论文标题
主题扩散发现基于深度非负自动编码器
Topic Diffusion Discovery Based on Deep Non-negative Autoencoder
论文作者
论文摘要
研究人员对各个研究社区发表的研究文章的爆炸爆炸感到不知所措。创建了许多研究学术网站，搜索引擎和数字图书馆，以帮助研究人员确定潜在的研究主题并跟上兴趣研究的最新进展。但是，研究人员仍然很难跟踪研究主题的扩散和进化，而无需花费大量时间来审查许多相关和无关紧要的文章。在本文中，我们考虑了一种新的主题扩散发现技术。具体而言，我们建议使用深层非负自动编码器进行信息差异测量，以监视主题扩散的进化距离，以了解研究主题如何随时间变化。实验结果表明，所提出的方法能够确定研究主题的演变以及在线时尚中发现主题扩散。
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.
