Retrofitting Word Vectors to Semantic Lexicons
Table 3: Absolute performance changes for including PPDB information while training LBL vectors. Spearman’s correlation (3 left columns) and accuracy (3 right columns) on different tasks. Bold indicates greatest improvement.
['Method', '[ITALIC] k,  [ITALIC] γ', 'MEN-3k', 'RG-65', 'WS-353', 'TOEFL', 'SYN-REL', 'SA']
S6SS2SSS0Px1 Results. For lazy, γ=0.01 performs best, but the method is in most cases not highly sensitive to γ’s value. For periodic, which overall leads to greater improvements over the baseline than lazy, k=50M performs best, although all other values of k also outperform the the baseline. Retrofitting, which can be applied to any word vectors, regardless of how they are trained, is competitive and sometimes better.