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Improving Hypernymy Detection with an Integrated Path-based and Distributional Method
2016
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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-based methods, which received less research attention. We suggest an improved path-based algorithm, in which the dependency paths are encoded using a recurrent neural network, that achieves results comparable to distributional methods. We then extend the approach to integrate both
doi:10.18653/v1/p16-1226
dblp:conf/acl/ShwartzGD16
fatcat:yljga7p5t5borfg5bneh4s25bi