Introduction to Information Retrieval
Introduction to Information Retrieval Introduction to Information Retrieval
Introduction to Information Retrieval tf-idf example • We often use different weightings for queries and documents. • Notation: ddd.qqq • Example: lnc.ltn • document: logarithmic tf, no df weighting, cosine normalization • query: logarithmic tf, idf, no normalization 50
Introduction to Information Retrieval Summary: Ranked retrieval in the vector space model • Represent the query as a weighted tf-idf vector • Represent each document as a weighted tf-idf vector • Compute the cosine similarity between the query vector and each document vector • Rank documents with respect to the query • Return the top K (e.g., K = 10) to the user 51
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<strong>Introduction</strong> <strong>to</strong> <strong>Information</strong> <strong>Retrieval</strong><br />
tf-idf example<br />
• We often use different weightings for queries and documents.<br />
• Notation: ddd.qqq<br />
• Example: lnc.ltn<br />
• document: logarithmic tf, no df weighting, cosine<br />
normalization<br />
• query: logarithmic tf, idf, no normalization<br />
50
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