Data for: Does deep learning help topic extraction? A kernel k-means clustering method with word embedding

Published: 24 Sep 2018 | Version 1 | DOI: 10.17632/kg5dcdt9b6.1

Description of this data

The 4770 dataset includes 4770 articles in the Web of Science database, covering 10 disciplines, such as artificial intelligence, business, history, and chemistry.

The 577 dataset includes 577 proposals granted by the National Science Foundation of the United States, and all the 577 proposals are within the area of computer science but are in different sub areas of computer science.

The 6767 dataset includes 6767 articles published in Journal of the Association for Information Science and Technology, Journal of Informetrics, and Scientometrics from 2000 to 2016. No labels are given for this dataset.

Experiment data files

This data is associated with the following publication:

Does deep learning help topic extraction? A kernel k-means clustering method with word embedding

Published in: Journal of Informetrics

Latest version

  • Version 1

    2018-09-24

    Published: 2018-09-24

    DOI: 10.17632/kg5dcdt9b6.1

    Cite this dataset

    Zhang, Yi; Chen, Hongshu; Liu, Feng; Zhang, Guangquan; liu, qian; Porter, Alan; Lu, Jie (2018), “Data for: Does deep learning help topic extraction? A kernel k-means clustering method with word embedding”, Mendeley Data, v1 http://dx.doi.org/10.17632/kg5dcdt9b6.1

Categories

Computer Science, Bibliometrics

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Licence

CC BY NC 3.0 Learn more

The files associated with this dataset are licensed under a Attribution-NonCommercial 3.0 Unported licence.

What does this mean?

You are free to adapt, copy or redistribute the material, providing you attribute appropriately and do not use the material for commercial purposes.

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