Here’s a fun spreadsheet that implements word2vec. Use it for jumping off point.

It has:

• A single small vocabulary of 9 words
• A single example mary had a little lamb
• We move positive vectors closer together (mary is IN the context window of had); We move negative vectors farther apart (toenail is NOT IN the context window of mary)

In word2vec we maintain two embeddings per vocabulary entry (input and output) vectors.

They mean subtly different things:

• Let’s say two inputs are similar, mary and jane.This says: when mary and jane act as centers, they co-occur with similar in-context words. Maybe poppins?
• Let’s say two outputs are similar: little and lamb . This says: they often appear near the same center words, ie mary

In the end, they’ll be very correlated. But most people take the input vectors after training

The spreadsheet models two word2vec variants

• Softmax: A model to predict probability of an adjacent word directly, given a center word. IE poppins near mary would have a reasonably high probability compared to monkeys near mary. Then backprop to the embeddings so it predicts these probabilities more accurately. Sadly, when you get to actual vocabularies of 100k-1m+, doing this during training becomes infeasible
• Skipgram, negative sampling: Learn on samples: tweak dot products of one positive to the center word (ie poppins closer to mary) - but push farther from out-of-context words, like mary to toenail . This method scales better and is more common

Further reading

• ​Gensim word2vec​
• ​word2vec out of black box​

-Doug

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