Could you elaborate on the significance of tf-idf in natural language processing?

Question

Anonymous · Accepted Answer

This is a text vectorization technique which is used to determine the relative
importance of a document / article / news item / movie etc.
TF is simply the frequency of a word in a document.
IDF is the inverse of the document frequency among the whole corpus of
documents.
TF-IDF is used mainly because of two reasons: Suppose we search for “the results
of latest European Socccer games” on Google. It is certain that “the” will occur
more frequently than “soccer games” but the relative importance of soccer games
is higher than the search query point of view.
In such cases, TF-IDF weighting negates the effect of high frequency words in
determining the importance of an item (document).

Could you elaborate on the significance of tf-idf in natural language processing?

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