Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
AutoTag: a collaborative approach to automated tag assignment for weblog posts
Proceedings of the 15th international conference on World Wide Web
Web 2.0: is it really different?
netWorker - Will network operators divide the web?
P-TAG: large scale automatic generation of personalized annotation tags for the web
Proceedings of the 16th international conference on World Wide Web
Tag Recommendations in Folksonomies
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Social people-tagging vs. social bookmark-tagging
EKAW'10 Proceedings of the 17th international conference on Knowledge engineering and management by the masses
Exploiting ontological relations for automatic semantic tag recommendation
Proceedings of the 7th International Conference on Semantic Systems
Uses of explicit and implicit tags in social bookmarking
Journal of the American Society for Information Science and Technology
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Although social tagging systems are becoming increasingly popular, tagging is still usually a manual process. When publishing on a social tagging system, the user is asked for the tags he wishes to assign to the resource being made available. In this paper, we present an automatic tag suggester, Tess. Our system makes recommendations based only on the textual contents of the resource and is independent of existing tags, thus allowing the emergence of novel tags. The system was evaluated by a group of users and statistical measures were applied to infer its performance. Results show that the system is not only able to suggest many useful tags, but also to discover new and relevant tags, not suggested by any of the human users.