A relevance feedback approach for the author name disambiguation problem

Authors:
Thiago A. Godoi;Ricardo da S. Torres;Ariadne M.B.R. Carvalho;Marcos A. Gonçalves;Anderson A. Ferreira;Weiguo Fan;Edward A. Fox
Affiliations:
University of Campinas, Campinas, Brazil;University of Campinas, Campinas, Brazil;University of Campinas, Campinas, Brazil;Federal University of Minas Gerais, Belo Horizonte, Brazil;Federal University of Ouro Preto, Ouro Preto, Brazil;Virginia Tech, Blacksburg, VA, USA;Virginia Tech, Blacksburg, VA, USA
Venue:
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Year:
2013

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Abstract

This paper presents a new name disambiguation method that exploits user feedback on ambiguous references across iterations. An unsupervised step is used to define pure training samples, and a hybrid supervised step is employed to learn a classification model for assigning references to authors. Our classification scheme combines the Optimum-Path Forest (OPF) classifier with complex reference similarity functions generated by a Genetic Programming framework. Experiments demonstrate that the proposed method yields better results than state-of-the-art disambiguation methods on two traditional datasets.