Neural-probabilistic methods have shown utility as a means to generate semantic vector representations of words (Bengio et al., 2003).
the Skip-gram and CBOW neural network architectures (Mikolov et al., 2013a) implemented within the word2vec and fastText software packages provide scalable approaches to online training of word vector representations that have been shown to perform well across a number of tasks (Mikolov et al., 2013a;Levy et al., 2015;.