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Plant Disease Classification and Segmentation Using a Hybrid Computer-Aided Model Using GAN and Transfer Learning Read chapter Read first chapter

2024 | OriginalPaper | Chapter

Machine Reading Comprehension for the Holy Quran: A Comparative Study

Authors : Souhaila Reggad, Abderrahim Ghadi, Lotfi El Aachak, Amina Samih

Published in: Innovations in Smart Cities Applications Volume 7

Publisher: Springer Nature Switzerland

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Abstract

Question Answering (QA) has become a popular topic of research in the Natural Language Processing (NLP) community in recent years. This means that researchers and enthusiasts in the field of NLP have been actively working on developing models and improving existing ones to better answer questions. However, there are fewer studies on Arabic QA compared to other languages, and even fewer on QA for the Quran. BERT is a deep neural network model that has outperformed other models on the SQuAD benchmark. BERT is known for its ability to understand contextual information and provide accurate answers. Therefore, it is a promising model for Quranic QA. In this paper, we will abord to a comparative study of different models based on BERT and used by researchers in the religious field of MRC more precisely the Holy Quran.

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previous chapter Reinforcement Learning Algorithms and Their Applications in Education Field: A Systematic Review
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Metadata
Title
Machine Reading Comprehension for the Holy Quran: A Comparative Study
Authors
Souhaila Reggad
Abderrahim Ghadi
Lotfi El Aachak
Amina Samih
Copyright Year
2024
Book
Innovations in Smart Cities Applications Volume 7

Print ISBN: 978-3-031-54375-3

Electronic ISBN: 978-3-031-54376-0

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-031-54376-0_38

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