Information Extraction and Retrieval - IIIT Hyderabad

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Problems with Keywords May not retrieve relevant documents that include synonymous terms. “restaurant” vs. “café” “PRC” vs. “China” May retrieve irrelevant documents that include ambiguous terms. “bat” (baseball vs. mammal) “Apple” (company vs. fruit) “bit” (unit of data vs. act of eating) 10
Relevant items are similar Much of IR depends upon idea that similar → relevant to same queries Usually measure query-document similarity Can consider document-document similarity “Similar” can be measured in many ways String matching Same vocabulary Probability arise from same model Same meaning … 11
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: Keyword Search Simplest notion 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 and 26: Vector-based Model • Define: - w
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

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