—Disambiguating tag senses can benefit many applications leveraging folksonomies as knowledge sources. In this paper, we propose an unsupervised tag sense disambiguation approach. For a target tag, we model all the annotations involving it with a 3-order tensor to fully explore the multi-type interrelated data. We perform spectral clustering over the hypergraph induced from the 3-order tensor to discover the clusters representing the senses of the target tag. We conduct experiments on a dataset collected from a real-world system. Both the supervised and unsupervised evaluation results demonstrate the effectiveness of the proposed approach.
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