Neural topic model with distance awareness

Author Identifier

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

Document Type

Conference Proceeding

Publication Title

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

Volume

15309 LNCS

First Page

337

Last Page

352

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

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.

DOI

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

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