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Transfer Learning to Timed Text Based Video Classification Using CNN
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (Computer Science)ORCID iD: 0000-0002-0199-2377
Norwegian University of Science and Technology - NTNU, Norway. (Department of Electronic Systems)
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (Computer Sciende)ORCID iD: 0000-0003-0512-6350
2019 (English)In: Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics, Seoul, South Korea: ACM Publications, 2019, article id 23Conference paper, Published paper (Refereed)
Abstract [en]

Open educational video resources are gaining popularity with a growing number of massive open online courses (MOOCs). This has created a niche for content providers to adopt effective solutions in automatically organizing and structuring of educational resources for maximum visibility. Recent advances in deep learning techniques are proving useful in managing and classifying resources into appropriate categories. This paper proposes one such convolutional neural network (CNN) model for classifying video lectures in a MOOC setting using a transfer learning approach. The model uses a time-aligned text transcripts corresponding to video lectures from six broader subject categories. Video lectures and their corresponding transcript dataset is gathered from the Coursera MOOC platform. Two different CNN models are proposed: i) CNN based classification using embeddings learned from our MOOC dataset, ii) CNN based classification using transfer learning. Word embeddings generated from two well known state-of-the-art pre-trained models Word2Vec and GloVe, are used in the transfer learning approach for the second case.

The proposed CNN models are evaluated using precision, recall, and F1 score and the obtained performance is compared with both conventional and deep learning classifiers. The proposed CNN models have an F1 score improvement of 10-22 percentage points over DNN and conventional classifiers

Place, publisher, year, edition, pages
Seoul, South Korea: ACM Publications, 2019. article id 23
National Category
Information Systems, Social aspects
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-88590DOI: 10.1145/3326467.3326483ISBN: 978-1-4503-6190-3 (print)OAI: oai:DiVA.org:lnu-88590DiVA, id: diva2:1345448
Conference
9th International Conference on Web Intelligence, Mining and Semantics, 26-28 June, Republic of Korea
Available from: 2019-08-24 Created: 2019-08-24 Last updated: 2019-09-04Bibliographically approved

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Kastrati, ZenunKurti, Arianit

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf