The term frequency - inverse document frequency (TF-IDF) approach to text vectorization is based on the bag-of-words representation:

Question

BlackTom AI · Accepted Answer

The question asks about whether the TF-IDF approach to text vectorization is based on the bag-of-words representation.
Option 1: True. TF-IDF indeed builds on the bag-of-words idea, where documents are represented as vectors of term counts or frequencies for a vocabulary, and then each term's frequency is weighted by how informative it is across the corpus via inverse document frequency. This means word order is ignored, and features are essentially the presence and frequency of terms within a document.
Option 2: False. This would assert that TF-IDF is not rooted in bag-of-words, implying some other representation is used. In practice, TF-IDF does rely on a bag-of-words-like structure: a fixed vocabulary is created, and documents are represented as sparse vectors indicating term occurrences or frequencies, with weights adjusted by IDF. The idea of preserving word order or syntactic structure is not part of the standard TF-IDF bag-of-words approach, which is what makes TF-IDF compatible with bag-of-words.
In summary, recognizing that TF-IDF operates on a bag-of-words framework helps explain why vector representations treat documents as collections of term tokens rather than sequences with preserved order; the weighting by IDF further refines the representation by downscaling common terms and upscaling rare, informative terms.

SP2025.B69.DAT.562.24 COMPLETE AFTER VIDEO 5: TF-IDF (Module 2 Quiz #4)

The term frequency - inverse document frequency (TF-IDF) approach to text vectorization is based on the bag-of-words representation:

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