Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting
Table 3: Results on word similarity datasets using SS-PPMI and DSS-PPMI embeddings
['Method', 'Lexical Knowledge', 'hops', 'Similarity SimLex999', 'Similarity WS353S', 'Similarity RG65', 'Relatedness WS353R', 'Relatedness TR9856', 'No Distinction WS353', 'No Distinction MEN', 'No Distinction MTurk', 'No Distinction RW']
SS-PPMI and DSS-PPMI provide significant improvements in word similarity and analogy: We clearly observe that the proposed models defeat the baseline in all the datasets. The margin of improvement is quite high in case of similarity datasets. We see close to 0.21 increase in spearman correlation for Simlex999, 0.04 increase in RG65. This is somewhat expected as we already saw that reachability matrix contains lexical information. Interestingly, we also saw improvements in relatedness datasets where the sprinkling approaches perform narrowly better than SPPMI based approach. In other datasets like WS353, MEN we see improvements of about 0.02 and 0.03 in spearman correlation respectively. Overall, sprinkling significantly improves the performance on word similarity task.