1 INTRODUCTION
Table 4: Comparison with raw distributions and other models for 200-dimensional word vector representations. A symmetric context window of ten words is used. Spearman rank correlation is reported on word similarity tasks. Accuracy is reported on word analogy tasks.
['[EMPTY]', '[BOLD] WS', '[BOLD] RG', '[BOLD] RW', '[BOLD] SYN.', '[BOLD] SEM.']
We use a symmetric context window of ten words, and the default values set by the authors for the other hyperparameters. We also compare these models directly with the raw distributions, computing similarities between them with the Hellinger distance. Using the raw probability distributions yields good results on the semantic task, while a dimension reduction with a stochastic low-rank approximation gives a better solution to compete with others on similarity and syntactic tasks.