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Topical tags vs . non - topical tags : towards a bipartite classification ?

Abstract : In this paper we investigate whether it is possible to create a computational approach that allows us to distinguish topical tags (i. e. , talking about the topic of a resource) and non-topical tags (i. e. , describing aspects of a resource that are not related to its topic) in folksonomies , in a way that correlates with humans. Towards this goal , we collected 21M tags (1. 2M unique terms) from Delicious and we developed an unsupervised statistical algorithm that classifies such tags by applying a word space model adapted to the folksonomy space. Our algorithm analyses the co-occurrence network of tags to a target tag and exploits graph-based metrics for their classification. We validated its outcomes against a reference classification made by humans on a limited number of terms in three separate tests. The analysis of the outcomes of our algorithm shows , in some cases , a consistent disagreement among humans and between humans and our algorithm about what constitutes a topical tag , and suggests the rise of a new category of overly generic tags (i. e. , umbrella tags) .
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Contributor : Valerio Basile Connect in order to contact the contributor
Submitted on : Sunday, November 15, 2015 - 9:27:47 AM
Last modification on : Friday, October 30, 2020 - 12:04:03 PM
Long-term archiving on: : Friday, April 28, 2017 - 6:14:15 PM


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Valerio Basile, Silvio Peroni, Fabio Tamburini, Fabio Vitali. Topical tags vs . non - topical tags : towards a bipartite classification ?. Journal of Information Science, SAGE Publications, 2014, ⟨10.1177/0165551515585283⟩. ⟨hal-01228923⟩



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