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Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
The hypernymy detection task has been addressed under various frameworks. Previously, the design of unsupervised hypernymy scores has been extensively studied. In contrast, supervised classifiers, especially distributional models, leverage the global contexts of terms to make predictions, but are more likely to suffer from "lexical memorization". In this work, we revisit supervised distributional models for hypernymy detection. Rather than taking embeddings of two terms as classificationdoi:10.18653/v1/2020.acl-main.334 fatcat:uwzxoemx35dwteetbozh5yke2q