- Conference Paper
Rights / licenseCreative Commons Attribution 4.0 International
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. Show more
Book titleProceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)
Pages / Article No.
PublisherAssociation for Computational Linguistics (ACL)
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