Information Extraction and Retrieval - IIIT Hyderabad

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Vector Based Model j dj Θ q • Sim(q,dj) = cos(Θ) = [vec(d j ) • vec(q)] / |d j | * |q| • = [Σ w ij * w iq ] / |d j | * |q| • Since w ij > 0 <strong>and</strong> w iq > 0, 0
Vector Based Model • Sim(q,d j ) = [Σ w ij * w iq ] / |d j | * |q| • How to compute the weights w ij <strong>and</strong> w iq ? • A good weight must take into account two effects: – quantification of intra-document contents (similarity) • tf factor, the term frequency within a document – quantification of inter-documents separation (dissimilarity) • idf factor, the inverse document frequency – w ij = tf(i,j) * idf(i) 27
Page 1 and 2: IR Systems (Note: Many slides from
Page 3 and 4: Approaches to IR Two types of
Page 5 and 6: Typical IR Task Given: Find: A
Page 7 and 8: Web Search System Web Spider Docume
Page 9 and 10: Keyword Search Simplest notion of
Page 11 and 12: Relevant items are similar Much of
Page 13 and 14: How effective is IR? Anecdotal e
Page 15 and 16: Intelligent IR Taking into account
Page 17 and 18: IR System Components Text Operati
Page 19 and 20: IR Models Set Theoretic Classic Mod
Page 21 and 22: Classic IR Models - Basic Concepts
Page 23 and 24: Drawbacks of the Boolean Model •
Page 25: Vector-based Model • Define: - w
Page 29 and 30: Probabilistic Model • An initial
Page 31: 31 Thank You - Questions? Vasudeva

Information Extraction and Retrieval - IIIT Hyderabad

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