On the other hand, the skipn-gram model requires two extra predictions corresponding to d w × V operations each, which is an order of magnitude more expensive even when subsampling V .
the drawback the bagof-words model is that it does not learn embeddings that are prone for learning syntactically oriented tasks, mainly due to lack of sensitivity to word order, since the context is defined by a sum of surrounding words.