Neural Topic Model with Distance Awareness
Shangyu Chen, He Zhao, Viet H. Huynh, Jianfei Cai, Dinh Phung · Research Square · 2023
Abstract Neural topic models (NTM) have shown their success in topic modelling with awide range of applications in text analysis. NTMs based on generative modelstend to have good refactoring capabilities in achieving good document represen-tations, rather than providing a topic representation that has an understandableassociation with documents and words. To bridge this gap, we have proposeda neural topic model that allows simultaneously learning topics from text dataas well as capturing the structural manifold in the original data space of doc-uments. Comprehensive experiments have shown that the proposed model canimprove learned topics in terms of intrinsic metrics (purity and coherence)and extrinsic downstream tasks (clustering, classification). Beyond that, themodel can provide interpretable low dimensional presentations of documents