A comprehensive survey of techniques for developing an Arabic question answering system
Yazeed Alkhurayyif1, Abdul Rahaman Wahab Sait2
1Department of Computer Science, Al Quwayiyah College of Sciences and Humanities, Shaqra University, Saudi Arabia.
Peerj. Computer Science
|June 22, 2023
Summary
Developing effective Arabic question-answering systems (QAS) requires focusing on datasets and deep learning techniques. This review highlights the need for unsupervised methods to overcome limitations of current supervised learning approaches in Arabic QAS.
Area of Science:
- Natural Language Processing
- Information Retrieval
- Arabic Language Technologies
Background:
- Arabic, a complex language with over 450 million speakers, presents unique challenges for Question-Answering Systems (QAS).
- Current Arabic QAS performance is often domain-specific, limiting their broad applicability.
- There's a recognized need for comprehensive strategies to enhance Arabic QAS development.
Purpose of the Study:
- To conduct a systematic literature review of strategies for developing Arabic Question-Answering Systems.
- To identify key factors influencing the performance of Arabic QAS.
- To explore emerging trends and techniques in Arabic QAS development.
Main Methods:
- Systematic literature review methodology.
- Collection and screening of 617 articles, with 40 papers included in the final analysis.
- Analysis of existing Arabic QAS features, classifications, and development techniques.
Main Results:
- Dataset quality and the application of deep learning techniques are crucial for improving Arabic QAS performance.
- Supervised learning methods currently employed in many Arabic QAS lead to performance limitations.
- Advancements in machine learning present opportunities for developing unsupervised Arabic QAS.
Conclusions:
- Future Arabic QAS development should prioritize robust datasets and advanced deep learning models.
- Exploring unsupervised learning approaches is essential to overcome the limitations of supervised methods.
- Further research into Arabic QAS development strategies is needed to meet evolving requirements.
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