Enhancing Topic Interpretability for Neural Topic Modeling Through Topic-Wise Contrastive Learning-Reference-Cited by-同舟云学术

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Enhancing Topic Interpretability for Neural Topic Modeling Through Topic-Wise Contrastive Learning

Published:2024-05-13 Issue: Volume:34 Page:584-597
ISSN:
Container-title:2024 IEEE 40th International Conference on Data Engineering (ICDE)
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Author:

Gao Xin¹,Lin Yang¹,Li Ruiqing¹,Wang Yasha²,Chu Xu³,Ma Xinyu¹,Yu Hailong¹

Affiliation:

1. Software Engineering Institution, School of Computer Science, Peking University,Beijing,China

2. Key Lab of High Confidence Software Technologies, Ministry of Education,Beijing,China

3. School of Computer Science, Peking University,Center on Frontiers of Computing Studies

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10597630/10597390/10597746.pdf?arnumber=10597746

Reference51 articles.

1. Survival Topic Models for Predicting Outcomes for Trauma Patients

2. Source-LDA: Enhancing Probabilistic Topic Models Using Prior Knowledge Sources

3. Is automated topic model evaluation broken? the incoherence of coherence;Hoyle;Advances in Neural Information Processing Systems,2021

4. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey

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