WETM: A word embedding-based topic model with modified collapsed Gibbs sampling for short text-Reference-Cited by-同舟云学术

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WETM: A word embedding-based topic model with modified collapsed Gibbs sampling for short text

Published:2023-08 Issue: Volume:172 Page:158-164
ISSN:0167-8655
Container-title:Pattern Recognition Letters
language:en
Short-container-title:Pattern Recognition Letters

Author:

Rashid Junaid,Kim Jungeun,Hussain Amir,Naseem Usman

Funder

Ministry of Science, ICT and Future Planning

National Research Foundation of Korea

Ministry of SMEs and Startups

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference43 articles.

1. Semantic augmented topic model over short text;Li,2018

2. GLTM: a global and local word embedding-based topic model for short texts;Liang;IEEE Access,2018

3. Probabilistic latent semantic indexing;Hofmann,1999

4. Latent Dirichlet allocation;Blei;J. Mach. Learn. Res.,2003

5. A survey on topic modeling;Divya;Int. J. Recent Adv. Eng. Technol. (IJRAET),2013

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