Negative Sampling Improves Hypernymy ExtractionBased on Projection Learning
Table 1: Performance of our approach for Russian for k=20 clusters compared to [Fu et al.2014].
['[BOLD] Model', '[EMPTY]', '[BOLD] hit@1', '[BOLD] hit@5', '[BOLD] hit@10', '[BOLD] AUC']
the baseline approach, the asymmetric regularization approach, and the neighbor regularization approach. The method reaches optimal performance for k=20 clusters. Our approach based on the regularization using synonyms as negative samples outperform the baseline (all differences between the baseline and our models are significant with respect to the t-test). According to all metrics, but hit@1 for which results are comparable to xΦ, the re-projection (xΦΦ) improves results.