Author(s): Balakrishnakumar V
Originally published on Towards AI.
1. CBOW :
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On a lighter note, the embedding of a particular word (In Higher Dimension) is nothing but a vector representation of that word (In Lower Dimension). Where words with similar meaning Ex. “Joyful” and “Cheerful” and other closely related words like Ex. “Money” and “Bank”, gets closer vector representation when projected in the Lower Dimension.
The transformation from words to vectors is called word embedding
So the underlying concept in creating a mini word embedding boils down to train a simple Auto-Encoder with some text data.
Before we proceed to our creation of mini word embedding, it’s good to brush up our… Read the full blog for free on Medium.
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Published via Towards AI