Question answering (QA) is a distinguished domain within information retrieva‎l, dedicated to the task of providing precise answers to questions formulated in natural language. Unlike conventional search engines that yield a collection of related documents, a QA system is designed to deliver direct and accurate responses. The architecture of a QA system encompasses three critical modules question analysis, passage retrieva‎l, and answer extraction. Despite the significant progress in QA systems for various languages, the development of Arabic QA systems has been notably impeded by intricate linguistic challenges and a pronounced shortage of resources and tools accessible to researchers. This study undertakes a comprehensive review of the scientific literature that has utilized deep learning methods on QA systems, with an emphasis on those tailored for the Arabic language, highlighting the existing gaps and proposing avenues for future research advancements.

12/11/2024 12:00:00 AM

85814

1403/11/23

deep neural network approach for arabic community question answering

سيستم هاي پاسخگويي به سوالات , پاسخگويي به سوالات جامعه , يادگيري عميق

Question answering systems , Community Question answering , Deep learning