The meaning behind words (Google)

 We’re on the cusp of deep learning for the masses. You can thank Google later — Tech News and Analysis: "word2vec. Google calls it “an efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words.”"
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"Kaggle’s Howard calls word2vec the “crown jewel” of natural language processing. “It’s the English language compressed down to a list of numbers,” he said.

Word2vec is designed to run on a system as small as a single multicore machine (Google tested its underlying techniques over days across more than 100 cores on its data center servers). Its creators have shown how it can recognize the similarities among words (e.g., the countries in Europe) as well as how they’re related to other words (e.g., countries and capitals). It’s able to decipher analogical relationships (e.g., short is to shortest as big is to biggest), word classes (e.g.,carnivore and cormorant both relate to animals) and “linguistic regularities” (e.g., “vector(‘king’) – vector(‘man’) + vector(‘woman’) is close to vector(‘queen’))."