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

ABSTRACT: 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].

SUBMITTER: Kastrati Z

PROVIDER: S-EPMC6950834 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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

Kastrati Zenun Z Kurti Arianit A Imran Ali Shariq AS

Data in brief 20200103

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 th ...[more]

PMID: 31921958

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