A Geometric Connectionist Machine for Word-Senses

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


In this chapter, we follow the design principles to precisely spatialize tree structured hypernym relations among word-senses onto word embeddings. Our target is to promote each word-embedding into a ball in a higher dimension space (\(\mathscr {N}\)-Ball) such that the configuration of these balls precisely captures tree-structured hypernym relations. Each \(\mathscr {N}\)-Ball represents a word-sense. One \(\mathscr {N}\)-Ball is contained by another \(\mathscr {N}\)-Ball, if and only if the word-sense represented by the first \(\mathscr {N}\)-Ball is a hyponym of the word-sense represented by the second \(\mathscr {N}\)-Ball.


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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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