Relevant bibliographies by topics / Arabic dataset

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Arabic dataset'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Arabic dataset"

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Sarwati Rahayu, Sulis Sandiwarno, Erwin Dwika Putra, Marissa Utami, and Hadiguna Setiawan. "Model Sequential Resnet50 Untuk Pengenalan Tulisan Tangan Aksara Arab." JSAI (Journal Scientific and Applied Informatics) 6, no. 2 (2023): 234–41. http://dx.doi.org/10.36085/jsai.v6i2.5379.

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Research for Arabic handwriting recognition is still limited. The number of public datasets regarding Arabic script is still limited for this type of public dataset. Therefore, each study usually uses its dataset to conduct research. However, recently public datasets have become available and become research opportunities to compare methods with the same dataset. This study aimed to determine the implementation of the transfer learning model with the best accuracy for handwriting recognition in Arabic script. The results of the experiment using ResNet50 are as follows: training accuracy is 91.
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I. Abdalla, Mahmoud, Mohsen A. Rashwan, and Mohamed A. Elserafy. "Generating realistic Arabic handwriting dataset." International Journal of Engineering & Technology 8, no. 4 (2019): 460. http://dx.doi.org/10.14419/ijet.v8i4.29786.

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During the previous year's holistic approach showing satisfactory results to solve ‎the ‎problem of Arabic handwriting word recognition instead of word letters ‎‎segmentation.‎ ‎In this paper, we present an efficient system for ‎ generation realistic Arabic handwriting dataset from ASCII input ‎text. We carefully selected simple word list that contains most Arabic ‎letters normal and ligature connection cases. To improve the ‎performance of new letters reproduction we developed our ‎normalization method that adapt its clustering action according to ‎created Arabic letters families. We enhanced
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Altamimi, Mohammed, and Abdulaziz M. Alayba. "ANAD: Arabic news article dataset." Data in Brief 50 (October 2023): 109460. http://dx.doi.org/10.1016/j.dib.2023.109460.

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Rajih Mohammed, Zaid, and Ahmed H. Aliwy. "English-Arabic Phonetic Dataset construction." BIO Web of Conferences 97 (2024): 00057. http://dx.doi.org/10.1051/bioconf/20249700057.

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In the field of natural language processing, the effectiveness of a semantic similarity task is significantly influenced by the presence of an extensive corpus. While numerous monolingual corpora exist, predominantly in English, the availability of multilingual resources remains quite restricted. In this study, we present a semi- automated framework designed for generating a multilingual phonetic English- Arabic corpus, specifically tailored for application in multilingual phonetically and semantic similarity tasks. The proposed model consists of four phases: data gathering, preprocessing and
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Alqifari, Reem, Hend Al-Khalifa, and Simon O’Keefe. "Arabic Temporal Common Sense Understanding." Computation 13, no. 1 (2024): 5. https://doi.org/10.3390/computation13010005.

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Natural language understanding (NLU) includes temporal text understanding, which can be complex and encompasses temporal common sense understanding. There are many challenges in comprehending common sense within a text. Currently, there is a limited number of datasets containing temporal common sense in English and there is an absence of such datasets specifically for the Arabic language. In this study, an Arabic dataset was constructed based on an available English dataset. This dataset is considered a valuable resource for the Arabic community. Consequently, different multilingual pre-traine
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Elteir, Marwa K. "Fine-Grained Arabic Post (Tweet) Geolocation Prediction Using Deep Learning Techniques." Information 16, no. 1 (2025): 65. https://doi.org/10.3390/info16010065.

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Leveraging Twitter data for crisis management necessitates the accurate, fine-grained geolocation of tweets, which unfortunately is often lacking, with only 1–3% of tweets being geolocated. This work addresses the understudied problem of fine-grained geolocation prediction for Arabic tweets, focusing on the Kingdom of Saudi Arabia. The goal is to accurately assign tweets to one of thirteen provinces. Existing approaches for Arabic geolocation are limited in accuracy and often rely on basic machine learning techniques. Additionally, advancements in tweet geolocation for other languages often re
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Turki, Hussain Mohammed, Essam Al Daoud, Ghassan Samara, et al. "Arabic fake news detection using hybrid contextual features." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 836. http://dx.doi.org/10.11591/ijece.v15i1.pp836-845.

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Technology has advanced and social media users have grown dramatically in the last decade. Because social media makes information easily accessible, some people or organizations distribute false news for political or commercial gain. This news may influence elections and attitudes. Even though English fake news is widely detected and limited, Arabic fake news is hard to recognize owing to a lack of study and data collection. Wara Arabic bidirectional encoder representations from transformers (WaraBERT), a hybrid feature extraction approach, combines word level tokenization with two Arabic bidi
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Mustafa, Dheya, Safaa M. Khabour, Mousa Al-kfairy, and Ahmed Shatnawi. "Leveraging sentiment analysis of food delivery services reviews using deep learning and word embedding." PeerJ Computer Science 11 (February 19, 2025): e2669. https://doi.org/10.7717/peerj-cs.2669.

