This CMU Machine Learning Course is using the Bag-of-words model without too much explanation.

wiki uses the term multiplicity to explain that model.

The bag-of-words model is a simplifying representation used in natural language processing and information retrieval (IR). In this model, a text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar and even word order but keeping multiplicity.

the link to explain multiplicity is in mathematical perspective, could someone please give a concrete example to explain multiplicity of Bag-of-words model in NLP perspective?


1 Answer 1


Multiplicity means keeping track of counts.

A set keeps track of whether something is in it or not.

Multiset additionally contains information of how many times something belongs to it.

In the context of bag of words it means that you count duplicates - this is for example important if you want to use different versions of Naive Bayes algorithm (Bernoulli vs Multinomial Naive Bayes).


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