Decoding Queries: An In-Depth Survey of Quality Techniques for Question Analysis in Arabic Question Answering Systems

In the field of natural language processing (NLP), natural language understanding (NLU) plays a critical role in transforming human languages into machine-interpretable formats. This study explores the methodologies, and resources of Arabic Question Answering Systems (QASs), particularly emphasizing NLU’s role in question analysis and classification. This key module is essential for semantically and logically interpreting questions, which are crucial for obtaining accurate, quality, context-sensitive answers in QASs. Our findings reveal that advanced deep learning models are highly effective in accurately processing complex languages. Simultaneously, machine learning algorithms maintain significance in classification tasks. This study also identified the potential of rule-based and hybrid approaches, suggesting that future research should focus on these, alongside evolving evaluation methods, to match the rapid developments in NLP. Arabic QAS faces unique challenges, including Arabic complex syntax, dialectal diversity, scarce tools and resources, and the need for benchmark datasets and standardized testing frameworks. Future trends lean towards comprehensive datasets for deep learning, standardized testing for Arabic QASs, and a hybrid approach in question classification, incorporating additional factors such as interrogative words, tense, aspect, and multiple question detection. This comprehensive survey examined the question analysis and classification of Arabic QASs, underscoring the specific challenges and limitations of Arabic language. This study highlights NLU’s crucial role of NLU in system efficiency, aiming to fill the existing literature gaps, it proposes new research directions, contributing to a deeper understanding of Arabic QASs and emphasizing the continuous need for innovation in NLU within QASs.