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Automatic Extraction of Legal Norms: Evaluation of Natural Language Processing Tools

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12331)

Abstract

Extracting and formalising legal norms from legal documents is a time-consuming and complex procedure. Therefore, the automatic methods that can accelerate this process are in high demand. In this paper, we address two major questions related to this problem: (i) what are the challenges in formalising legal documents into a machine understandable formalism? (ii) to what extent can the data-driven state-of-the-art approaches developed in the Natural Language Processing (NLP) community be used to automate the normative mining process. The results of our experiments indicate that NLP technologies such as relation extraction and semantic parsing are promising research avenues to advance research in this area.

Keywords

  • Natural Language Processing
  • Automatic rule extraction
  • Legal norms
  • Evaluation

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Fig. 1.

(adopted from [25])

Fig. 2.

Notes

  1. 1.

    Here, the term generation is used in a loose way (normative rules can be generated or extracted or distilled or built, etc.), and is not used to refer exclusively to the text generation problem study by the Natural Language Generation community.

  2. 2.

    Available at: http://bitbucket.csiro.au/users/fer201/repos/regtech-dataset.

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Ferraro, G. et al. (2020). Automatic Extraction of Legal Norms: Evaluation of Natural Language Processing Tools. In: Sakamoto, M., Okazaki, N., Mineshima, K., Satoh, K. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2019. Lecture Notes in Computer Science(), vol 12331. Springer, Cham. https://doi.org/10.1007/978-3-030-58790-1_5

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