Topic Diffusion Discovery Based on Deep Non-negative Autoencoder
This addresses the challenge for researchers in efficiently monitoring topic changes without extensive manual review, though it appears incremental as it builds on existing autoencoder and divergence methods.
The paper tackles the problem of tracking research topic diffusion and evolution from large volumes of articles by proposing a Deep Non-negative Autoencoder with information divergence measurement, which successfully identifies topic evolution and diffusion in an online fashion.
Researchers have been overwhelmed by the explosion of research articles published by various research communities. Many research scholarly websites, search engines, and digital libraries have been created to help researchers identify potential research topics and keep up with recent progress on research of interests. However, it is still difficult for researchers to keep track of the research topic diffusion and evolution without spending a large amount of time reviewing numerous relevant and irrelevant articles. In this paper, we consider a novel topic diffusion discovery technique. Specifically, we propose using a Deep Non-negative Autoencoder with information divergence measurement that monitors evolutionary distance of the topic diffusion to understand how research topics change with time. The experimental results show that the proposed approach is able to identify the evolution of research topics as well as to discover topic diffusions in online fashions.