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AuthorSaleh, Moutaz
AuthorAljaram, Jihad Mohamad
Available date2019-11-20T08:09:53Z
Publication Date2019-05-16
Publication Name2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, JEEIT 2019 - Proceedingsen_US
CitationM. Saleh and J. M. Alja’am, "Towards Adaptive Multimedia System for Assisting Children with Arabic Learning Difficulties," 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), Amman, Jordan, 2019, pp. 794-799.
AbstractChildren with learning difficulties (LD) are increasing dramatically in the Arab world. Such children require quick intervention especially during the early childhood years. This paper presents a dynamic multimedia system for helping children with LD to overcome their learning problems. We introduce an approach to automatically convert modern standard Arabic children's stories to the finest representative images that can efficiently illustrate the meaning of words. Specifically, first, we apply natural language processing techniques to analyze the text in stories and we extract keywords of all characters and events in each sentence. Second, we apply an image captioning process through a pre-trained deep learning model for all retrieved images from our multimedia database as well as the Google search engine. Third, using sentence similarities, most significant images are retrieved back by selecting top highest similarity values. The proposed system aims to better enhance understanding, communications, and thinking skills for children with LD in elementary schools and special education centers.
SponsorNPRP grant #10-0205-170346 from the Qatar National Research Fund (a member of Qatar Foundation).
SubjectAssistive Technology
SubjectKnowledge Extraction
SubjectMultimedia Content
SubjectNatural Language Processing
SubjectSemantic Analysis
TitleTowards adaptive multimedia system for assisting children with Arabic learning difficulties
TypeConference Paper
elsevier.identifier.scopusid SCOPUS_ID:85067113256

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