TF-IDF (Term Frequency Inverse Document Frequency) wurde zuerst von Luhn (1957) vorgeschlagen und von Spärck (1972) optimiert. Es liefert die Information darüber, wie wichtig ein Wort für ein Dokument in einer Textsammlung ist. Heute gibt es eine Vielzahl unterschiedlicher Varianten und Anwendungen des tf-idf-Maßes. Ein prominentes Beispiel ist der in der Python-Bibliothek „sklearn“ enthaltene Tf-idf-Vectorizer, der viele nützliche Parameter anbietet. Das in unserem Framework implementierte Tf-idf-Maß basiert auf dieser Anwendung.


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