Inverse Document Frequency

Inverse Document Frequency (IDF) is a weighting scheme used in information retrieval and text mining to assess the importance of a word within a collection of documents, emphasizing words that are rare across the corpus but frequent within specific documents. Current research focuses on integrating IDF with various machine learning models, such as Naive Bayes, Support Vector Machines, and sentence transformers, to improve performance in tasks like dialect identification, stance detection, and requirement traceability. The effectiveness of IDF, often combined with Term Frequency (TF) as TF-IDF, continues to be explored and refined, particularly in addressing challenges related to vocabulary gaps, domain adaptation, and handling synonyms to enhance the accuracy and efficiency of text analysis applications.