By vectorizing text using TF-IDF approach we lose some information contained in the raw document:

Question

BlackTom AI · Accepted Answer

Consider what TF-IDF does as a representation: it transforms a document into a vector where each dimension corresponds to a term, weighted by how important that term is in the document relative to the corpus. 
Option True first: it is correct to say that this process loses some information present in the raw text. Specifically, TF-IDF discards word order and syntactic structure, so you no longer know which words appeared together or in what sequence. Nuances like negation (e.g., “not good” vs “good”) can be treated similarly to their positive counterparts, reducing the ability to capture exact meaning. It also ignores multi-word expressions unless you explicitly use bigrams/trigrams, and it abstracts away punctuation and other stylistic cues that might carry meaning in the original document. Moreover, contextual meaning and polysemy (the same word having different senses) are not captured beyond simple frequency-based weighting. All of these aspects mean some information is inevitably lost when moving from raw text to a TF-IDF vector.
Option False first: this statement would be incorrect because, in standard TF-IDF, several kinds of information are indeed lost. If one asserts that no information is lost, you would be ignoring the loss of word order, syntax, and contextual meaning described above. The raw document contains sequence and structure; TF-IDF focuses on term importance and frequency, not on sequential or contextual relations, which is precisely a source of information loss. Therefore, this option mischaracterizes what TF-IDF preserves versus what it discards.
Option Truth value recap: the claim that information is lost in the vectorization is consistent with the known limitations of TF-IDF as a bag-of-words variant that ignores order and context; hence the statement is true. Option False incorrectly asserts no information is lost, which contradicts the fundamental nature of the TF-IDF transformation.

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

By vectorizing text using TF-IDF approach we lose some information contained in the raw document:

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