peculiarities inherent to its features, such as long texts and their technical vocabulary. Topic modeling consists of discovering a semantic structure in the text. This paper investigates the application of topic modeling and the use of information about the legislation cited in identifying the subject of legal documents and evaluating its applicability in the classification of Brazilian lawsuits. The models were trained with a Golden Collection of 16 thousand initial petitions and indictments from the Court of Justice of the State of Ceará, in Brazil, whose lawsuits were classified in the five more representative National Council of Justice (CNJ) of Brazil classes - Common Civil Procedure, Execution of Extrajudicial Title, Criminal Action - Ordinary Procedure, Special Civil Court Procedure, and Tax Enforcement. The results obtained outperform the baseline, achieving 0.89 of F1 score (macro). Our interpretation is that the representation of the document through contextual embeddings generated by BERT, as well as the architecture of the model with bidirectional contexts, makes it possible to capture the specific context of the domain of legal documents. Thus, the use of the legislation mentioned in the representation of documents can improve the accuracy of the classification task.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.cnj.jus.br/sgt/consulta_publica_classes.php.

  2. 2.

    The lemmatization process and PoS tagging were based on what is available in the spaCy library for Portuguese language (https://spacy.io/).

  3. 3.

    https://xgboost.readthedocs.io/en/latest/python/python_api.html.

References

  1. Angelov, D.: Top2Vec: Distributed Representations of Topics. arXiv:2008.09470v1 (2020)

  2. Grootendorst, M.: BERTopic: leveraging BERT and c-TF-IDF to create easily interpretable topics (2020). https://doi.org/10.5281/zenodo.4381785

  3. Remmits, Y.: Finding the Topics of Case Law: Latent Dirichlet Allocation on Supreme Court Decisions, Thesis. Radboad Universiteit (2017)

    Google Scholar 

  4. Araújo, P.H.L., Campos, T.: Topic Modelling Brazilian Supreme Court Lawsuits. JURI SAYS, vol. 113 (2020)

    Google Scholar 

  5. Neill, J.O., Robin, C., Brien, L.O., Buitelaar, P.: An Analysis of Topic Modelling for Legislative Texts. ASAIL 2017, London, UK (2017)

    Google Scholar 

  6. Devlin, J., Chang, Ming-Wei, Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics (2019)

    Google Scholar 

  7. Sumner, C., Byers, A., Boochever, R., Park, G.J.: Predicting dark triad personality traits from Twitter usage and a linguistic analysis of Tweets. In: Proceedings of ICMLA (2012). https://doi.org/10.1109/ICMLA.2012.218

  8. Pérez-Rosas, V., Mihalcea, R.: Experiments in open domain deception detection. In: Màrquez, L., Callison-Burch, C., Su, J., Pighin, D., Marton, Y. (eds.) Proceedings of EMNLP. Association for Computational Linguistics (2015). http://aclweb.org/anthology/D/D15/D15-1133.pdf

  9. Pinheiro, V., Pequeno, T., Furtado, V., Nogueira, D.: Information extraction from text based on semantic inferentialism. In: Andreasen, T., Yager, R.R., Bulskov, H., Christiansen, H., Larsen, H.L. (eds.) FQAS 2009. LNCS (LNAI), vol. 5822, pp. 333–344. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04957-6_29

    Chapter  Google Scholar 

  10. Justin, C., Cristian, D.-N.-M., Jure, L.: Anti-social behavior in online discussion communities. In: Proceedings of ICWSM (2015)

    Google Scholar 

  11. Katz, D.M., Bommarito, I.I., Michael, J.I., Blackman, J.: Predicting the Behavior of the Supreme Court of the United States: A General Approach. arXiv:1407.6333 (2014)

  12. Aletras, N., Tsarapatsanis, D., Preotiuc-Pietro, D., Lampos, V.: Predicting judicial decisions of the european court of human rights: a natural language processing perspective. PeerJ Comput. Sci. 10 (2016)

