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    <titleInfo>
        <title>Comparing Euclidean and Hyperbolic Embeddings on the WordNet Nouns Hypernymy Graph</title>
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            <title>Proceedings of the Second Workshop on Insights from Negative Results in NLP</title>
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            <namePart type="given">João</namePart>
            <namePart type="family">Sedoc</namePart>
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            <namePart type="given">Shabnam</namePart>
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    <abstract>Nickel and Kiela (2017) present a new method for embedding tree nodes in the Poincare ball, and suggest that these hyperbolic embeddings are far more effective than Euclidean embeddings at embedding nodes in large, hierarchically structured graphs like the WordNet nouns hypernymy tree. This is especially true in low dimensions (Nickel and Kiela, 2017, Table 1). In this work, we seek to reproduce their experiments on embedding and reconstructing the WordNet nouns hypernymy graph. Counter to what they report, we find that Euclidean embeddings are able to represent this tree at least as well as Poincare embeddings, when allowed at least 50 dimensions. We note that this does not diminish the significance of their work given the impressive performance of hyperbolic embeddings in very low-dimensional settings. However, given the wide influence of their work, our aim here is to present an updated and more accurate comparison between the Euclidean and hyperbolic embeddings.</abstract>
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    <identifier type="doi">10.18653/v1/2021.insights-1.8</identifier>
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%0 Conference Proceedings
%T Comparing Euclidean and Hyperbolic Embeddings on the WordNet Nouns Hypernymy Graph
%A Bansal, Sameer
%A Benton, Adrian
%Y Sedoc, João
%Y Rogers, Anna
%Y Rumshisky, Anna
%Y Tafreshi, Shabnam
%S Proceedings of the Second Workshop on Insights from Negative Results in NLP
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F bansal-benton-2021-comparing
%X Nickel and Kiela (2017) present a new method for embedding tree nodes in the Poincare ball, and suggest that these hyperbolic embeddings are far more effective than Euclidean embeddings at embedding nodes in large, hierarchically structured graphs like the WordNet nouns hypernymy tree. This is especially true in low dimensions (Nickel and Kiela, 2017, Table 1). In this work, we seek to reproduce their experiments on embedding and reconstructing the WordNet nouns hypernymy graph. Counter to what they report, we find that Euclidean embeddings are able to represent this tree at least as well as Poincare embeddings, when allowed at least 50 dimensions. We note that this does not diminish the significance of their work given the impressive performance of hyperbolic embeddings in very low-dimensional settings. However, given the wide influence of their work, our aim here is to present an updated and more accurate comparison between the Euclidean and hyperbolic embeddings.
%R 10.18653/v1/2021.insights-1.8
%U https://aclanthology.org/2021.insights-1.8
%U https://doi.org/10.18653/v1/2021.insights-1.8
%P 49-53

Markdown (Informal)

[Comparing Euclidean and Hyperbolic Embeddings on the WordNet Nouns Hypernymy Graph](https://aclanthology.org/2021.insights-1.8) (Bansal & Benton, insights 2021)

• Comparing Euclidean and Hyperbolic Embeddings on the WordNet Nouns Hypernymy Graph (Bansal & Benton, insights 2021)

ACL

• Sameer Bansal and Adrian Benton. 2021. Comparing Euclidean and Hyperbolic Embeddings on the WordNet Nouns Hypernymy Graph. In Proceedings of the Second Workshop on Insights from Negative Results in NLP, pages 49–53, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.