Inclusive yet Selective: Supervised Distributional Hypernymy Detection (2014)
Stephen Roller, Katrin Erk, and Gemma Boleda
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.
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In Proceedings of the 25th International Conference on Computational Linguistics (COLING 2014), pp. 1025--1036, Dublin, Ireland, August 2014.
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Stephen Roller Ph.D. Student roller [at] cs utexas edu