Semantic Taxonomy Induction from Heterogenous Evidence

  title={Semantic Taxonomy Induction from Heterogenous Evidence},
  author={Rion Snow and Daniel Jurafsky and Andrew Y. Ng},
We propose a novel algorithm for inducing semantic taxonomies. Previous algorithms for taxonomy induction have typically focused on independent classifiers for discovering new single relationships based on hand-constructed or automatically discovered textual patterns. By contrast, our algorithm flexibly incorporates evidence from multiple classifiers over heterogenous relationships to optimize the entire structure of the taxonomy, using knowledge of a word’s coordinate terms to help in… CONTINUE READING
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