Deciphering Arabic question: a dedicated survey on Arabic question analysis methods, challenges, limitations and future pathways

Abstract

Abstract This survey reviews different research on question analysis, including other comparative studies of question analysis approaches and an evaluation of the questions by different NLP techniques that are used in question interpretation and categorization. Among these key findings noted includes the assessment of deep learning models such as M-BiGRU-CNN and M-TF-IDF, which come with high precision and accuracy when applied with the effectiveness of use in dealing with the complexities involved in a language. Some of the most mature machine learning algorithms, for example, SVM or logistic regression, remain powerful models, especially on the classification task, meaning that the latter continues to be relevant. This study further underlines the applicability of rule-based or hybrid methodologies in certain linguistic situations, and it must be said that custom design solutions are required. We could recommend, on this basis, directing future work towards the integration of these hybrid systems and towards the definition of more general methodologies of evaluation that are in line with the constant evolution of NLP technologies. It revealed that the underlying challenges and barriers in the domain are very complex syntactic and dialectic variations, unavailability of software tools, very critical standardization in Arabic datasets, benchmark creation, handling of translated data, and the integration of Large Language Models (LLMs). The paper discusses the lack of identity and processing of such structures through online systems for comparison. This comprehensive review highlights not only the diversified potential for the capabilities of NLP techniques in refining question analysis but also the potential way of great promises for further enhancements and improvements in this progressive domain.