Inclusive yet Selective: Supervised Distributional Hypernymy Detection
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BiRRE: Learning Bidirectional Residual Relation Embeddings for Supervised Hypernymy Detection
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 distribution…
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Detecting hypernymy relations is a key task in NLP, which is addressed in the literature using two complementary approaches. Distributional methods, whose supervised variants are the current best performers, and path-bas…
Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection
The fundamental role of hypernymy in NLP has motivated the development of many methods for the automatic identification of this relation, most of which rely on word distribution. We investigate an extensive number of suc…
Hypernym DiscoveryDistributional Inclusion Hypothesis and Quantifications: Probing for Hypernymy in Functional Distributional Semantics
Functional Distributional Semantics (FDS) models the meaning of words by truth-conditional functions. This provides a natural representation for hypernymy but no guarantee that it can be learnt when FDS models are traine…