Haq Nawaz
2025
Automated Generation of Arabic Verb Conjugations with Multilingual Urdu Translation: An NLP Approach
Haq Nawaz
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Manal Elobaid
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Ali Al-Laith
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Saif Ullah
Proceedings of the 1st Workshop on NLP for Languages Using Arabic Script
This paper presents a rule-based automated system for generating both Arabic verb conjugations and their corresponding Urdu translations. The system processes triliteral, non-weak Arabic roots across key tenses Past Simple, Past Simple Negative, Present Simple, and Present Simple Negative. Addressing the challenges posed by Arabic morphology, our rule-based approach applies patterns and morphological rules to accurately produce verb conjugations, capturing essential grammatical variations in gender, number, and person. Simultaneously, the system generates Urdu translations using predefined patterns that is aligned with the grammatical nuances of Arabic, ensuring semantic consistency. As the first system of its kind, it uniquely provides a cross-lingual resource that bridges two linguistically similar but distinct languages. By focusing on rule based precision and dual-language outputs, it addresses critical gaps in NLP resources, serving as a valuable tool for linguists, educators, and NLP researchers in academic and religious contexts where Arabic and Urdu coexist.
2022
Stars at Qur’an QA 2022: Building Automatic Extractive Question Answering Systems for the Holy Qur’an with Transformer Models and Releasing a New Dataset
Ahmed Sleem
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Eman Mohammed lotfy Elrefai
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Marwa Mohammed Matar
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Haq Nawaz
Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection
The Holy Qur’an is the most sacred book for more than 1.9 billion Muslims worldwide, and it provides a guide for their behaviours and daily interactions. Its miraculous eloquence and the divine essence of its verses (Khorami, 2014)(Elhindi,2017) make it far more difficult for non-scholars to answer their questions from the Qur’an. Here comes the significant role of technology in assisting all Muslims in answering their Qur’anic questions with state-of-the-art advancements in natural language processing (NLP) and information retrieval (IR). The task of constructing the finest automatic extractive Question Answering system from the Holy Qur’an with the use of the recently available Qur’anic Reading Comprehension Dataset(QRCD) was announced for LREC 2022 (Malhas et al., 2022) which opened up this new area for researchers around the world. In this paper, we propose a novel Qur’an Question Answering dataset with over 700 samples to aid future Qur’an research projects and three different approaches where we utilised self-attention based deep learning models (transformers) for building reliable intelligent question-answering systems for the Holy Qur’an that achieved a partial Reciprocal Rank (pRR) best score of 52% on the released QRCD test se
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Co-authors
- Ali Al-Laith 1
- Manal Elobaid 1
- Eman Mohammed lotfy Elrefai 1
- Marwa Mohammed Matar 1
- Ahmed Sleem 1
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