ENHANCING ARABIC TEXT COMPREHENSION THROUGH Automated Grammar-Aware Question Generation
| dc.contributor.author | Deema Jabi | |
| dc.contributor.author | Raneen Sawalmeh | |
| dc.date.accessioned | 2025-02-06T07:39:46Z | |
| dc.date.available | 2025-02-06T07:39:46Z | |
| dc.date.issued | 2025-02-03 | |
| dc.description.abstract | In recent years, the integration of Natural Language Processing (NLP) techniques into Arabic language education has gained significant attention. This project focuses on enhancing Arabic text comprehension through the development of an automated, grammar-aware question generation system. The system leverages advanced NLP models to analyze Arabic texts, ensuring a deep understanding of their syntactic and semantic structures. By incorporating Arabic grammar rules, the system generates contextually relevant and linguistically accurate questions tailored to the text. This approach not only aids in improving reading comprehension skills but also fosters a better understanding of Arabic grammar among learners. The project combines rule-based techniques with machine learning models, ensuring both precision and adaptability across diverse Arabic dialects and literary styles. The proposed solution has potential applications in educational platforms, language learning tools, and automated assessment systems, making it a valuable contribution to Arabic language technology and education. Keywords: Natural Language Processing, Annotation Systems, Similarity Measures, Online Quizzes. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.11888/19897 | |
| dc.language.iso | en | |
| dc.supervisor | DR. AMJAD HAWASH | |
| dc.title | ENHANCING ARABIC TEXT COMPREHENSION THROUGH Automated Grammar-Aware Question Generation | |
| dc.type | Graduation Project |
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