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Mohammed Mourchid
Ibn Tofail University

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The use of Arabic linguistic resources to develop learning applications Ilham Blanchete; Mohammed Mourchid
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp562-571

Abstract

This paper aims at explaining how Arabic linguistic resources are generated and exploited to enhance Arabic acquisition. We have adopted the root and pattern approach to generate our resources using the linguistic NooJ platform. This work has been carried out in two phases: generating the linguistic resources and developing the application that exploits the pre-built resources. First, we have generated three different resources: comprehensive verbs and masdar resources linked to each other. A nouns-and-adjectives resource, where nouns and adjectives are linked to their broken plural forms. NooJ calls these resources to apply linguistic analysis to a given corpus and returns detailed annotations, which provides accurate morphological, syntactical, and semantic properties of each analyzed word. We have also used the mixed nature of Arabic masdar to implement transformational rules, which generate nominal sentences from verbal ones and vice versa. Second, we have developed an application that provides valuable learning functionalities, like full/semi verb conjugation, the extraction of broken plural forms of a given singular form, the extraction of masdar forms of a given verb form, and return words that share the same root. The developed application can be used by teachers, students/learners, and computational linguists interested in Arabic acquisition.
Syntactic analysis of complex sentences containing Arabic psychological verbs Asmaa Amzali; Asmaa Kourtin; Mohammed Mourchid; Abdelaziz Mouloudi; Samir Mbarki
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp312-321

Abstract

Complex Arabic sentences, especially those containing Arabic psychological verbs, follow a common underlying structure characterized by two essential components: the predicate and the subject. In addition, there are two optional elements: the head and the complement. These sentences, rooted in basic noun phrases (NPs), can be expanded within the predicate, subject, or complement, resulting in compound structures. This study aims to develop a syntactic analyzer for parsing complex sentences containing Arabic psychological verbs. To achieve this, we will use the dictionary generated from the lexicon-grammar table of Arabic psychological verbs, which contains all lexical, syntactic, semantic, and transformational information related to these verbs. Then, we will extend an existing analyzer to recognize and label all grammatical structures within complex sentences containing Arabic psychological verbs. Finally, we will evaluate the efficiency of this analyzer through tests on different texts and corpora.
Enhancing Moroccan legal cases analysis through ontology-based information extraction Kaoutar Belhoucine; Nadia Zame; Mohammed Mourchid
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1081-1091

Abstract

The efficient organization of diverse disorder cases within a unified memory necessitates an adaptable representation. This study introduces an ontology-based approach for extracting facts from Moroccan legal cases. Leveraging ontological frameworks, a comprehensive case architecture is established, enabling advanced information extraction. Utilizing rules, patterns, and knowledge modeling harmonizes cases and identifies pervasive legal concepts. Statistical techniques unveil latent entities within complex legal textual discourse. Empirical validation demonstrates proficiency, extracting up to 25 regular entities. The rule-based mechanism achieves an F1-score of 99.5%, highlighting precision, while the statistical extractor achieves an 88.3% F1-score, revealing concealed entities. This work presents an innovative ontology-based paradigm for legal information extraction, contributing to advanced knowledge management in the legal domain.
Discontinuous Arabic frozen expressions modelization and implementation Asmaa Kourtin; Asmaa Amzali; Mohammed Mourchid; Samir Mbarki
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp342-349

Abstract

Frozen expressions hold significant importance in the field of natural language processing, attracting considerable attention from researchers across various languages in recent years. The Arabic language, in particular, boasts a wealth of frozen expressions inherited from the pre-Islamic and early Islamic periods, with persistent usage to the present day. This linguistic richness has motivated researchers to systematically collect, classify, and elucidate these expressions. Various classifications have emerged, addressing aspects such as continuity, discontinuity, allowance for variations, and restriction from variations. Our aim is to produce lexicon-grammar tables of discontinuous Arabic frozen expressions and implement them. Our approach involves the meticulous collection and study of these expressions, followed by the transformation of their lexicon-grammar tables into dictionaries and syntactic grammars within the NooJ platform. This methodology allows us to recognize and annotate these expressions in texts and corpora, even when they exhibit discontinuity. Such recognition has the potential to address several challenges in automatic natural language processing, including the area of automatic translation.
Co-Authors Abdelaziz Mouloudi Asmaa Amzali Asmaa Kourtin Ilham Blanchete Kaoutar Belhoucine Nadia Zame Samir Mbarki