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Unsupervised Relation Extraction in Specialized Corpora Using Sequence Mining

  • Kata GáborEmail author
  • Haïfa Zargayouna
  • Isabelle Tellier
  • Davide Buscaldi
  • Thierry Charnois
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9897)

Abstract

This paper deals with the extraction of semantic relations from scientific texts. Pattern-based representations are compared to word embeddings in unsupervised clustering experiments, according to their potential to discover new types of semantic relations and recognize their instances. The results indicate that sequential pattern mining can significantly improve pattern-based representations, even in a completely unsupervised setting.

Keywords

Sequential Pattern Semantic Relation Parse Tree Relation Extraction Sequential Pattern Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work is part of the program “Investissements d’Avenir” overseen by the French National Research Agency, ANR-10-LABX-0083 (Labex EFL). The authors would like to thank the anonymous reviewers for their valuable comments.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Kata Gábor
    • 1
    Email author
  • Haïfa Zargayouna
    • 1
  • Isabelle Tellier
    • 2
  • Davide Buscaldi
    • 1
  • Thierry Charnois
    • 1
  1. 1.LIPN, CNRS (UMR 7030), Université Paris 13VilletaneuseFrance
  2. 2.LaTTiCe, CNRS (UMR 8094), ENS Paris, Université Sorbonne Nouvelle - Paris 3, PSL Research University, Université Sorbonne Paris CitéParisFrance

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