Information Extraction and Retrieval - IIIT Hyderabad

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Vector-based Model • Use of binary weights is too limiting • Non-binary weights provide consideration for partial matches • These term weights are used to compute a degree of similarity between a query and each document • Ranked set of documents provides for better matching 24
Vector-based Model • Define: – w ij > 0 whenever k i ∈ d j – w iq >= 0 associated with the pair (k i ,q) – vec(d j ) = (w 1j , w 2j , ..., w tj ) – vec(q) = (w 1q , w 2q , ..., w tq ) – To each term k i is associated a unitary vector vec(i) – The unitary vectors vec(i) and vec(j) are assumed to be orthonormal (i.e., index terms are assumed to occur independently within the documents) • The t unitary vectors vec(i) form an orthonormal basis for a t-dimensional space • In this space, queries and documents are represented as weighted vectors 25
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: Drawbacks of the Boolean Model •
Page 27 and 28: Vector Based Model • Sim(q,d j )
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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