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The skip-gram model of word2vec uses a shallow neural network to learn the word embedding with (input-word, context-word) data. When I read the tutorials for the skip-gram model there was not any mentioning regarding the N-gram. However I came across several online discussions in which people claim --- skip-gram model in word2vec is an expanded version of N-Gram model. Also I don't really understand this "k-skip-n-gram" in the following Wikipedia page.

Wikipedia cited a paper from 1992 for "skip-grams", so I guess this is not the word2vec's skip-gram model, right? Another paper regarding this "skip-grams" is https://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf. This is very confusing. Why there's no one clear this up.

The wikipedia source and the online discussion are as follows:

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Looks like a duplicate but cannot yet comment here.

https://stats.stackexchange.com/questions/364131/is-skip-gram-model-in-word2vec-an-expanded-version-of-n-gram-model-skip-gram-vs

Answered there.

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