Embeddings and topic vectors for MOOC lectures dataset
| Main Author: | Kastrati, Zenun |
|---|---|
| Other Authors: | Kurti, Arianit, Imran, Ali Shariq |
| Format: | Dataset |
| Terbitan: |
Mendeley
, 2019
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| Subjects: | |
| Online Access: |
https:/data.mendeley.com/datasets/xknjp8pxbj |
Daftar Isi:
- This dataset is comprised of word embeddings and document topic distribution vectors generated from transcripts of 12032 video lectures from 200 courses that were collected from Coursera learning platform. Two well-known natural language processing techniques, namely Word2Vec and Latent Dirichlet Allocation (LDA) implemented in the Gensim package in Python are used to generate word embeddings and topic vectors, respectively.