Topic Diffusion Discovery Based on Deep Non-negative Autoencoder
- URL: http://arxiv.org/abs/2010.03710v1
- Date: Thu, 8 Oct 2020 00:58:10 GMT
- Title: Topic Diffusion Discovery Based on Deep Non-negative Autoencoder
- Authors: Sheng-Tai Huang, Yihuang Kang, Shao-Min Hung, Bowen Kuo, I-Ling Cheng
- Abstract summary: We propose using a Deep Non-negative Autoencoder with information divergence measurement to monitor topic diffusion.
The proposed approach is able to identify the evolution of research topics as well as to discover topic diffusions in online fashions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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