Emerging Scientific Topic Discovery by Finding Infrequent Synonymous Biterms-Reference-Cited by-同舟云学术

Emerging Scientific Topic Discovery by Finding Infrequent Synonymous Biterms

Published:2022 Issue: Volume: Page:29-40
ISSN:0302-9743
Container-title:Advances in Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Wu Junfeng^ORCID,Huang Guangyan^ORCID,Zarei Roozbeh^ORCID,Li Jianxin^ORCID,Huang Guang-Li^ORCID,Zheng Hui^ORCID,He Jing^ORCID,Chi Chi-Hung^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-05933-9_3

Reference17 articles.

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4. Dridi, A., Gaber, M.M., Azad, R.M.A., Bhogal, J.: Leap2Trend: a temporal word embedding approach for instant detection of emerging scientific trends. IEEE Access 7, 176414–176428 (2019)

5. Erten, C., Harding, P.J., Kobourov, S.G., Wampler, K., Yee, G.: Exploring the computing literature using temporal graph visualization. In: Visualization and Data Analysis 2004, vol. 5295, pp. 45–56. International Society for Optics and Photonics (2004)

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1. Emerging Scientific Topic Discovery by Analyzing Reliable Patterns of Infrequent Synonymous Biterms;IEEE Transactions on Emerging Topics in Computational Intelligence;2024-02