Discovering word senses from text
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploring social annotations for the semantic web
Proceedings of the 15th international conference on World Wide Web
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Automatic labeling of multinomial topic models
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Tag Recommendations in Folksonomies
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Exploring content models for multi-document summarization
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems
Towards ontology learning from folksonomies
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Best topic word selection for topic labelling
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Folksonomies constitute an important type of Web 2.0 services, where users collectively annotate (or "tag") resources to create custom categories. Semantic relation of these categories hint at the possibility of another categorization at a higher level. Discovering these more general categories, called "topics", is an important task. One problem is to discover these semantically coherent topics and the accompanying small sets of tags that cover these topics in order to facilitate more detailed item search. Another important problem is to find words/phrases that describe these topics, i.e. labels or "meta-tag"s. These labeled topics can immensely increase the item search efficiency of users in a folksonomy service. However, this possibility has not been sufficiently exploited to date. In this paper, a probabilistic model is used to identify topics in a folksonomy, which are then associated with relevant, descriptive meta-tags. In addition, a small set of diverse and relevant tags are found which cover the semantics of the topic well. The resulting topics form a personalized categorization of folksonomy data due to the personalized nature of the model employed. The results show that the proposed method is successful at discovering important topics and the corresponding identifying meta-tags.