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Unsupervised Open Relation Extraction

  • Hady Elsahar
  • Elena Demidova
  • Simon Gottschalk
  • Christophe Gravier
  • Frederique Laforest
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10577)

Abstract

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.

Keywords

Relation extraction Word embedding NLP 

Notes

Acknowledgements

This work was partially funded by H2020-MSCA-ITN-2014 WDAqua (64279), ALEXANDRIA (ERC 339233) and Data4UrbanMobility (BMBF).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hady Elsahar
    • 1
  • Elena Demidova
    • 2
  • Simon Gottschalk
    • 2
  • Christophe Gravier
    • 1
  • Frederique Laforest
    • 1
  1. 1.Univ Lyon, UJM-Saint-Etienne, CNRS, Laboratoire Hubert CurienLyonFrance
  2. 2.L3S Research CenterLeibniz Universität HannoverHannoverGermany

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