  Word representation is fundamental in NLP tasks, because it is precisely from
the coding of semantic closeness between words that it is possible to think of
teaching a machine to understand text. Despite the spread of word embedding
concepts, still few are the achievements in linguistic contexts other than
English. In this work, analysing the semantic capacity of the Word2Vec
algorithm, an embedding for the Italian language is produced. Parameter setting
such as the number of epochs, the size of the context window and the number of
negatively backpropagated samples is explored.
