  Skip-gram (word2vec) is a recent method for creating vector representations
of words ("distributed word representations") using a neural network. The
representation gained popularity in various areas of natural language
processing, because it seems to capture syntactic and semantic information
about words without any explicit supervision in this respect. We propose
SubGram, a refinement of the Skip-gram model to consider also the word
structure during the training process, achieving large gains on the Skip-gram
original test set.
