The bag-of-words approach takes into account information about the occurrences of tokens in all documents of a text corpus:

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Question restatement: The statement claims that the bag-of-words approach takes into account information about the occurrences of tokens in all documents of a text corpus.

Option 1 (True): This would assert that BoW leverages token occurrences across the entire corpus to represent each document. In standard BoW, the representation for a single document is a vector of token counts (or frequencies) for the tokens that appear in that document, not a summary of how often those tokens appear across all documents. While the corpus is used to build the overall vocabulary, the actual feature vector for a document reflects its own intra-document token counts, not a global tally across all documents. Therefore, claiming it uses occurrences across all documents to form each document’s features is incorrect.

Option 2 (False): This aligns with the typical BoW formulation. BoW ignores word order and syntax, and for each document it records how many times each token from the vocabulary appears within that document. The information about how often tokens appear across other documents is not directly encoded in a single document's BoW vector. If one needs to incorporate cross-document frequency information, techniques like document frequency or TF-IDF weighting are used, but plain BoW does not inherently encode global cross-document counts in the document representation.

In summary, the core misunderstanding in the first option is assuming global, corpus-wide token counts influence each document’s BoW vector in the basic representation, which is not how BoW is typically defined. The second option correctly describes the standard BoW behavior, making it the accurate choice for this statement.

SP2025.B69.DAT.562.24 COMPLETE AFTER VIDEO 4: Bag-of-Words (Module 2 Quiz #3)

The bag-of-words approach takes into account information about the occurrences of tokens in all documents of a text corpus:

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