Title
Hierarchical Embeddings for Hypernymy Detection and Directionality.
Abstract
We present a novel neural model HyperVec to learn hierarchical embeddings for hypernymy detection and directionality. While previous embeddings have shown limitations on prototypical hypernyms, HyperVec represents an unsupervised measure where embeddings are learned in a specific order and capture the hypernym$-$hyponym distributional hierarchy. Moreover, our model is able to generalize over unseen hypernymy pairs, when using only small sets of training data, and by mapping to other languages. Results on benchmark datasets show that HyperVec outperforms both state$-$of$-$the$-$art unsupervised measures and embedding models on hypernymy detection and directionality, and on predicting graded lexical entailment.
Year
DOI
Venue
2017
10.18653/v1/d17-1022
EMNLP
DocType
Volume
Citations 
Conference
abs/1707.07273
5
PageRank 
References 
Authors
0.40
26
4
Name
Order
Citations
PageRank
Kim Anh Nguyen1152.97
Maximilian Köper2276.18
Sabine Schulte im Walde344065.65
Ngoc Thang Vu422035.62