Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora

Stephen Roller, Douwe Kiela, Maximilian Nickel


Abstract
Methods for unsupervised hypernym detection may broadly be categorized according to two paradigms: pattern-based and distributional methods. In this paper, we study the performance of both approaches on several hypernymy tasks and find that simple pattern-based methods consistently outperform distributional methods on common benchmark datasets. Our results show that pattern-based models provide important contextual constraints which are not yet captured in distributional methods.
Anthology ID:
P18-2057
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
358–363
Language:
URL:
https://www.aclweb.org/anthology/P18-2057
DOI:
10.18653/v1/P18-2057
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PDF:
http://aclanthology.lst.uni-saarland.de/P18-2057.pdf
Video:
 https://vimeo.com/285803929
Presentation:
 P18-2057.Presentation.pdf