Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection

Haw-Shiuan Chang, Ziyun Wang, Luke Vilnis, Andrew McCallum


Abstract
Modeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, 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 inclusion vector embedding (DIVE), a simple-to-implement unsupervised method of hypernym discovery via per-word non-negative vector embeddings which preserve the inclusion property of word contexts. In experimental evaluations more comprehensive than any previous literature of which we are aware—evaluating on 11 datasets using multiple existing as well as newly proposed scoring functions—we find that our method provides up to double the precision of previous unsupervised methods, and the highest average performance, using a much more compact word representation, and yielding many new state-of-the-art results.
Anthology ID:
N18-1045
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
485–495
Language:
URL:
https://aclanthology.org/N18-1045
DOI:
10.18653/v1/N18-1045
Bibkey:
Cite (ACL):
Haw-Shiuan Chang, Ziyun Wang, Luke Vilnis, and Andrew McCallum. 2018. Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 485–495, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection (Chang et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1045.pdf
Note:
 N18-1045.Notes.pdf
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