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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 thedoi:10.1145/2467696.2467709 dblp:conf/jcdl/GodoiTCGFFF13 fatcat:piohdinb6vaa7pniclj6fs56ny