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Companies that deliver food (food delivery services, or FDS) try to use customer feedback to identify aspects where the customer experience could be improved. Consumer feedback on purchasing and receiving goods via online platforms is a crucial tool for learning about a company’s performance. Many English-language studies have been conducted on sentiment analysis (SA). Arabic is becoming one of the most extensively written languages on the World Wide Web, but because of its morphological and grammatical difficulty as well as the lack of openly accessible resources for Arabic SA, like as dictio
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Shaker, Noor Haydar, and Ban N. Dhannoon. "Word embedding for detecting cyberbullying based on recurrent neural networks." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 500. http://dx.doi.org/10.11591/ijai.v13.i1.pp500-508.

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<span lang="EN-US">The phenomenon of cyberbullying has spread and has become one of the biggest problems facing users of social media sites and generated significant adverse effects on society and the victim in particular. Finding appropriate solutions to detect and reduce cyberbullying has become necessary to mitigate its negative impacts on society and the victim. Twitter comments on two datasets are used to detect cyberbullying, the first dataset was the Arabic cyberbullying dataset, and the second was the English cyberbullying dataset. Three different pre-trained global vectors (GloV
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Shaker, Noor Haydar, and Ban N. Dhannoon. "Word embedding for detecting cyberbullying based on recurrent neural networks." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 500–508. https://doi.org/10.11591/ijai.v13.i1.pp500-508.

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The phenomenon of cyberbullying has spread and has become one of the biggest problems facing users of social media sites and generated significant adverse effects on society and the victim in particular. Finding appropriate solutions to detect and reduce cyberbullying has become necessary to mitigate its negative impacts on society and the victim. Twitter comments on two datasets are used to detect cyberbullying, the first dataset was the Arabic cyberbullying dataset, and the second was the English cyberbullying dataset. Three different pre-trained global vectors (GloVe) corpora with different
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Dissertations / Theses on the topic "Arabic dataset"

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Leighton, Carly L. "Desert dune system response to Late Quaternary environmental change in the northeastern Rub’ al Khali : advances in the application of optically stimulated luminescence datasets." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:b4821755-1971-4244-a2dd-d7ceee4fec5d.

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The application of optically stimulated luminescence (OSL) dating to desert sand dunes has allowed accumulation histories to be used as tools to infer past environmental change. In response to issues facing the interpretation of these records, two research questions are addressed in this thesis. (i) Are dune chronologies representative of dune stratigraphies? And (ii) how can we most appropriately interpret dune chronologies as records of Quaternary environmental conditions? Five dune profiles were sampled for OSL dating at two sites in the northeastern Rub’ al Khali in the southern Arabian Pe
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Book chapters on the topic "Arabic dataset"

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AlSaleh, Deem, Mashael Bin AlAmir, and Souad Larabi-Marie-Sainte. "SNAD Arabic Dataset for Deep Learning." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55180-3_47.

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Salem Al Mukhaiti, Ayesha Jumaa, Sanjeera Siddiqui, and Khaled Shaalan. "Dataset Built for Arabic Sentiment Analysis." In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64861-3_38.

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Zarnoufi, Randa, Mohammed Hajhouj, Walid Bachri, Hamid Jaafar, and Mounia Abik. "MAOffens: Moroccan Arabic Offensive Language Dataset." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80438-0_2.

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Almahdawi, Amer J., and William J. Teahan. "A New Arabic Dataset for Emotion Recognition." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22868-2_16.

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Hammoud, Jaafar, Aleksandra Vatian, Natalia Dobrenko, Nikolai Vedernikov, Anatoly Shalyto, and Natalia Gusarova. "New Arabic Medical Dataset for Diseases Classification." In Intelligent Data Engineering and Automated Learning – IDEAL 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91608-4_20.

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Abdelrazek, Aly, Walaa Medhat, Eman Gawish, and Ahmed Hassan. "Topic Modeling on Arabic Language Dataset: Comparative Study." In Advances in Model and Data Engineering in the Digitalization Era. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23119-3_5.

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Omar, Ahmed, Tarek M. Mahmoud, and Tarek Abd-El-Hafeez. "Building Online Social Network Dataset for Arabic Text Classification." In The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74690-6_48.

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Saidi, Rakia, Fethi Jarray, Asma Akacha, and Wissem Aribi. "WSDTN a Novel Dataset for Arabic Word Sense Disambiguation." In Advances in Computational Collective Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-41774-0_16.

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Elnagar, Ashraf, Yasmin S. Khalifa, and Anas Einea. "Hotel Arabic-Reviews Dataset Construction for Sentiment Analysis Applications." In Intelligent Natural Language Processing: Trends and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67056-0_3.

