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Topic Modelling of Legal Texts Using Bidirectional Encoder Representations from Sentence Transformers

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Advances in Information Systems, Artificial Intelligence and Knowledge Management (ICIKS 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 486))

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Abstract

Topic Modeling of legal texts is a challenging task because of its complicated language structures, and technical features. Recently, there has been a big boost in the number of legislative documents, which makes it very difficult for law experts to keep up with legislation like implementing acts and analyzing cases. The importance of topics is affected by the processing and the presentation of law texts in some contexts. The aim of this work is to figure out the legal opinions from cases seen by the supreme court of the United States and the legal judgments from cases seen by the supreme court of India. In this study we used different Language Models to create sentence embeddings from those legal texts datasets. This paper employs BERTopic technique and a baseline approach in order to discover significant topics from legal opinions and legal judgment.

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Notes

  1. 1.

    https://www.courtlistener.com/.

  2. 2.

    http://www.westlawindia.com.

  3. 3.

    https://github.com/Law-AI/semantic-segmentation.

  4. 4.

    https://www.sbert.net/.

  5. 5.

    https://radimrehurek.com/gensim/index.html.

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Acknowledgments

This work was supported by Google PhD Fellowships program and by Google Cloud Platform (GCP).

Author information

Authors and Affiliations

  1. LARODEC Laboratory, University of Tunis, Tunis, Tunisia

    Eya Hammami & Rim Faiz

  2. IHEC, University of Carthage, Carthage, Tunisia

    Rim Faiz

Authors
  1. Eya Hammami
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  2. Rim Faiz
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Corresponding author

Correspondence to Eya Hammami .

Editor information

Editors and Affiliations

  1. ESC Amiens and University of Picardie Jules Verne, Amiens, France

    Inès Saad

  2. University of Paris Dauphine-PSL, Paris, France

    Camille Rosenthal-Sabroux

  3. University of Sfax, Sfax, Tunisia

    Faiez Gargouri

  4. University of Portsmouth, Portsmouth, UK

    Salem Chakhar

  5. University of Portsmouth, Portsmouth, UK

    Nigel Williams

  6. University of Portsmouth, Portsmouth, UK

    Ella Haig

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Hammami, E., Faiz, R. (2024). Topic Modelling of Legal Texts Using Bidirectional Encoder Representations from Sentence Transformers. In: Saad, I., Rosenthal-Sabroux, C., Gargouri, F., Chakhar, S., Williams, N., Haig, E. (eds) Advances in Information Systems, Artificial Intelligence and Knowledge Management. ICIKS 2023. Lecture Notes in Business Information Processing, vol 486. Springer, Cham. https://doi.org/10.1007/978-3-031-51664-1_24

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  • DOI: https://doi.org/10.1007/978-3-031-51664-1_24

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  • Print ISBN: 978-3-031-51663-4

  • Online ISBN: 978-3-031-51664-1

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