From Hyperbolic Geometry Back to Word Embeddings
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Association for Computational Linguistics
Abstract
We choose random points in the hyperbolic disc and claim that these points are already word representations. However, it is yet to be uncovered which point corresponds to which word of the human language of interest. This correspondence can be approximately established using a pointwise mutual information between words and recent alignment techniques.
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Assylbekov Zhenisbek; Nurmukhamedov Sultan; Sheverdin Arsen; Mach Thomas. (2022). From Hyperbolic Geometry Back to Word Embeddings. Proceedings of the 7th Workshop on Representation Learning for NLP. https://doi.org/10.18653/v1/2022.repl4nlp-1.5