Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings

Matthew Le, Stephen Roller, Laetitia Papaxanthos, Douwe Kiela, Maximilian Nickel


Abstract
We consider the task of inferring “is-a” relationships from large text corpora. For this purpose, we propose a new method combining hyperbolic embeddings and Hearst patterns. This approach allows us to set appropriate constraints for inferring concept hierarchies from distributional contexts while also being able to predict missing “is-a”-relationships and to correct wrong extractions. Moreover – and in contrast with other methods – the hierarchical nature of hyperbolic space allows us to learn highly efficient representations and to improve the taxonomic consistency of the inferred hierarchies. Experimentally, we show that our approach achieves state-of-the-art performance on several commonly-used benchmarks.
Anthology ID:
P19-1313
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3231–3241
Language:
URL:
https://www.aclweb.org/anthology/P19-1313
DOI:
10.18653/v1/P19-1313
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1313.pdf