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:

View Explanation

Log in for full answers

Similar Questions

When analyzing articles, the tf-idf-tf_idf framework is used to:

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

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

In a consumer society, many adults channel creativity into buying things

Economic stress and unpredictable times have resulted in a booming industry for self-help products

People born without creativity never can develop it

A product has a selling price of $20, a contribution margin ratio of 40% and fixed cost of $120,000. To make a profit of $30,000. The number of units that must be sold is: Type the number without $ and a comma. Eg: 20000

Which of the following statement regarding cost is correct:

More Practical Tools for Students Powered by AI Study Helper

Homework AI Solver

Stylized AI Paper Writer

Plagiarism Checker Assistant

Citation AI Academic Writing Tool

In-Class Translation Assistant

AI Note Generator

AI Quiz Answers

Past Exam Questions from University Test Bank

Smart Practice Assistant

Adaptive Practice

Making Your Study Simpler