The main contributions of [Distributed representations of words and phrases and their compositionality](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) by Mikolov et al. are several extension to their previously introduced skip-gram (and CBOW) model for word vector representations learned on large text corpora.
For a given word in a training text corpus, the skip-gram model tries to predict surrounding words. Thus, training the skip-gram model on a text corpus adapts vector representations of words in a way that maximizes the probability for correct surrounding words. This leads to distributed vector representations that capture a large number of precise syntactic and semantic word relationships.
They propose a method to identify idiomatic phrases that are not compositions of individual words (like “Boston Globe”) in order to improve the vocabulary base. Also, an alternative training method to the hierarchical softmax, negative sampling, is introduced and analysed in detail. Further, they show that meaningful linear arithmetic operations can be performed on the trained vector representations, which makes precise analogical reasoning possible.