Abstract
With the advent of artificial intelligence and proliferation in the demand for an online dialogue system, the popularity of chatbots is growing on various industrial platforms. Their applications are getting widely noticed with intelligent tools as they are able to mimic human behavior in natural languages. Chatbots have been proven successful for many languages, such as English, Spanish, and French, over the years in varied fields like entertainment, medicine, education, and commerce. However, Arabic chatbots are challenging and are scarce, especially in the maintenance domain. Therefore, this research proposes a novel framework for an Arabic troubleshooting chatbot aiming at diagnosing and solving technical issues. The framework addresses the difficulty of using the Arabic language and the shortage of Arabic chatbot content. This research presents a realistic implementation of creating an Arabic corpus for the chatbot using the developed framework. The corpus is developed by extracting IT problems/solutions from multiple domains and reliable sources. The implementation is carried forward towards solving specific technical solutions from customer support websites taken from different well-known organizations such as Samsung, HP, and Microsoft. The claims are proved by evaluating and conducting experiments on the dataset by comparing with the previous researches done in this field using different metrics. Further, the validations are well presented by the proposed system that outperforms the previously developed different types of chatbots in terms of several parameters such as accuracy, response time, dataset data, and solutions given as per the user input.
Highlights
Every generation of the computer device is becoming more complex than the last one, and the troubleshooting task becomes more difficult for the end-user [1]
Artificial intelligence relies on data as input; it is processed via specific approach to generate the demand output like-human thinking
Use a nonsegmentation method (Holistic approach). e role of the convolutional neural network (CNN), which is one of the deep learning techniques, is deconstructed in this paper [8], detecting hand signs, by anticipating the word or recommending the most relevant word, and generating the word that deaf persons communicate with people using sign language
Summary
Nadrh Abdullah Alhassan, Abdulaziz Saad Albarrak ,1 Surbhi Bhatia ,1 and Parul Agarwal 2. Erefore, this research proposes a novel framework for an Arabic troubleshooting chatbot aiming at diagnosing and solving technical issues. E framework addresses the difficulty of using the Arabic language and the shortage of Arabic chatbot content. Is research presents a realistic implementation of creating an Arabic corpus for the chatbot using the developed framework. E corpus is developed by extracting IT problems/solutions from multiple domains and reliable sources. E implementation is carried forward towards solving specific technical solutions from customer support websites taken from different well-known organizations such as Samsung, HP, and Microsoft. The validations are well presented by the proposed system that outperforms the previously developed different types of chatbots in terms of several parameters such as accuracy, response time, dataset data, and solutions given as per the user input
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
