Title: Full Text Citation Analysis for Scientific Recommendation
While different citation analysis studies employed various sophisticated network analysis methods for scientific characterization, the basic assumption was easy and straightforward: either Publication1 cites Publication2, or Author1 cites Author2, regardless of sentiment, reason, topic, or motivation. More recent studies have shown, however, that this assumption is oversimplified. For this proposed research, by using citation context (extract from full-text data), we will characterize each scientific publication/venue/author along with each citation relation on a scholarly network differently by using labeled topic modeling method. More importantly, based on our experiment result, we found full-text citation can significantly enhance the scientific recommendation performance.
Posted October 26, 2012