Representing and Reconstructing PhySH: Which Embedding Competent?

Xiaoli Chen, Zhixiong Zhang


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.
Anthology ID:
2020.wosp-1.7
Volume:
Proceedings of the 8th International Workshop on Mining Scientific Publications
Month:
05 August
Year:
2020
Address:
Wuhan, China
Editors:
Petr Knoth, Christopher Stahl, Bikash Gyawali, David Pride, Suchetha N. Kunnath, Drahomira Herrmannova
Venue:
WOSP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
48–53
Language:
URL:
https://aclanthology.org/2020.wosp-1.7
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Representing and Reconstructing PhySH: Which Embedding Competent? (Chen & Zhang, WOSP 2020)
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PDF:
https://aclanthology.org/2020.wosp-1.7.pdf