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

Question

Anonymous · Accepted Answer

TF-IDF is an approach based off ngrams. TF or term frequency is the count or
ratio of words in a text blob. IDF or inverse document frequency finds the
importance of the word across multiple text blobs. Together, they will be used
to find important words in a text and their intersity depending on how many
times it has been used. TF-IDF is used in multiple applications like text
classification etc. The downside of the approach is that the vector that it
creates is very sparse and as it's an ngram based approach it does not utilize
context information.

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

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