Incorporating Embedding to Topic Modeling for More Effective Short Text Analysis-Reference-Cited by-同舟云学术

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Incorporating Embedding to Topic Modeling for More Effective Short Text Analysis

Published:2023-04-30 Issue: Volume: Page:
ISSN:
Container-title:Companion Proceedings of the ACM Web Conference 2023
language:
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Author:

Rashid Junaid¹^ORCID,Kim Jungeun²^ORCID,Naseem Usman³^ORCID

Affiliation:

1. Department of Data Science, Sejong University, Seoul, Republic of Korea, Republic of Korea

2. Department of Software, Kongju National University, Cheonan, Republic of Korea, Republic of Korea

3. School of Computer Science, The University of Sydney, Sydney, Australia, Australia

Funder

National Research Foundation of Korea

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3543873.3587316

Reference21 articles.

1. David Blei, Andrew Ng, and Michael Jordan. 2001. Latent dirichlet allocation. Advances in neural information processing systems 14 (2001).

2. Density-Based Clustering Based on Hierarchical Density Estimates

3. BTM: Topic Modeling over Short Texts

4. Word2Vec

5. Latent semantic analysis

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