An Analysis of Word2Vec for the Italian Language
Computation and Language
2020-08-11 v1 Machine Learning
Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2001.09332,
title = {An Analysis of Word2Vec for the Italian Language},
author = {Giovanni Di Gennaro and Amedeo Buonanno and Antonio Di Girolamo and Armando Ospedale and Francesco A. N. Palmieri and Gianfranco Fedele},
journal= {arXiv preprint arXiv:2001.09332},
year = {2020}
}
Comments
Presented at the 2019 Italian Workshop on Neural Networks (WIRN'19) - June 2019