Retrofitting Word Vectors to Semantic Lexicons
Table 2: Absolute performance changes with retrofitting. Spearman’s correlation (3 left columns) and accuracy (3 right columns) on different tasks. Higher scores are always better. Bold indicates greatest improvement for a vector type.
['Lexicon', 'MEN-3k', 'RG-65', 'WS-353', 'TOEFL', 'SYN-REL', 'SA']
All of the lexicons offer high improvements on the word similarity tasks (the first three columns). On the TOEFL task, we observe large improvements of the order of 10 absolute points in accuracy for all lexicons except for FrameNet. FrameNet’s performance is weaker, in some cases leading to worse performance (e.g., with Glove and SG vectors). For the extrinsic sentiment analysis task, we observe improvements using all the lexicons and gain 1.4% (absolute) in accuracy for the Multi vectors over the baseline. This increase is statistically significant (p<0.01, McNemar).