Resolving Tag Ambiguity
Source:
ACM Multimedia, ACM, Vancouver, Canada (2008)
Abstract:
Tagging is an important way for users to succinctly describe the content they upload to the Internet. However, most tag-suggestion systems recommend words that are
highly correlated with the existing tag set, and thus add little information to a user's contribution.
This paper describes a means to determine the ambiguity of a set of (user-contributed) tags and suggests new tags that disambiguate the original tags. We introduce a probabilistic framework that allows us to find two tags that appear in different contexts but are both likely to co-occur with the original tag set. If such tags can be found, the current description is considered ``ambiguous'' and the two tags are recommended to the user for further clarification. In contrast to previous work, we only query the user when information is most needed and good suggestions are available.
We verify the efficacy of our approach using geographical, temporal
and semantic metadata, and a user study.
We built our system using statistics from a large (100M)
database of images and their tags.
Download:
ACM COPYRIGHT NOTICE. Copyright © 2009 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or
permissions@acm.org.
