Inclusive yet Selective: Supervised Distributional Hypernymy Detection
Stephen Roller, Katrin Erk, Gemma Boleda · 2014
We test the Distributional Inclusion Hypothesis, which states that hypernyms tend to occur in a superset of contexts in which their hyponyms are found. We find that this hypothesis only holds when it is applied to relevant dimensions. We propose a robust supervised approach that achieves accuracies of.84 and.85 on two existing datasets and that can be interpreted as selecting the dimensions that are relevant for distributional inclusion. 1