Unsupervised Open Relation Extraction

  title={Unsupervised Open Relation Extraction},
  author={Hady ElSahar and E. Demidova and S. Gottschalk and C. Gravier and F. Laforest},
  • Hady ElSahar, E. Demidova, +2 authors F. Laforest
  • Published in ESWC 2017
  • Computer Science
  • We explore methods to extract relations between named entities from free text in an unsupervised setting. In addition to standard feature extraction, we develop a novel method to re-weight word embeddings. We alleviate the problem of features sparsity using an individual feature reduction. Our approach exhibits a significant improvement by \(5.8\%\) over the state-of-the-art relation clustering scoring a F1-score of 0.416 on the NYT-FB dataset. 
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