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WET: Word embedding-topic distribution vectors for MOOC video lectures dataset

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...

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Detalles Bibliográficos
Autores principales: Kastrati, Zenun, Kurti, Arianit, Imran, Ali Shariq
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950834/
https://www.ncbi.nlm.nih.gov/pubmed/31921958
http://dx.doi.org/10.1016/j.dib.2019.105090
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author Kastrati, Zenun
Kurti, Arianit
Imran, Ali Shariq
author_facet Kastrati, Zenun
Kurti, Arianit
Imran, Ali Shariq
author_sort Kastrati, Zenun
collection PubMed
description 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].
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spelling pubmed-69508342020-01-09 WET: Word embedding-topic distribution vectors for MOOC video lectures dataset Kastrati, Zenun Kurti, Arianit Imran, Ali Shariq Data Brief Computer Science 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]. Elsevier 2020-01-03 /pmc/articles/PMC6950834/ /pubmed/31921958 http://dx.doi.org/10.1016/j.dib.2019.105090 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Computer Science
Kastrati, Zenun
Kurti, Arianit
Imran, Ali Shariq
WET: Word embedding-topic distribution vectors for MOOC video lectures dataset
title WET: Word embedding-topic distribution vectors for MOOC video lectures dataset
title_full WET: Word embedding-topic distribution vectors for MOOC video lectures dataset
title_fullStr WET: Word embedding-topic distribution vectors for MOOC video lectures dataset
title_full_unstemmed WET: Word embedding-topic distribution vectors for MOOC video lectures dataset
title_short WET: Word embedding-topic distribution vectors for MOOC video lectures dataset
title_sort wet: word embedding-topic distribution vectors for mooc video lectures dataset
topic Computer Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950834/
https://www.ncbi.nlm.nih.gov/pubmed/31921958
http://dx.doi.org/10.1016/j.dib.2019.105090
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