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 negative 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 on three datasets from different languages. The russian model. $ python -V; pip show tensorflow numpy scipy scikit-learn gensim | egrep -i '(name|version)' Python 3.5.2 :: Continuum Analytics, Inc. Name: tensorflow Version: 0.12.1 Name: numpy Version: 1.12.0 Name: scipy Version: 0.18.1 Name: scikit-learn Version: 0.18.1 Name: gensim Version: 0.13.4.1 The english-combined model has been trained using the well-known word embeddings dataset based on Google News: GoogleNews-vectors-negative300.bin on EVALution, BLESS, K&H+N, ROOT09 combined. The english-evalution model is traned on EVALution only. $ python -V; pip show tensorflow numpy scipy scikit-learn gensim | egrep -i '(name|version)' Python 3.5.2 :: Anaconda custom (64-bit) Name: tensorflow Version: 0.12.1 Name: numpy Version: 1.11.3 Name: scipy Version: 0.18.1 Name: scikit-learn Version: 0.18.1 Name: gensim Version: 0.13.4.1

We acknowledge the support of the Deutsche Forschungsgemeinschaft (DFG) foundation under the "JOIN-T" project, the Deutscher Akademischer Austauschdienst (DAAD), the Russian Foundation for Basic Research (RFBR) under the project no. 16-37-00354 мол_а, and the Russian Foundation for Humanities under the project no. 16-04-12019 "RussNet and YARN thesauri integration". We also thank Microsoft for providing computational resources under the Microsoft Azure for Research award. Finally, we are grateful to Benjamin Milde, Andrey Kutuzov, Andrew Krizhanovsky, and Martin Riedl for discussions and suggestions related to this study.

{"references": ["Ustalov, Dmitry et al. (2017). Negative Sampling Improves Hypernymy Extraction Based on Projection Learning. Proc. EACL 2017 Short Papers"]}