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I am having a little friendly debate with my coworker on how to properly/optimally do topic modeling. I am just using the regular traditional nmf/lda approach and he decided to do it using "skip grams" in the sense that he just took every single bigram word combination in every document within N words (he did it within 5) and labels it as a topic, sorts it for those that appear most commonly and filters out non sensical topics.

Can you please tell me if there is any validity to this approach over LDA/NMF?

To me it seems like this is just equivalent to running regexes within 5 words on every possible bigram and not modeling, so there would be no reason to invent LDA/NMF if this "skiagram" approach can possibly to better. But I could be wrong.

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From what I understand, it sounds like your coworker is only considering a bigram as a topic? Wouldn't this result in a number of topics equal to the number of bigrams he/she finds? This is less than ideal in a situation where you would only want a small number of topics.

LDA represents every document in a corpus as a mixture of topics and each word in every document is mapped to a corresponding topic (For a better explanation, see here).

Topics in a topic model are generally represented a list of unigrams. There are many algorithms that can be used that will construct topic phrases based upon unigram lists outputted by topics (Here is one from 2007 for example).

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