@inproceedings{Hotho2003,
	title = {WordNet improves text document clustering},
	author = {Andreas Hotho and Steffen Staab and Gerd Stumme},
	booktitle = {Semantic Web Workshop at the Annual International ACM SIGIR Conference
	on Research and Development in Information Retrieval},
	url = {www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003wordnet.pdf},
	year = {2003},
	biburl = {http://www.bibsonomy.org/bibtex/2b8c511f87f1f59dcb8aa3ee03ddd7bec/marcoalvarez},
	abstract = {Text document clustering plays an important role in providing intuitive
	navigation and browsing mechanisms by organizing large amounts of
	information into a small number of meaningful clusters. The bag of
	words representation used for these clustering methods is often unsatisfactory
	as it ignores relationships between important terms that do not co-occur
	literally. In order to deal with the problem, we integrate background
	knowledge --- in our application Wordnet --- into the process of
	clustering text documents. We cluster the documents by a standard
	partitional algorithm. Our experimental evaluation on Reuters newsfeeds
	compares clustering results with pre-categorizations of news. In
	the experiments, improvements of results by background knowledge
	compared to the baseline can be shown for many interesting tasks.},
	keywords = {Clustering WordNet }
}