Unsupervised web name disambiguation using semantic similarity and single-pass clustering

Authors:
Elias Iosif
Affiliations:
Dept of Electronics and Computer Engineering, Technical University of Crete, Chania, Greece
Venue:
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Year:
2010

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Abstract

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.