In this paper, we propose a method for name disambiguation. For a given set of names and documents we cluster the documents and map each cluster to the appropriate name. The proposed method incorporates an unsupervised metric for semantic similarity computation and a computationally low-cost clustering algorithm. We experimented with the data used in Web People Search Task of SemEval-2007, in which 16 different teams were participated. The proposed system has an equal performance compared to the officially best system. © Springer-Verlag Berlin Heidelberg 2010.
CITATION STYLE
Iosif, E. (2010). Unsupervised web name disambiguation using semantic similarity and single-pass clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6040 LNAI, pp. 133–141). https://doi.org/10.1007/978-3-642-12842-4_17
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