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Part of the book series: Studies in Computational Intelligence ((SCI,volume 910))

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Abstract

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

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References

  • 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.

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  • 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.

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Correspondence to Tiansi Dong .

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Dong, T. (2021). Design Principles of Geometric Connectionist Machines. In: A Geometric Approach to the Unification of Symbolic Structures and Neural Networks. Studies in Computational Intelligence, vol 910. Springer, Cham. https://doi.org/10.1007/978-3-030-56275-5_5

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