Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
February 17, 2026
Neural Networks and Rules-based Systems used to Find Rational and
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Question Answering (QA) system is a combination of Information Retrieval(IR) and Natural Language Processing (NLP) techniques. It returns a specific answer in response to user question. However, a system that can interact with the user to clarify and refine the answer is required. We propose QA system that adopts a user model for adaptation and a dialogue interface for interaction with the user combined with information retrieval and natural language techniques for Arabic Language. Our system will be able to handle users’ questions in natural language and to present answers in in respect to the user’s preferences and expected needs. The system achieved a precision of 82.05% and a dialogue success rate of 71.6%. The result is highly promising. As an extension for the present work, we need to make the system more adaptive and capable to learn and evolve with every new interactive scenario.
Waheeb Ahmed. 2017. \u201cArabic Question Answering with Dialogue Support\u201d. Global Journal of Computer Science and Technology - H: Information & Technology GJCST-H Volume 17 (GJCST Volume 17 Issue H1): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Question Answering (QA) system is a combination of Information Retrieval(IR) and Natural Language Processing (NLP) techniques. It returns a specific answer in response to user question. However, a system that can interact with the user to clarify and refine the answer is required. We propose QA system that adopts a user model for adaptation and a dialogue interface for interaction with the user combined with information retrieval and natural language techniques for Arabic Language. Our system will be able to handle users’ questions in natural language and to present answers in in respect to the user’s preferences and expected needs. The system achieved a precision of 82.05% and a dialogue success rate of 71.6%. The result is highly promising. As an extension for the present work, we need to make the system more adaptive and capable to learn and evolve with every new interactive scenario.
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