Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting
Table 7: Comparison with various baselines for word similarity and relatedness.
['Model', 'SimLex999', 'WS353S', 'RG65']
In order to make fair and direct comparison between Sprinkling and Retrofitting, we applied retrofitting and W-retrofitting (using inverse-path similarity over PPDB graph) on the 300 dimensional SPPMI vectors. We make the following observations. W-Retrofitting does much better than Retrofitting in similarity datasets, as what we saw with GloVe embeddings. The source of the improvement comes comes from two things: inclusion of two-hop neighbor information and the intelligent choice of weights from WordNet in W-Retrofitting.