For all models, we normalize the embeddings so that the L-2 norm equals 1, which is important in measuring semantic similarity via inner product.
for unsupervised training on large scale raw texts (language modeling) we train fCT so that phrase embeddings -as composed in Section 2 -predict contextual words, an extension of the skip-gram objective (Mikolov et al., 2013b) to phrases.