Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora

July 16, 2018

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.

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AUTHORS

Written by

Max Nickel

Douwe Kiela

Stephen Roller

Publisher

ACL

Research Topics

Speech & Audio

Natural Language Processing (NLP)

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