Challenges and opportunities for Arabic question-answering systems: current techniques and future directions

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Information Retrieval

Background:

• Arabic language presents unique challenges for NLP due to complex morphology and limited resources.
• Existing Arabic Question-Answering (QA) systems are scarce, often based on small, unavailable datasets.
• Previous reviews of Arabic QA studies are limited in scope and do not cover recent advancements.

Purpose of the Study:

• To systematically review and analyze existing Arabic QA systems.
• To identify limitations, concerns, and future research directions in Arabic QA.
• To develop a novel taxonomy for categorizing Arabic QA techniques.

Main Methods:

• Analysis of Arabic QA studies based on datasets, domains, question types, and information retrieval mechanisms.
• Systematic review of literature to identify trends and gaps.
• Development of a new taxonomy for classifying QA techniques.

Main Results:

• Identified limitations in datasets, domains, and techniques used in current Arabic QA systems.
• Highlighted the need for more comprehensive and up-to-date reviews.
• Proposed a novel taxonomy to categorize Arabic QA approaches.

Conclusions:

• Significant challenges remain in developing robust Arabic QA systems.
• Further research is needed to address resource limitations and explore advanced NLP techniques.
• The proposed taxonomy offers a structured framework for understanding and advancing Arabic QA research.