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El Ansari, Oumayma, Zahir Jihad, and Mousannif Hajar. "A Dataset to Support Sexist Content Detection in Arabic Text." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51935-3_14.

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Conference papers on the topic "Arabic dataset"

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Al-Dulaimi, Ahmed, Hala Adnan Fadel, and Maryam K. Hasan. "Ultimate Arabic News Dataset: A New Efficient Dataset for Arabic Text Classification." In 2024 10th International Engineering Conference on Advances in Computer and Civil Engineering (IEC). IEEE, 2024. https://doi.org/10.1109/iec61018.2024.11063800.

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Ashraf, Yasser, Yuxia Wang, Bin Gu, Preslav Nakov, and Timothy Baldwin. "Arabic Dataset for LLM Safeguard Evaluation." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-long.285.

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Alyafeai, Zaid, Khalid Almubarak, Ahmed Ashraf, et al. "CIDAR: Culturally Relevant Instruction Dataset For Arabic." In Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.764.

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Ibrahim, Mariam, Milad Ghantous, and Nada Sharaf. "Dataset Generation for Egyptian Arabic Sign Language." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013380100003890.

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Magdy, Samar Mohamed, Fakhraddin Alwajih, Sang Yun Kwon, Reem Abdel-Salam, and Muhammad Abdul-Mageed. "Gazelle: An Instruction Dataset for Arabic Writing Assistance." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.941.

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Bouchiha, Djelloul, Abdelghani Bouziane, Noureddine Doumi, et al. "WiHArD: Wikipedia Based Hierarchical Arabic Dataset for Text Classification." In 2024 4th International Conference on Embedded & Distributed Systems (EDiS). IEEE, 2024. https://doi.org/10.1109/edis63605.2024.10783418.

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Nakhleh, Saja, Ahmad M. Mustafa, and Hassan Najadat. "AraT5GQA: Arabic Question Answering model using automatic generated dataset." In 2024 15th International Conference on Information and Communication Systems (ICICS). IEEE, 2024. http://dx.doi.org/10.1109/icics63486.2024.10638274.

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Magdy, Samar Mohamed, Sang Yun Kwon, Fakhraddin Alwajih, Safaa Taher Abdelfadil, Shady Shehata, and Muhammad Abdul-Mageed. "JAWAHER: A Multidialectal Dataset of Arabic Proverbs for LLM Benchmarking." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-long.613.

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Aliah, Muhammad, Dmitry V. Berezkin, and Ilya A. Kozlov. "Enhancing Arabic Text Classification: The Impact of Dataset Variety on BERT Model." In 2025 7th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE). IEEE, 2025. https://doi.org/10.1109/reepe63962.2025.10971045.

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Jannani, Ayoub, Taoufik Amzil, Nawal Sael, and Soukaina Bouhsissin. "Sentiment-Annotated Hibapress: A Moroccan News Arabic Dataset (SAHMNAD) predicted using Fine-Tuned Arabic Language Models and Zero-Shot LLMs." In 2025 5th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). IEEE, 2025. https://doi.org/10.1109/iraset64571.2025.11008106.

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Reports on the topic "Arabic dataset"

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Arab Women: A Profile of Diversity and Change [Arabic]. Population Council, 1994. http://dx.doi.org/10.31899/pgy1994.1002.

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The status of Arab women is the subject of much speculation, generalization, and stereotyping by those inside and outside the region. The paucity of objective, accessible information makes Arab women one of the least understood social groups. The aim of this book is to help correct misconceptions about Arab women by introducing systematic information for 21 Arab countries. Widely published international statistical data, mostly from the United Nations and the World Bank, were used for the comparisons. These datasets are compiled from country reports, national surveys, and aggregated smaller st
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Energy Open Data, Energy Policy Scenario Models and Tools. King Abdullah Petroleum Studies and Research Center, 2022. http://dx.doi.org/10.30573/ks--2021-wb06.

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The workshop brought together over 40 experts from research, academia, government and industry to exchange ideas and experiences around the theme of open data for the energy sector. Participants discussed obstacles for Saudi Arabia, including the limited availability of open source data and the lack of cutting-edge tools and modeling systems, and how these challenges can be addressed to improve energy sector analysis and policy design. The event also focused on the KAPSARC Data Portal (KDP), which aggregates around 1,300 relevant datasets from 80 publishers, and how energy modelers can use it
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Arab Women: A Profile of Diversity and Change. Population Council, 1994. http://dx.doi.org/10.31899/pgy1994.1001.

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The status of Arab women is the subject of much speculation, generalization, and stereotyping by those inside and outside the region. The paucity of objective, accessible information makes Arab women one of the least understood social groups. The aim of this book is to help correct misconceptions about Arab women by introducing systematic information for 21 Arab countries. Widely published international statistical data, mostly from the United Nations and the World Bank, were used for the comparisons. These datasets are compiled from country reports, national surveys, and aggregated smaller st
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