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Is an algorithm for constructing vector representations of words, also known as. The vector for each word is a semantic description of how that word is used in context, so two words that are used similarly in text will get similar vector represenations. Once you map words into vector space, you can then use vector math to find words that have similar semantics. Provides a nice Python implementation of Word2Vec.

Building a NLTK FreqDist on Redis. Say you want to build a frequency distribution. Can store large sliceable index lists. The only solution I know that meets those requirements is Redis. Is not persistent , shelve. Is far too slow, BerkeleyDB is not network accessible. Especially given how new it is.