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WET: Word embedding-topic distribution vectors for MOOC video lectures dataset
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0002-0199-2377
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0003-0512-6350
Norwegian University of Science and Technology, Norway.
2020 (English)In: Data in Brief, E-ISSN 2352-3409, Vol. 28, article id 105090Article in journal (Refereed) Published
Abstract [en]

In this article, we present a dataset containing word embeddings and document topic distribution vectors generated from MOOCs video lecture transcripts. Transcripts of 12,032 video lectures from 200 courses were collected from Coursera learning platform. This large corpus of transcripts was used as input to two well-known NLP techniques, namely Word2Vec and Latent Dirichlet Allocation (LDA) to generate word embeddings and topic vectors, respectively. We used Word2Vec and LDA implementation in the Gensim package in Python. The data presented in this article are related to the research article entitled “Integrating word embeddings and document topics with deep learning in a video classification framework” [1]. The dataset is hosted in the Mendeley Data repository [2].

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 28, article id 105090
Keywords [en]
Word embedding, Document topics, Video lecture transcript, MOOC, LDA, Word2Vec
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-90820DOI: 10.1016/j.dib.2019.105090OAI: oai:DiVA.org:lnu-90820DiVA, id: diva2:1384454
Available from: 2020-01-09 Created: 2020-01-09 Last updated: 2020-01-29Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
  • en-GB
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