Which representation treats every word in a document as an independent feature, ignoring word order?

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Multiple Choice

Which representation treats every word in a document as an independent feature, ignoring word order?

Treating every word as an independent feature while discarding word order is the Bag of Words approach. In BoW, you build a fixed vocabulary from the corpus and, for each document, create a vector with one dimension per word. The value in each dimension reflects how often that word appears (or simply its presence). Because the representation does not record where or in what order the words occur, the sequence information is lost, which is the defining characteristic of BoW. Binary Bag of Words is a variant where the vector entries indicate presence (0 or 1) rather than counts, while Count Bag of Words uses actual counts. Term Frequency is a weighting within this framework that scales counts by document length or total terms, but it is not by itself a separate representation.

Treating every word as an independent feature while discarding word order is the Bag of Words approach. In BoW, you build a fixed vocabulary from the corpus and, for each document, create a vector with one dimension per word. The value in each dimension reflects how often that word appears (or simply its presence). Because the representation does not record where or in what order the words occur, the sequence information is lost, which is the defining characteristic of BoW.

Binary Bag of Words is a variant where the vector entries indicate presence (0 or 1) rather than counts, while Count Bag of Words uses actual counts. Term Frequency is a weighting within this framework that scales counts by document length or total terms, but it is not by itself a separate representation.

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