Tf Idf

TF-IDF (Term Frequency-Inverse Document Frequency) is a widely used technique in information retrieval and natural language processing for quantifying the importance of words in a document relative to a corpus. Current research focuses on integrating TF-IDF with other methods, such as word embeddings and deep learning models (e.g., BERT, transformers), to improve performance in tasks like text classification, sentiment analysis, and information retrieval, particularly for low-resource languages. This combination often yields superior results compared to using TF-IDF alone, demonstrating its continued relevance despite the rise of more complex models. The effectiveness and efficiency of TF-IDF, especially when combined with other techniques, makes it a valuable tool across diverse applications, from document classification to recommendation systems.