Analysis of Italian Word Embeddings
Table 6: Solved errors
['[BOLD] Parametrization', '[BOLD] #errors solved']
As we can see from these lists the errors are done on the same words and this because they are the most common in the dataset (e.g.: in the dataset there are 217 queries that require Florida as answer compared to the 55 of Italia). However if we compare the frequency of these errors in the analogy test within the three parametrisation we can observe an improvement of approximately 15% in accuracy with SG-500 w5-m5-n10. To further investigate these improvements related to the aforementioned parametrisation we focused on one of the most frequent errors in the analogy test, the word California. As we can see from the list of the analogy test solved For example an increase in the dimensionality increases the accuracy, but mainly in analogy test with words that have a representation in the training data related to a wider set of contexts (Houston:Texas; Chicago:Illinois). The best parametrisation is obtained increasing the negative sampling. As we can see from the examples provided, the analogies are resolved thanks to a contextual similarity between the two pairs (Huntsville:Alabama; Oakland:California). In these cases the negative sampling could help to filter out from each representation those words that are not expected to be relevant for the words embeddings.