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Training Neural Tensor Networks with the Never Ending Language Learner

  • Flávio A. O. Santos
  • Filipe B. do Nascimento
  • Matheus S. Santos
  • Hendrik T. Macedo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 738)

Abstract

Neural Networks have become the state-of-the-art technique in the field of Natural Language Processing (NLP). Many models attempt to learn and extend facts on graph-based knowledge bases (KBs). These datasets and models are valuable resources for many NLP tasks but are occasionally limited by data incompleteness. Previous work limited the number of relationships the model would learn from. In this paper we attempt to train a Neural Tensor Network (NTN) using 97 relationships from the Never Ending Language Learner (NELL) knowledge base. We compare its performance with previous NTNs trained with 11 relationships from Wordnet and 13 relationships from Freebase. Our model has achieved significant accuracy given the limited number of tuples per relationship in NELL’s KB.

Keywords

Neural Tensor Network Never Ending Language Learning Knowledge base 

Notes

Acknowledgements

The authors thank FAPITEC-SE for granting a graduate scholarship to Flávio Santos, CNPq for granting a graduate scholarship to Filipe Nascimento and a productivity scholarship to Hendrik Macedo [DT-II, Processo 310446/2014-7] and LCAD-UFS for providing a cluster for the execution of the experiments.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Flávio A. O. Santos
    • 1
  • Filipe B. do Nascimento
    • 2
  • Matheus S. Santos
    • 2
  • Hendrik T. Macedo
    • 1
  1. 1.Computer Science Postgraduate ProgramFederal University of SergipeSão CristóvãoBrazil
  2. 2.Computer Science DepartmentFederal University of SergipeSão CristóvãoBrazil

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