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.

PDF

In Proceedings of the 25th International Conference on Computational Linguistics (COLING 2014), pp. 1025--1036, Dublin, Ireland, August 2014.

@inproceedings{roller:coling14, title={Inclusive yet Selective: Supervised Distributional Hypernymy Detection}, author={Stephen Roller and Katrin Erk and Gemma Boleda}, booktitle={Proceedings of the 25th International Conference on Computational Linguistics (COLING 2014)}, month={August}, address={Dublin, Ireland}, pages={1025--1036}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127459", year={2014} }