Learning Better Word Embedding by Asymmetric Low-Rank Projection of Knowledge Graph
Table 3: Spearman’s Rank Correlation(ρ) on three Word Similarity Datasets: WS353, SCWS, and RW. Each ρ is reported as the average value of five repeated runs.
['Task/Model', '[BOLD] SG', '[BOLD] RNet', '[BOLD] SG+TransH', '[BOLD] ProjectNet']
For ProjectNet and SG+TransH, the word embedding vectors were directly used to compute the similarity scores, which is different from the analogical reasoning task. This is because there is no explicit relationship available in the evaluation process. The best performances of ProjectNet on the three datasets were obtained with the parameters setting to (mL=50,mR=95,α=0.05), (mL=40,mR=90,α=0.01), and (mL=60,mR=95,α=0.05) respectively.