    Google Scholar 

  13. Sulea, O. M., Zampieri, M., Vela, M., vanGenabith, J.: Predicting the law area and decisions of French Supreme Court cases. In: Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP, pp. 716–722. INCOMA Ltd. (2017)

    Google Scholar 

  14. Araújo, P.H.L., Campos, T.E., Braz, F.A., Silva, N.C.: VICTOR: a dataset for Brazilian legal documents classification. In: Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pp. 1449–1458. Marseille (2020)

    Google Scholar 

  15. Neogi, P.P.G., Das, A.K., Goswami, S., Mustafi, J.: Topic modeling for text classification. In: Mandal, J.K., Bhattacharya, D. (eds.) Emerging Technology in Modelling and Graphics. AISC, vol. 937, pp. 395–407. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-7403-6_36

    Chapter  Google Scholar 

  16. Ge, J., Lin, S., Fang, Y.: A Text classification algorithm based on topic model and convolutional neural network. J. Phys.: Conf. Ser. 1748, 032036 (2021). https://doi.org/10.1088/1742-6596/1748/3/032036

  17. Luz de Araujo, P.H., de Campos, T.E., de Oliveira, R.R.R., Stauffer, M., Couto, S., Bermejo, P.: LeNER-Br: a dataset for named entity recognition in brazilian legal text. In: Villavicencio, A., et al. (eds.) PROPOR 2018. LNCS (LNAI), vol. 11122, pp. 313–323. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99722-3_32

    Chapter  Google Scholar 

  18. Reimers, N., Gurevych, I.: Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019). https://arxiv.org/pdf/1908.10084.pdf

  19. McInnes, L., Healy, J.: UMAP: Uniform manifold approximation and projection for dimension reduction, J. Open Source Softw. 3(29), 861 (2018). arXiv:1802.03426 (2018)

  20. McInnes, L., Healy, J., Astels, S.: hdbscan: hierarchical density based clustering. J. Open Source Softw. 2(11), 205 (2017). https://doi.org/10.21105/joss.00205

    Article  Google Scholar 

  21. Aguiar, A., Silveira, R., Pinheiro, V., Furtado, V., Neto, J.A.: Text classification in legal documents extracted from lawsuits in brazilian courts. In: Britto, A., Valdivia Delgado, K. (eds.) BRACIS 2021. LNCS (LNAI), vol. 13074, pp. 586–600. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91699-2_40

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Fortaleza, Fortaleza, Brazil

    André Aguiar, Vasco Furtado, Vládia Pinheiro & João A. Monteiro Neto

  2. Federal Institute of Education, Science and Technology of Ceará, Fortaleza, Brazil

    Raquel Silveira

  3. ETICE - Information Technology Company of Ceará, Fortaleza, Brazil

    Vasco Furtado

Authors
  1. André Aguiar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raquel Silveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vasco Furtado
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vládia Pinheiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. João A. Monteiro Neto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to André Aguiar .

Editor information

Editors and Affiliations

  1. Universidade de Fortaleza, Fortaleza, Brazil

    Vládia Pinheiro

  2. CiTIUS - Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    Pablo Gamallo

  3. Universidade Nova de Lisboa, Lisbon, Portugal

    Raquel Amaro

  4. University of Sheffield, Sheffield, UK

    Carolina Scarton

  5. INESC-ID, Lisbon, Portugal

    Fernando Batista

  6. Federal University of São Carlos, São Carlos, Brazil

    Diego Silva

  7. University of Lisbon, Lisbon, Portugal

    Catarina Magro

  8. Sentimonitor, Porto Alegre, Brazil

    Hugo Pinto

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Aguiar, A., Silveira, R., Furtado, V., Pinheiro, V., Neto, J.A.M. (2022). Using Topic Modeling in Classification of Brazilian Lawsuits. In: Pinheiro, V., et al. Computational Processing of the Portuguese Language. PROPOR 2022. Lecture Notes in Computer Science(), vol 13208. Springer, Cham. https://doi.org/10.1007/978-3-030-98305-5_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-98305-5_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98304-8

  • Online ISBN: 978-3-030-98305-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation