A conditional random field model for name disambiguation in National Natural Science Foundation of China fund. Issue 2 (June 2018)

• Record Type: Journal Article
• Authors: Si, Hang Jun; Tong, Weiqin; Kausar, Samina
• Is Part Of: Journal of algorithms & computational technology. Volume 12:Issue 2(2018)
• Page Start: 91
• Page End: 100
• Publication Date: 2018-06
• Subjects: Name disambiguation -- conditional random field -- National Natural Science Foundation of China fund -- clustering; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748301817751481
• Languages: English
• ISSNs: 1748-3018

Abstract

The name ambiguity problem affects the accuracy of the web search, document retrieval, and information fusion. A lot of work has been done to solve the name disambiguation problem for publication or paper, but we propose a model to solve this problem for the National Natural Science Foundation of China fund. In this paper, we propose a probabilistic Markov random fields framework to solve the problem of the National Natural Science Foundation of China fund name disambiguation. We define an objective function and use parameters learning algorithm to get the suitable parameters. Experimental results indicate that our approach significantly outperforms other different traditional clustering methods.