Implicit association via crowd-sourced coselection

  • Authors:

  • Helen Ashman;Michael Antunovic;Satit Chaprasit;Gavin Smith;Mark Truran

  • Affiliations:

  • University of South Australia, Adelaide, Australia;University of South Australia, Adelaide, Australia;University of South Australia, Adelaide, Australia;University of Nottingham, Nottingham, United Kingdom;University of Teesside, Middlesbrough, United Kingdom

  • Venue:
  • Proceedings of the 22nd ACM conference on Hypertext and hypermedia
  • Year:
  • 2011

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Abstract

The interaction of vast numbers of search engine users with sets of search results sets is a potential source of significant quantities of resource classification data. In this paper we discuss work which uses coselection data (i.e. multiple click-through events generated by the same user on a single search engine result page) as an indicator of mutual relevance between web resources and a means for the automatic clustering of sense-singular resources. The results indicate that coselection can be used in this way. We ground-truthed unambiguous query clustering, forming a foundation for work on automatic ambiguity detection based on the resulting number of generated clusters. Using the cluster overlap by population principle, the extension of previous work allowed determination of synonyms or lingual translations where overlapping clusters indicated the mutual relevance in coselection and subsequently the irrelevance of the actual label inherited from the user query.