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An object in the real world can be located in 3d space. You can say that one representation of that object is as a point in that space; it is embedded in a 3d embedding space. Of course, those coordinates are not the only way in which the object can be represented, but for a certain problem context, these location coordinates are useful. Given objects A,B,C, or rather, given their coordinates, one can tell which two are closest to each other, or you can find the point D that is the other point of the parallelogram ... this. In fact, it allows you to do similarity tests like "A:B :: C:D". This is through standard vector algebra. Now, imagine each word associated with a 100-dimensional vector. You can do the same thing. Amazingly, one can do things like "man:woman ::king: ...." and get the answer "queen", just by treating each word as a vector, and looking up the inverse mapping for vector to word. It almost feels ... intelligent! This embedding -- each word associated with an n-D vector -- is obtained while training neural nets. In fact, now you have
readymade, pre-trained embedding approaches like Word2Vec. https://www.tensorflow.org/tutorials/text/word2vec |