A Semantic Annotation Model for Arabic Legal Texts

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

doi:10.1145/2903220.2903244

Proceedings of the 9th Hellenic Conference on Artificial Intelligence 2016

DOI: 10.1145/2903220.2903244

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A Semantic Annotation Model for Arabic Legal Texts

Ines Berrazega¹,

Rim Faiz

Asma Bouhafs

et al.

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2022

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Neural Embeddings for the Elicitation of Jurisprudence Principles: The Case of Arabic Legal Texts

Alrumayyan

Al-Yahya

2022

Applied Sciences

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In the domain of law and legal systems, jurisprudence principles (JPs) are considered major sources of legislative reasoning by jurisprudence scholars. Generally accepted JPs are often used to support the reasoning for a given jurisprudence case (JC). Although eliciting the JPs associated with a specific JC is a central task of legislative reasoning, it is complex and requires expertise, knowledge of the domain, and significant and lengthy human exertion by jurisprudence scholars. This study aimed to leverage advances in language modeling to support the task of JP elicitation. We investigated neural embeddings—specifically, doc2vec architectures—as a representation model for the task of JP elicitation using Arabic legal texts. Four experiments were conducted to evaluate three different architectures for document embedding models for the JP elicitation task. In addition, we explored an approach that integrates task-oriented word embeddings (ToWE) with document embeddings (paragraph vectors). The results of the experiments showed that using neural embeddings for the JP elicitation task is a promising approach. The paragraph vector distributed bag-of-words (PV-DBOW) architecture produced the best results for this task. To evaluate how well the ToWE model performed for the JP elicitation task, a graded relevance ranking measure, discounted cumulative gain (DCG), was used. The model achieved good results with a normalized DCG of 0.9 for the majority of the JPs. The findings of this study have significant implications for the understanding of how Arabic legal texts can be modeled and how the semantics of jurisprudence principles can be elicited using neural embeddings.

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Neural Embeddings for the Elicitation of Jurisprudence Principles: The Case of Arabic Legal Texts

Alrumayyan

Al-Yahya

2022

Applied Sciences

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A Semantic Annotation Model for Arabic Legal Texts

Cited by 1 publication

References 3 publications

Neural Embeddings for the Elicitation of Jurisprudence Principles: The Case of Arabic Legal Texts

Neural Embeddings for the Elicitation of Jurisprudence Principles: The Case of Arabic Legal Texts

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