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
Copyright
subscription content
First Page
337
Last Page
352
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