]> BibSonomy publications for/bibtex/1cf1af505b638677f00b3d3d7a5903199 Magdeburg, GermanyFrom Data and Information Analysis to Knowledge Engineering: Proceedings of the 29th Annual Conference of the German Classification Society (GfKl 2005)334--341Studies in Classification, Data Analysis, and Knowledge OrganizationLearning Ontologies to Improve Text Clustering and Classification302006imported 2008-08-15 13:59:14.0 From Data and Information Analysis to Knowledge Engineering: Proceedings of the 29th Annual Conference of the German Classification Society (GfKl 2005), March 9-11, 2005, Magdeburg, Germany334--341Studies in Classification, Data Analysis, and Knowledge OrganizationLearning Ontologies to Improve Text Clustering and Classification302006ontology-learning sb text-classification text-clustering text-mining 2008-04-25 14:50:09.0 From Data and Information Analysis to Knowledge Engineering334--341Learning Ontologies to Improve Text Clustering and Classification20062006 classification clustering myown ol text 2008-02-28 10:16:38.0Recent work has shown improvements in text clustering and classification tasks by integrating conceptual features extracted from ontologies. In this paper we present text mining experiments in the medical domain in which the ontological structures used are acquired automatically in an unsupervised learning process from the text corpus in question. We compare results obtained using the automatically learned ontologies with those obtained using manually engineered ones. Our results show that both types of ontologies improve results on text clustering and classification tasks, whereby the automatically acquired ontologies yield a improvement competitive with the manually engineered ones. ER -