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A Machine Learning Approach to Argument Mining in Legal Documents

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AI Approaches to the Complexity of Legal Systems (AICOL 2015, AICOL 2016, AICOL 2016, AICOL 2017, AICOL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10791))

Abstract

This study aims to analyze and evaluate the natural language arguments present in legal documents. The research is divided into three modules or stages: an Argument Element Identifier Module identifying argumentative and non-argumentative sentences in legal texts; an Argument Builder Module handling clustering of argument’s components; and an Argument Structurer Module distinguishing argument’s components (premises and conclusion). The corpus selected for this research was the set of Case-Laws issued by the European Court of Human Rights (ECHR) annotated by Mochales-Palau and Moens [8]. The preliminary results of the Argument Element Identifier Module are presented, including its main features. The performance of two machine learning algorithms (Support Vector Machine Algorithm and Random Forest Algorithm) is also measured.

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Notes

  1. 1.

    http://araucaria.computing.dundee.ac.uk/doku.php.

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Acknowledgment

The current work is funded by EMMA-WEST in the framework of the EU Erasmus Mundus Action 2.

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Authors and Affiliations

  1. Department of Informatics, University of Évora, Évora, Portugal

    Prakash Poudyal

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  1. Prakash Poudyal
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Correspondence to Prakash Poudyal .

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Editors and Affiliations

  1. University of Turin, Turin, Italy

    Ugo Pagallo

  2. University of Bologna, Bologna, Italy

    Monica Palmirani

  3. La Trobe University, Melbourne, VIC, Australia

    Pompeu Casanovas

  4. University of Bologna, Bologna, Italy

    Giovanni Sartor

  5. Inria - Sophia Antipolis-Méditerranée, Sophia Antipolis, France

    Serena Villata

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Poudyal, P. (2018). A Machine Learning Approach to Argument Mining in Legal Documents. In: Pagallo, U., Palmirani, M., Casanovas, P., Sartor, G., Villata, S. (eds) AI Approaches to the Complexity of Legal Systems. AICOL AICOL AICOL AICOL AICOL 2015 2016 2016 2017 2017. Lecture Notes in Computer Science(), vol 10791. Springer, Cham. https://doi.org/10.1007/978-3-030-00178-0_30

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  • DOI: https://doi.org/10.1007/978-3-030-00178-0_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00177-3

  • Online ISBN: 978-3-030-00178-0

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