N-scheme model: An approach towards reducing Arabic language sparseness

Mohamed, Mohamed Achraf Ben; Zrigui, Sarra; Zouaghi, Anis; Zrigui, Mounir

doi:10.1109/icta.2015.7426895

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…ZRIGUI M tops the table as the most prolific author, publishing a total of 10 papers and a fractionalized contribution of 3.67. Indicating not only prolific research production, but significant participation in collaborative research efforts within the Arabic language research community [2][3][4][5][6][7][8][9][10][11]. ALOULOU C and ISMAIL Z follow with 5 articles each.…”

Section: Relevant Sourcesmentioning

confidence: 99%

Mapping the Evolution of Arabic Language Research: A Bibliometric Approach

Abdulroya Benseng,

Sakirin Tam

2024

MJALS

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This bibliometric analysis examines the progression of research on the Arabic language using a thorough dataset obtained from Scopus and shared on GitHub. This study uses quantitative measurements to analyze the field's progression, pinpointing significant trends, notable contributors, and thematic emphasis areas. The analysis shows a strong connection between linguistic studies and technology improvements through the incorporation of computer approaches like natural language processing and deep learning. Geographical contributions demonstrate a worldwide interest in Arabic language studies, with Saudi Arabia, Malaysia, Jordan, and Tunisia being the top countries in terms of scholarly output and citation effect. The study delves into a detailed examination of sociolinguistic aspects, specifically focusing on gender, in Arabic language research. The discipline is expanding dynamically and showing interdisciplinary characteristics through a consistent growth in research output and a diverse range of themes indicated by keyword analysis. The co-occurrence network analysis demonstrates the interconnection of research issues, with "Arabic languages" serving as a core node connecting several sub-disciplines. This bibliometric analysis illustrates the expansion and thematic development of Arabic language research, highlighting its growing complexity and worldwide influence. This study enhances comprehension of the present state and future trends of Arabic language studies by emphasizing the use of computational technology and the importance of international cooperation.

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Section: Relevant Sourcesmentioning

confidence: 99%

Mapping the Evolution of Arabic Language Research: A Bibliometric Approach

Abdulroya Benseng,

Sakirin Tam

2024

MJALS

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“…)معني‬ Affixes include time markers, sex and / or numbers ( ‫اعرابية‬ ‫. )حركات‬ Clitics include some prepositions ‫حروف(‬ ‫,)الجر‬ conjunctions ‫حروف(‬ ‫)العطف‬ determinants ‫,)داتمحد(‬ possessive pronouns ‫ضمائر(‬ ‫)الملكية‬ and pronouns ‫)ضمائر(‬ [17]. The Stemming process reduces the number of features extracted from a corpus by converting the words to their stems.…”

Section: Synonymsmentioning

confidence: 99%

ISAO: An Intelligent System of Opinions Analysis

Zrigui¹,

Ayadi²,

Zouaghi³

et al. 2016

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Today, the need to automatically process opinions is strongly felt. It is in this context that we situate this work whose objective is to contribute to the achievement of opinions analysis system, enabling a binary classification on a set of textual data. For this, we studied and evaluated several methods, Support Vector Machines (SVM) and Naïve Bayes (NB), on a corpus composed of 500 journals films. These models have not been satisfactory. To improve the results we have introduced a pre-treatment phase or standardization corpus before classification; this phase has improved the quality of the classification.

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Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems

Zerrouki

Balla

2017

Data in Brief

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Arabic diacritics are often missed in Arabic scripts. This feature is a handicap for new learner to read َArabic, text to speech conversion systems, reading and semantic analysis of Arabic texts.The automatic diacritization systems are the best solution to handle this issue. But such automation needs resources as diactritized texts to train and evaluate such systems.In this paper, we describe our corpus of Arabic diacritized texts. This corpus is called Tashkeela. It can be used as a linguistic resource tool for natural language processing such as automatic diacritics systems, dis-ambiguity mechanism, features and data extraction.The corpus is freely available, it contains 75 million of fully vocalized words mainly 97 books from classical and modern Arabic language.The corpus is collected from manually vocalized texts using web crawling process.

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N-scheme model: An approach towards reducing Arabic language sparseness

Cited by 3 publications

References 16 publications

Mapping the Evolution of Arabic Language Research: A Bibliometric Approach

Mapping the Evolution of Arabic Language Research: A Bibliometric Approach

ISAO: An Intelligent System of Opinions Analysis

Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems

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