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        <title>Representing and Reconstructing PhySH: Which Embedding Competent?</title>
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    <abstract>Recent advances in natural language processing make embedding representations dominate the computing language world. Though it is taken for granted, we actually have limited knowledge of how these embeddings perform in representing the complex hierarchy of domain scientific knowledge. In this paper, we conduct a comprehensive comparison of well-known embeddings’ capability in capturing the hierarchical Physics knowledge. Several key findings are: i, Poincaré embeddings do outperform if trained on PhySH taxonomy, but it fails if trained on co-occurrence pairs which are extracted from raw text. ii, No algorithm can properly learn hierarchies from the more realistic case of co-occurrence pairs, which contains more noisy relations other than hierarchical relations. iii, Our statistic analysis of Poincaré embedding’s representation of PhySH shows successful hierarchical representation share two characteristics: firstly, upper-level terms have a smaller semantic distance to root; secondly, upper-level hypernym-hyponym pairs should be further apart than lower-level hypernym-hyponym pairs.</abstract>
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%0 Conference Proceedings
%T Representing and Reconstructing PhySH: Which Embedding Competent?
%A Chen, Xiaoli
%A Zhang, Zhixiong
%Y Knoth, Petr
%Y Stahl, Christopher
%Y Gyawali, Bikash
%Y Pride, David
%Y Kunnath, Suchetha N.
%Y Herrmannova, Drahomira
%S Proceedings of the 8th International Workshop on Mining Scientific Publications
%D 2020
%8 05 aug
%I Association for Computational Linguistics
%C Wuhan, China
%F chen-zhang-2020-representing
%X Recent advances in natural language processing make embedding representations dominate the computing language world. Though it is taken for granted, we actually have limited knowledge of how these embeddings perform in representing the complex hierarchy of domain scientific knowledge. In this paper, we conduct a comprehensive comparison of well-known embeddings’ capability in capturing the hierarchical Physics knowledge. Several key findings are: i, Poincaré embeddings do outperform if trained on PhySH taxonomy, but it fails if trained on co-occurrence pairs which are extracted from raw text. ii, No algorithm can properly learn hierarchies from the more realistic case of co-occurrence pairs, which contains more noisy relations other than hierarchical relations. iii, Our statistic analysis of Poincaré embedding’s representation of PhySH shows successful hierarchical representation share two characteristics: firstly, upper-level terms have a smaller semantic distance to root; secondly, upper-level hypernym-hyponym pairs should be further apart than lower-level hypernym-hyponym pairs.
%U https://aclanthology.org/2020.wosp-1.7
%P 48-53

Markdown (Informal)

[Representing and Reconstructing PhySH: Which Embedding Competent?](https://aclanthology.org/2020.wosp-1.7) (Chen & Zhang, WOSP 2020)

• Representing and Reconstructing PhySH: Which Embedding Competent? (Chen & Zhang, WOSP 2020)

ACL

• Xiaoli Chen and Zhixiong Zhang. 2020. Representing and Reconstructing PhySH: Which Embedding Competent?. In Proceedings of the 8th International Workshop on Mining Scientific Publications, pages 48–53, Wuhan, China. Association for Computational Linguistics.