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    <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.</abstract>
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%0 Conference Proceedings
%T Hierarchical Embeddings for Hypernymy Detection and Directionality
%A Nguyen, Kim Anh
%A Köper, Maximilian
%A Schulte im Walde, Sabine
%A Vu, Ngoc Thang
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F nguyen-etal-2017-hierarchical
%X 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.
%R 10.18653/v1/D17-1022
%U https://aclanthology.org/D17-1022/
%U https://doi.org/10.18653/v1/D17-1022
%P 233-243

Markdown (Informal)

[Hierarchical Embeddings for Hypernymy Detection and Directionality](https://aclanthology.org/D17-1022/) (Nguyen et al., EMNLP 2017)

• Hierarchical Embeddings for Hypernymy Detection and Directionality (Nguyen et al., EMNLP 2017)

ACL

• Kim Anh Nguyen, Maximilian Köper, Sabine Schulte im Walde, and Ngoc Thang Vu. 2017. Hierarchical Embeddings for Hypernymy Detection and Directionality. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 233–243, Copenhagen, Denmark. Association for Computational Linguistics.