Sökning: id:"swepub:oai:DiVA.org:lnu-88590" >
Transfer Learning t...
Transfer Learning to Timed Text Based Video Classification Using CNN
-
- Kastrati, Zenun, 1984- (författare)
- Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM),Computer Science
-
- Imran, Ali Shariq (författare)
- Norwegian University of Science and Technology - NTNU, Norway,Department of Electronic Systems
-
- Kurti, Arianit, 1977- (författare)
- Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM),Computer Sciende
-
(creator_code:org_t)
- 2019-06-26
- 2019
- Engelska.
-
Ingår i: Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics. - New York, NY, USA : ACM Publications. - 9781450361903
- Relaterad länk:
-
https://urn.kb.se/re...
-
visa fler...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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
Ämnesord
- SAMHÄLLSVETENSKAP -- Medie- och kommunikationsvetenskap -- Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning (hsv//swe)
- SOCIAL SCIENCES -- Media and Communications -- Information Systems, Social aspects (hsv//eng)
Nyckelord
- Computer Science
- Datavetenskap
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
Hitta via bibliotek
Till lärosätets databas