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dc.contributor.authorKastrati, Zenun
dc.contributor.authorImran, Ali Shariq
dc.contributor.authorKurti, Arianit
dc.identifier.citationPattern Recognition Letters. 2019, 128 85-92.nb_NO
dc.description.abstractThe advent of MOOC platforms brought an abundance of video educational content that made the selection of best fitting content for a specific topic a lengthy process. To tackle this challenge in this paper we report our research efforts of using deep learning techniques for managing and classifying educational content for various search and retrieval applications in order to provide a more personalized learning experience. In this regard, we propose a framework which takes advantages of feature representations and deep learning for classifying video lectures in a MOOC setting to aid effective search and retrieval. The framework consists of three main modules. The first module called pre-processing concerns with video-to-text conversion. The second module is transcript representation which represents text in lecture transcripts into vector space by exploiting different representation techniques including bag-of-words, embeddings, transfer learning, and topic modeling. The final module covers classifiers whose aim is to label video lectures into the appropriate categories. Two deep learning models, namely feed-forward deep neural network (DNN) and convolutional neural network (CNN) are examined as part of the classifier module. Multiple simulations are carried out on a large-scale real dataset using various feature representations and classification techniques to test and validate the proposed framework.nb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.titleIntegrating word embeddings and document topics with deep learning in a video classification frameworknb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.source.journalPattern Recognition Lettersnb_NO
dc.description.localcode© 2019. This is the authors’ accepted and refereed manuscript to the article. Locked until 21.8.2021 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
cristin.unitnameInstitutt for datateknologi og informatikk

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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal