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What is the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm used for?
What is the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm used for?
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High TF + Rare word across documents = High TF-IDF score (important keyword).
Updated On: Mar 2, 2026
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Solution and Explanation
Step 1: Meaning of TF-IDF.
TF-IDF (Term Frequency–Inverse Document Frequency) is a statistical algorithm used in Natural Language Processing (NLP) to measure the importance of a word in a document relative to a collection of documents (called a corpus).
Step 2: Understanding Term Frequency (TF).
Term Frequency measures how often a word appears in a document. The more frequently a term appears, the higher its TF value. However, common words like “the” or “is” may appear frequently but are not very meaningful.
Step 3: Understanding Inverse Document Frequency (IDF).
Inverse Document Frequency measures how important a word is across all documents in the corpus. If a word appears in many documents, its IDF value decreases. Rare words that appear in fewer documents get higher IDF values, making them more significant.
Step 4: Purpose of TF-IDF.
The TF-IDF algorithm combines both TF and IDF to assign a weight to each word. It helps identify important and relevant words in a document while reducing the impact of commonly used words. It is widely used in:
• Search engines to rank documents based on relevance
• Text mining and information retrieval
• Document classification and clustering
• Keyword extraction
Conclusion.
TF-IDF is used to evaluate the importance of words in documents, helping systems identify relevant keywords and improve text analysis and search results.
TF-IDF (Term Frequency–Inverse Document Frequency) is a statistical algorithm used in Natural Language Processing (NLP) to measure the importance of a word in a document relative to a collection of documents (called a corpus).
Step 2: Understanding Term Frequency (TF).
Term Frequency measures how often a word appears in a document. The more frequently a term appears, the higher its TF value. However, common words like “the” or “is” may appear frequently but are not very meaningful.
Step 3: Understanding Inverse Document Frequency (IDF).
Inverse Document Frequency measures how important a word is across all documents in the corpus. If a word appears in many documents, its IDF value decreases. Rare words that appear in fewer documents get higher IDF values, making them more significant.
Step 4: Purpose of TF-IDF.
The TF-IDF algorithm combines both TF and IDF to assign a weight to each word. It helps identify important and relevant words in a document while reducing the impact of commonly used words. It is widely used in:
• Search engines to rank documents based on relevance
• Text mining and information retrieval
• Document classification and clustering
• Keyword extraction
Conclusion.
TF-IDF is used to evaluate the importance of words in documents, helping systems identify relevant keywords and improve text analysis and search results.
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