Information Extraction and Retrieval - IIIT Hyderabad

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What could be missing? No effort to underst<strong>and</strong> the text Simple word comparison seems flawed vice president is not the same as president <strong>and</strong> vice Bill as a name is not the same as a duck’s bill George Bush is the same as President Bush A run in a stocking is different from a 5-mile run A pen for writing is not the same as a pig pen Surely IR effectiveness can be improved by addressing some of those problems 14
Intelligent IR Taking into account the meaning of the words used. Taking into account the order of words in the query. Adapting to the user based on direct or indirect feedback. Taking into account the authority of the source. 15
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: How effective is IR? Anecdotal e
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

Information Extraction and Retrieval - IIIT Hyderabad

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