Post-Processing of Word Representations via Variance Normalization and Dynamic Embedding
Table 2: The SRCC performance comparison (×100) for SGNS alone, SGNS+PPA and SGNS+PVN against word similarity datasets, where the last row is the average performance weighted by the pair number of each dataset.
['Type', 'SGNS', 'PPA', 'PVN(ours)']
We see that the SGNS+PVN performs better than the SGNS+PPA. We observe the largest performance gain of the SGNS+PVN reaches 5.2% in the avarage SRCC scores. It is also interesting to point out that the PVN is more robust than the PPA with different settings in d.