Learning Taxonomic Relations from Heterogeneous Sources of Evidence

@inproceedings{Cimiano2003LearningTR,
  title={Learning Taxonomic Relations from Heterogeneous Sources of Evidence},
  author={Philipp Cimiano and Aleksander Pivk and Lars Schmidt-Thieme and Steffen Staab},
  year={2003}
}
We present a novel approach to learning taxonomic relations between terms by considering multiple and heterogeneous sources of vidence. In order to derive an optimal combination of these sources, we exploit a machine-learning approach, representing all the sources of evidence as first-or der features and training standard classifiers. We consider in particular different f a ures derived from WordNet, an approach matching Hearst-style patterns in a corpus and on the Web as well as further… CONTINUE READING
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