Paper: Inclusive yet Selective: Supervised Distributional Hypernymy Detection

ACL ID C14-1097
Title Inclusive yet Selective: Supervised Distributional Hypernymy Detection
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

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.