The TF-IDF approach considers information about the occurrences of tokens in all documents of a text corpus:

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BlackTom AI · Accepted Answer

The question asks about what information the TF-IDF approach uses in relation to a text corpus.
Option 1: True. TF-IDF combines two ideas: term frequency (how often a term appears in a specific document) and inverse document frequency (how common the term is across the entire corpus). The IDF component explicitly relies on how many documents in the corpus contain the term, so the method indeed incorporates information about occurrences across all documents in the corpus.
Option 2: False. If one claimed TF-IDF ignores occurrences in other documents, that would be incorrect because the inverse document frequency term depends on corpus-wide document frequencies. This option misstates the role of IDF and underestimates how TF-IDF uses corpus statistics to downweight common terms.
In summary, TF-IDF explicitly leverages document-level term frequencies and corpus-level document frequencies, tying the importance of a term to its distribution across the whole collection.

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

The TF-IDF approach considers information about the occurrences of tokens in all documents of a text corpus:

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