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Design Principles of Geometric Connectionist Machines

  • Tiansi DongEmail author
Chapter
  • 254 Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 910)

Abstract

In this chapter, we introduce a novel geometric method to precisely spatialize symbolic tree structures onto vector embeddings.

References

  1. Erk, K. (2009). Supporting inferences in semantic space: Representing words as regions. In IWCS-8’09 (pp. 104–115). Stroudsburg, PA, USA: Association for Computational Linguistics.Google Scholar
  2. Fu, R., Guo, J., Qin, B., Che, W., Wang, H., & Liu, T. (2015). Learning semantic hierarchies: A continuous vector space approach. IEEE Transactions on Audio, Speech, and Language Processing, 23(3), 461–471.Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.ML2R Competence Center for Machine Learning Rhine-Ruhr, MLAI Lab, AI Foundations Group, Bonn-Aachen International Center for Information Technology (b-it)University of BonnBonnGermany

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