Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection

@inproceedings{Chang2018DistributionalIV,
  title={Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection},
  author={Haw-Shiuan Chang and ZiYun Wang and Luke Vilnis and Andrew McCallum},
  booktitle={NAACL-HLT},
  year={2018}
}
Modeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, coreference, relation extraction, and question answering. Supervised learning from labeled hypernym sources, such as WordNet, limits the coverage of these models, which can be addressed by learning hypernyms from unlabeled text. Existing unsupervised methods either do not scale to large vocabularies or yield unacceptably poor accuracy. This paper introduces distributional… CONTINUE READING
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