Negative Sampling Improves Hypernymy ExtractionBased on Projection Learning
Table 2: Performance of our approach for English without clustering (k=1) and with the optimal number of cluster on the EVALution datasets (k=30) and on the combined datasets (k=25).
['[BOLD] Model', '[EMPTY]', '[ITALIC] k', 'EVALution  [BOLD] hit@1', 'EVALution  [BOLD] hit@5', 'EVALution  [BOLD] hit@10', 'EVALution  [BOLD] AUC', 'EVALution, BLESS, K&H+N, ROOT09  [ITALIC] k', 'EVALution, BLESS, K&H+N, ROOT09  [BOLD] hit@1', 'EVALution, BLESS, K&H+N, ROOT09  [BOLD] hit@5', 'EVALution, BLESS, K&H+N, ROOT09  [BOLD] hit@10', 'EVALution, BLESS, K&H+N, ROOT09  [BOLD] AUC']
S4SS3SSS0Px3 Discussion of Results. Similarly to the first experiment, our approach consistently improves results robustly across various configurations. As we change the number of clusters, types of embeddings, the size of the training data and type of relations used for negative sampling, results using our method stay superior to those of the baseline. The regularizers without re-projection (xΦ) obtain lower results in most configurations as compared to re-projected versions (xΦΦ). Overall, the neighbor regularization yields slightly better results in comparison to the asymmetric regularization. We attribute this to the fact that some synonyms z are close to the original hyponym x, while others can be distant. Thus, neighbor regularization is able to safeguard the model during training from more errors. This is also a likely reason why the performance of both regularizers is similar: the asymmetric regularization makes sure that a re-projected vector does not belong to a semantic neighborhood of the hyponym. Yet, this is exactly what neighbor regularization achieves. Note, however that neighbor regularization requires explicit negative examples, while asymmetric regularization does not.