# Topic Modeling - n-grams or 1,2,3,...n-grams?

Do people use n-grams or 1,2,3,...n-grams in both matrix factorisation and generative models in Topic Modeling?

I've been trying to understand the basics of Topic Modeling and came to know that there are two ways - Matrix Factorisation like LSA and NNMF and generative models like LDA and pLSA.

However, while reading the texts, I had a question - Do people use n-grams or 1,2,3,...n-grams in both matrix factorisation and generative models in Topic Modeling? For example, if n=5, then do people use only 5-grams or do they use all unigrams, bigrams, trigrams, 4-grams and 5-grams for creating the document term matrix?

If there are contextual answers then what are the reasons for using either?

• @rahuladwani In the regular case of unigrams, the probs $p(w|t)$ are comparable across words and naturally sum to 1 (since one can divide by the total number of words for $p(w)$). I don't know how this is handled with a combination of n-grams, since there's no natural universe for the probability space. But apparently from Brian's answer it seems to be common, so I assume that this issue is solved somehow. Commented Dec 5, 2022 at 11:22