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

Abstract

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 paper provides an overview of methodologies and resources that have been developed so far concerning Arabic QAS, focusing on NLU regarding question analysis and classification. These components perform an important role in obtaining accurate, quality, context-sensitive answers. Findings indicate that deep learning models work wonders for complex languages, but machine learning algorithms usually do the job in most classification tasks. Further, there is mention of the potential of rule-based and hybrid approaches, whose research in the future should be integrated with evolving evaluation methods necessary to keep pace with the advances in NLP. Challenges especially pertinent to Arabic QAS are complex syntax, dialectal diversity, limited tool support, and a lack of benchmark datasets. Other directions for the future are the development of complete datasets, standardized test bed frameworks, and extra question classification, adopting a hybrid approach considering interrogative words along with question multiplicity. This survey would therefore be helpful in highlighting shortcomings in the literature, suggesting new directions for research, and emphasizing that innovation concerning NLU within QASs is an ongoing necessity.

Keywords

• Natural language understanding (NLU)
• Arabic question answering systems
• Arabic understanding
• question analysis
• challenges in Arabic question analysis