Neural topic model with distance awareness

Author Identifier (ORCID)

Viet Huynh: https://orcid.org/0000-0003-1308-1164

Abstract

Neural topic models (NTMs) have shown their success in topic modeling with a wide range of applications in text analysis. NTMs based on generative models prioritize document representations with good reconstruction capabilities, but they are they are insufficient in preserving distances between documents in the topic space. To bridge this gap, inspired by manifold learning, we propose a neural topic model that enables the reflection of word-to-word relationships onto topic-to-topic associations. This is achieved by approximating the distances between documents in the word space within the topic space. Extensive experiments demonstrate that the proposed model outperforms state-of-the-art NTMs in improving the quality of learned topics, as evidenced by metrics such as purity, diversity, coherence. Beyond that, the model can provide more interpretable low dimensional visualizations of documents.

Document Type

Conference Proceeding

Date of Publication

1-1-2025

Volume

15309 LNCS

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Publisher

Springer

School

School of Science

RAS ID

77618

Comments

Chen, S., Zhao, H., Huynh, V., Phung, D., & Cai, J. (2025). Neural topic model with distance awareness. In A. Antonacopoulos, S. Chaudhuri, R. Chellappa, C. L. Liu, S. Bhattacharya, & U. Pal (Eds.), Pattern recognition. ICPR 2024. Lecture notes in computer science (Vol. 15309, pp. 337-352). Springer, Cham. https://doi.org/10.1007/978-3-031-78189-6_22

Copyright

subscription content

First Page

337

Last Page

352

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Link to publisher version (DOI)

10.1007/978-3-031-78189-6_22