TF-IDF (term frequency inverse document frequency) was first suggested by Luhn (1957) and optimized by Spärck (1972). It weighs how important a word is to a document in a collection of texts. Today, there is a wide range of different variants and applications of the tf-idf measure. One prominent example is the Tf-idf-Vectorizer contained in the Python library “sklearn” that suggests many useful parameters. The Tf-idf measure implemented in our framework is based on this application. 


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