You are right about this, but in this case a vector would represent a collection of words. Getting the k closes entries would show the k closest text entries to

You are right about this, but in this case a vector would represent a collection of words. Getting the k closes entries would show the k closest text entries to a given query. This is super interesting for "semantic" search, where you are looking for meaning as opposed to just a textual match. For example, the text "chocolate milk" is all the same characters as "milk chocolate", but likely have very different usage within the context of retailers or cooks. So, their vectors should be very different. Word2Vec is NLP that uses a neural net to build these vectors: https://en.wikipedia.org/wiki/Word2vec https://en.wikipedia.org/wiki/Word2vec