A Knowledge-Based Approach for Provisions’ Categorization in Arabic Normative Texts

Berrazega, Ines; Faiz, Rim; Bouhafs, Asma; Mourad, Ghassan

doi:10.1007/978-3-319-33625-1_37

Cited by 1 publication

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“…The efficiency‐increasing capacity of artificial intelligence has been recognized through the increased incorporation of AI‐based systems in the application and the administration of law (Surden, 2022). Natural Language Processing (NLP), as a subfield of artificial intelligence, has been used in several legal technologies such as automatic summarization (Gelbart & Smith, 1991; Polsley et al, 2016) and classification (de Araujo et al, 2020; Li et al, 2019) of legal documents, identification and specification of normative provisions' categories (Berrazega et al, 2016; Bouhyaoui et al, 2018), legal question answering systems (Do et al, 2017; Morimoto et al, 2017; Taniguchi et al, 2018), documents automation (Hassan & Le, 2020) and court decisions analysis (Metsker et al, 2019). A recent monograph (Frolova & Ermakova, 2022) states that prediction of court decisions is one of the captivating legal technologies that increasingly attracts the attention of legal practitioners and NLP researchers alike.…”

Section: Introductionmentioning

confidence: 99%

Prediction of court decision from Arabic documents using deep learning

Zahir

2023

Expert Systems

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The increasing amount of electronic legal documents represents a great opportunity for the development of intelligent computational systems for legal texts processing and classification. Most of these systems use classical machine learning and large datasets in English. This paper proposes an approach to automatically predict legal case outcome from written description of the events in Arabic using deep learning.An in-house corpus from the decisions of the Moroccan Court of Cassation is built and used to train a deep learning model that predicts judgement. As the created corpus is of limited size, a new data augmentation method is proposed to boost the prediction performance. Two settings for text representation are tested, namely FastText and GloVe embeddings, and multiple deep learning models architectures are tested. The proposed approach succeeds in predicting judicial decisions of the Moroccan Court of Cassation with an accuracy of 80.51% on six classes. Even with a small dataset, the proposed data augmentation method was helpful in improving the overall models' performance. Despite the advancement in the area of legal judgement prediction over the years, this work is the first attempt to predict legal outcome using the documents of the Moroccan Cassation court. The corpus created in the context of this work will be made publicly available to the community.

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Section: Introductionmentioning

confidence: 99%

Prediction of court decision from Arabic documents using deep learning

Zahir

2023

Expert Systems

View full text Add to dashboard Cite

show abstract

A Knowledge-Based Approach for Provisions’ Categorization in Arabic Normative Texts

Cited by 1 publication

References 2 publications

Prediction of court decision from Arabic documents using deep learning

Prediction of court decision from Arabic documents using deep learning

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