There are two strategies for managing the huge number of potential contexts a word can appear in. Skip-gram hierarchical softmax (SGHS) uses a binary tree to more efficiently represent the vocabulary, whereas skip-gram negative sampling (SGNS) updates only a limited number of word vectors during each training step. SGNS is preferred in general, yet SGHS showed slight benefits in some reliability scenarios in our prior investigations <cite>(Hellrich and Hahn, 2016a)</cite> .