Incorporating user feedback into name disambiguation of scientific cooperation network

Authors:
Yuhua Li;Aiming Wen;Quan Lin;Ruixuan Li;Zhengding Lu
Affiliations:
Intelligent and Distributed Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, P.R. China;Intelligent and Distributed Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, P.R. China;Intelligent and Distributed Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, P.R. China;Intelligent and Distributed Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, P.R. China;Intelligent and Distributed Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, P.R. China
Venue:
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Year:
2011

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Abstract

In scientific cooperation network, ambiguous author names may occur due to the existence of multiple authors with the same name. Users of these networks usually want to know the exact author of a paper, whereas we do not have any unique identifier to distinguish them. In this paper, we focus ourselves on such problem, we propose a new method that incorporates user feedback into the model for name disambiguation of scientific cooperation network. Perceptron is used as the classifier. Two features and a constraint drawn from user feedback are incorporated into the perceptron to enhance the performance of name disambiguation. Specifically, we construct user feedback as a training stream, and refine the perceptron continuously. Experimental results show that the proposed algorithm can learn continuously and significantly outperforms the previous methods without introducing user interactions.