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    DMIR Research Group

    Publications by Andreas Hotho

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    Learning Ontologies to Improve Text Clustering and Classification

    in From Data and Information Analysis to Knowledge Engineering . Springer Berlin Heidelberg , 2006 . page 334--341 .

    Recent 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 -
    Further Information
    DOIhttp://dx.doi.org/http://dx.doi.org/10.1007/3-540-31314-1_40
    Tags2006  classification  clustering  for:dmir  myown  ol  selected  tau  text 

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    Andreas Hotho
    DMIR Research Group
    Am Hubland
    97074 Würzburg

    Phone: +49 931 31-86731
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