Analysis of Italian Word Embeddings
Table 3: Vocabulary length
['[BOLD] m=1', '[BOLD] m=5', '[BOLD] Berardi']
The main trend that it is possible to notice is that accuracy increases as the number of dimensions of the embedded vectors increases. This indicates that Italian language benefits of a rich representation that can account for its rich morphology. Another important trend that emerges is the fact that the parameters have the same effect on both algorithms and that they perform very differently on all the tasks. CBOW has very low accuracy compared to SG. We can also see that the dim hyper-parameter is not correlated with the dimension of the vocabulary (model complexity) as one should expect. However the dim hyper-parameter seems to be correlated only with the accuracy in the semantic tasks while the performances on the morpho-syntactic tasks seems not to have a big bust increasing the dimensionality.