Negative Sampling Improves Hypernymy Extraction Based on Projection Learning

Dmitry Ustalov, Nikolay Arefyev, Chris Biemann, Alexander Panchenko


Abstract
We present a new approach to extraction of hypernyms based on projection learning and word embeddings. In contrast to classification-based approaches, projection-based methods require no candidate hyponym-hypernym pairs. While it is natural to use both positive and negative training examples in supervised relation extraction, the impact of positive examples on hypernym prediction was not studied so far. In this paper, we show that explicit negative examples used for regularization of the model significantly improve performance compared to the state-of-the-art approach of Fu et al. (2014) on three datasets from different languages.
Anthology ID:
E17-2087
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
543–550
Language:
URL:
https://www.aclweb.org/anthology/E17-2087
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/E17-2087.pdf